International Journal of Automation and Control (31 papers in press)

Regular Issues

• A comparison among conventional and unconventional proposed PID control structures  
  by Mohamed Jasim Mohamed  
  Abstract: In this paper, different proportional-integral-derivative (PID) control structures were proposed and derived mathematically from the equation of the conventional PID control structure. Some of these PID control structures are rarely used in the literature, but the rest of them are not mentioned. The purpose of this paper is to explore and examine the usefulness of these unconventional PID control structures by comparing them with the conventional PID control structure. Initially, simple and well-known control objectives are used in comparisons like integral time absolute error (ITAE) and integral time square error (ITSE). So, these different PID control structures are used in four different examples. The particle swarm optimisation (PSO) algorithm is used to find the optimal values of the parameters for each control structure. From these comparisons, the results show that other unconventional PID control structures outperform the conventional PID control structure and the choice of the conventional PID control structures is not always the best.
  Keywords: linear systems; PSO; particle swarm optimisation; PID controller structure; time domain performance index; windup techniques.
  DOI: 10.1504/IJAAC.2025.10063741
   
  
• Design of IMC-PID controller with PSO-based fractional filter for SOPTD processes  
  by Parikshit Kumar Paul, Chanchal Dey, Rajanikanta Mudi, Pubali Mitra Paul 
  Abstract: Over the decades PID controllers are the main choice for industrial applications. Second Order plus Time Delay (SOPTD) models are considered in industry because of better process dynamics than other processes. The internal model control (IMC) technique in a PID form is mostly designed for SOPTD processes. But in this scheme, limited numbers of tuning rules are available to provide satisfactory performances. An IMC-PID controller with cascading fractional filter can offer better control for the SOPTD processes. But, choosing its appropriate fractional order value is a crucial task. So, PSO based fractional IMC filter with a PID controller is proposed. The effectiveness of the proposed controller is performed on several types of SOPTD processes both in set-point tracking and load variations. The robustness and sensitivity functions are compared for the proposed scheme with the other reported control strategy toward achieving the best possible controller structure for SOPTD processes.
  Keywords: IMC; internal model control; fractional filter; PSO; particle swam optimization; second order plus time delay process; PID controller.
  DOI: 10.1504/IJAAC.2025.10063907
   
  
• Multi-objective optimisation for AGV and machine integrated scheduling problem considering battery consumption rate  
  by Bin Wu, Yuchao Ding 
  Abstract: With the advancement of eco-friendly manufacturing and smart production, researchers have increasingly focused on the AGV and machine integrated scheduling problem, considering energy consumption. However, the current research overlooks the varying battery consumption rates of AGV under different operational conditions. This paper addresses this gap by dissecting AGV battery usage into load and no-load scenarios and develops a multi-objective optimization model for the integrated scheduling problem. An improved Non-Dominated Sorting Genetic Algorithm (I-NSGA-II) is presented to solve the model. In the algorithm, a novel two-segment real number encoding approach for machine/AGV assignment and process operations is proposed. The Taguchi analysis was used to discuss the key parameters of the algorithm, and experiments were conducted to perform sensitivity analysis on the model. Simulation results demonstrate that the proposed algorithm outperforms three other widely recognized algorithms in the benchmark.
  Keywords: multi-objective optimisation; scheduling; automatic guide vehicle; flexible job shop; NSGA-II.
  DOI: 10.1504/IJAAC.2025.10064156
   
  
• Modified single phase sliding mode control for a class of mismatched uncertain systems  
  by Viet Anh Duong  
  Abstract: This paper presents a new sliding mode control concept for guaranteeing the invariance of a linear time varying uncertain system to mismatched uncertainties over all time. Two sets of exponential-type switching surfaces are proposed to eliminate the effect of the mismatched uncertainties in the sliding mode. By nature of these surfaces, the reaching phase is eliminated to avoid the non-robustness associated with that phase, at the same time, the system states are always confined to the new sliding mode from the initial time. Moreover, necessary and sufficient invariance conditions are given such that mismatched uncertainties completely vanish from the entire response of the system at the very beginning time. A linear matrix inequalities-based design method for the switching surfaces is also given. In addition, a control law is constructed to maintain the sliding mode. Finally, numerical results are presented to demonstrate the effectiveness of the proposed concept.
  Keywords: invariance condition; SMC; sliding mode control; matched and mismatched uncertainty; LMIs; linear matrix inequalities; invariance property.
  DOI: 10.1504/IJAAC.2025.10064448
   
  
• A multi-threaded parallel iterative greedy algorithm for distributed flowshop group scheduling problems with preventive maintenance  
  by Xiaobin Sun, Hongyan Sang, Wanzhong Wu, Yasheng Zhao, Qiuyang Han 
  Abstract: In actual production, factories not only pursue productivity, but also pay attention to the reliability and stability of the production process. For the continuity of production machines, this paper investigates the distributed flow shop group scheduling problem with preventive maintenance (DFGSP/PM). In order to minimize the makespan, a mathematical model of DFGSP/PM is developed and a multi-threaded parallel iterative greedy algorithm (MPIG) is proposed. A greedy NEH method is designed to generate the initial solution. In order to couple the two subproblems of DFGSP/PM, a two-stage destruction and reconstruction is designed. A multi-threaded parallel local search strategy (MPLS) is introduced to improve the search efficiency of the MPIG, so that the optimal insertion positions of the groups in the sequence can be searched faster and the two subproblems can be coupled effectively. Effectiveness analysis has demonstrated that the proposed MPIG significantly reduces computation time and expands the search space.
  Keywords: distributed flowshop scheduling; preventive maintenance; iterative greedy algorithm; group scheduling; parallel computing.
  DOI: 10.1504/IJAAC.2025.10064456
   
  
• Reduced-order modelling-based FOPID controller design for interval-model Zeta converter using Bode envelope  
  by V.P. Meena, Preeti Meena, V.P. Singh, Ahmad Taher Azar, Saim Ahmed 
  Abstract: This article proposes the design of a fractional-order-proportional-integral-derivative (FOPID) controller for an interval-modeled Zeta converter, employing the Bode envelope method. The approach employs reduced-order modeling, employing direct truncation and Routh-Pad{'e} approximation for numerator and denominator polynomials, respectively. The Zeta converter's mathematical model, derived via state-space averaging, is initially a fourth-order interval model, subsequently reduced to a first-order interval model. The primary objective is to design an FOPID controller meeting specific performance criteria, such as desired gain crossover frequency and phase margin. To achieve this, the teacher-learning-based optimization (TLBO) algorithm minimizes the objective function, determining optimal controller parameters. Step and Bode responses are provided to demonstrate the controller's effectiveness and applicability.
  Keywords: reduced-order modelling; FOPID controller; interval model; Zeta converter; Bode envelope; TLBO; teacher-learning-based optimisation; SSA; state-space averaging.
  DOI: 10.1504/IJAAC.2025.10064457
   
  
• Deep learning optimisation for spatial wind power forecasting: a data driven approach to grid stability enhancement  
  by Nashwa Ahmad Kamal, Mohamed ElSobky, Ahmed M. Ibrahim, Zeeshan Haider 
  Abstract: While wind power has surged as a clean energy source in recent decades, its inherently unstable nature poses a challenge to grid stability. However, forecasting challenges remain, including inconsistent historical data for individual turbines and growing errors in multi-step predictions. This paper presents a novel solution to tackle the intricate problem of spatial dynamic wind power forecasting, leveraging the latest advancements in deep learning-based forecasting models. To achieve the best possible settings for the wind power forecasting model, we prepared the solution after exploring different dimensions including deep learning models, features selection, scaling methods, look-back window size, and optimizers. We selected 6 state-of-the-art forecasting models, 3 scaling methods, 8 optimizers, and a look-back window size ranging from 1 to 14 days. Our findings demonstrate the effectiveness of the proposed framework and establish a foundation for further advancements in wind power forecasting accuracy and grid stability.
  Keywords: wind power forecast; forecast; deep learning; SDWPF; spatially dynamic wind power forecasting; turbine.
  DOI: 10.1504/IJAAC.2025.10064572
   
  
• Improving the diagnosis of partial shading faults by utilising artificial neural networks optimised with the whale optimisation algorithm  
  by Saliha Sebbane, Nabil El Akchioui 
  Abstract: This paper introduces a hybrid approach combining an Artificial Neural Network (ANN) with the Whale Optimization Algorithm (WOA) to diagnose partial shading in photovoltaic (PV) systems. It includes two models: WOA-ANN-Classification, which detects and classifies shading, and WOA-ANN-Localization, which identifies the shading location. The approach was tested against other algorithms like Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and Differential Evolution (DE), using metrics such as mean square error, CPU time, and accuracy. Results showed the WOA-ANN models outperformed others, with the classification model achieving 99.99% accuracy and the localization model 99.96%. These findings highlight the approach's potential to improve fault diagnosis in PV systems, enhancing energy efficiency.
  Keywords: photovoltaic (PV) system; partial shading fault; ANN; artificial neural network; WOA; whale optimisation algorithm; fault detection; classification;localisation.
  DOI: 10.1504/IJAAC.2025.10064965
   
  
• Elevator fault classification based on multi-grained cascade forest with variable importance measure  
  by Yijin Ji, Haoxiang Sun, Xu Zhou 
  Abstract: Current deep learning based elevator fault classification models are mostly elevator-specific due to the lack of a general fault dataset. The performance is also significantly limited if the training data is insufficient or the hyper-parameter is not well tuned. In this work, a new elevator fault dataset is firstly proposed for different elevators based on massive fault recordings from 96,333 elevator emergency disposal service platform and the basic parameters of elevators. Variable importance is then evaluated using random forest with mean decrease accuracy (MDA) for better feature understanding. Using the variables with high importance as input features, a multi-grained cascade forest model is finally proposed for more accurate and faster elevator fault classification. Experiment results validate the superior performance of the proposed model, including an easy training on relatively less training data, a higher classification accuracy than traditional models, and a higher running efficiency than not using variable importance measure.
  Keywords: elevator fault; fault classification; variable importance; importance measure; random forest; mean decrease accuracy; multi-grained cascade forest.
  DOI: 10.1504/IJAAC.2025.10065314
   
  
• Deep learning rotor fault detection using the Gramian angular field and the Markov transition field encoding approaches  
  by Aroui Tarek, Marmouch Sameh 
  Abstract: Using deep learning techniques for diagnostic purposes has garnered considerable interest and demonstrated encouraging outcomes across diverse fields. Convolutional neural networks (CNN) are extensively employed in image-based diagnostic tasks. This paper proposes an approach that transforms the temporal signal of the stator current into visual representations. The combined utilization of the Gramian angular field (GAF) and the Markov transition field (MTF) allows for a comprehensive analysis of stator current data. This combined approach is associated with two deep learning models (VGG19 and RESNET50) for rotor broken bars detection. We have used an experimental database acquired under varied load conditions and with different types of rotor faults to demonstrate the reliability of our approach. The results obtained demonstrate the efficacy of the proposed strategy.
  Keywords: rotor faults; diagnosis; GAF; Gramian Angular Field; Markov Transition Field; VGG19; RESNET50.
  DOI: 10.1504/IJAAC.2025.10065499
   
  
• Intelligent control for accurate fast response and minimum energy of motion for industrial robotic manipulator  
  by Areej Shaar, Jasim A. Ghaeb 
  Abstract: This work proposes an approach to optimise the performance of a six degrees-of-freedom (6-DOF) robotic manipulator. The focus is on achieving a balance between three key objectives: rapid response speed, minimal positioning error, and reduced energy consumption during movement. The methodology employs a two-phase approach. First, a kinematic model is established using the Denavit-Hartenberg convention. Subsequently, a grey wolf optimiser (GWO) identifies optimal joint configurations for diverse target locations within the workspace. These optimal configurations serve as training data for a forward neural network (FNN) model, enabling it to predict optimal joint angles for future tasks. The proposed method demonstrates exceptional capability in precisely positioning the manipulator at desired locations within a short timeframe (0.01 sec average) while maintaining high accuracy (0.0056 mean square error (MSE) average) and achieving significant energy savings (70% average reduction). This approach presents a promising solution for enhancing the overall performance of 6-DOF robotic manipulators.
  Keywords: robot manipulators; optimisation; LQR; linear quadratic regulator; DOF; degrees-of-freedom; energy consumption; neural network.
  DOI: 10.1504/IJAAC.2025.10065596
   
  
• Real-time optimisation study of the step size factor in MFAC based on the barrier function method  
  by Fei Li, Shouli Gao, Li Zeng, Dongya Zhao 
  Abstract: The effectiveness of the model free adaptive control (MFAC) method relies heavily on the selection of the step size factor. The selection of inappropriate value of the step size factor can greatly reduce the effectiveness of the model free adaptive controller or even lead to the instability of the control system. In this paper, a real-time optimization method is proposed for the step size factor in compact form dynamic linearization based MFAC (CFDL-MFAC) scheme. Theoretical analysis reveals that it has higher convergence accuracy than the conventional CFDL-MFAC method, and finally the superiority of the method is further verified by simulation examples.
  Keywords: MFAC; model free adaptive control; step size factor; real-time optimisation; convergence accuracy.
  DOI: 10.1504/IJAAC.2025.10066002
   
  
• Event-triggered synchronisation control of fractional-order time-delay neural networks  
  by Dinh Cong Huong  
  Abstract: The event-triggered synchronisation control problem of neural networks is important in information encryption and key exchange. The objective of this problem is to determine an event-triggered controller such that the known neural networks can track the unknown neural networks. Existing results on the problem of designing event-triggered synchronisation controllers for integer-order neural networks have not been extended to fractional-order neural networks. In this paper, we consider the event-triggered synchronisation control for a class of fractional-order time-delay neural networks with an unknown time-varying delay in the state vector. The controller in this paper uses only information about the state vector when an event-triggered condition is held. This contrasts with previous methods of solving the synchronisation control problem where the information of the state vector was assumed to be always continuously available. Therefore, it is significant in saving communication resources while still maintaining the desired robust control performance.
  Keywords: event-triggered mechanisms (ETMs); fractional-order time-delay neural networks; event-triggered control; linear matrix inequalities (LMIs).
  DOI: 10.1504/IJAAC.2025.10066017
   
  
• Observer-based adaptive neural network robust 𝐻∞ control of quadrotor aerial robot  
  by Zakaria Bellahcene, Abdelmalek Laidani, Mohamed Bouhamida 
  Abstract: This paper presents an approach utilising observer-based adaptive neural networks to achieve robust 𝐻∞ control in an autonomous quadrotor system. The methodology addresses parameter uncertainties and wind disturbances for enhanced control performance. By integrating a state observer design, our method eliminates the need for direct access to state variables. We employ adaptive neural networks, specifically radial basis function neural networks, to approximate unknown functions through state estimates. The proposed methodology ensures 𝐻∞ tracking performance, addressing cumulative uncertainties from unmodelled dynamics, approximation errors, and external disturbances. Leveraging Lyapunov stability theory, we rigorously establish the uniform ultimate boundedness of both the observer-based controller and the closed-loop system. Simulation results are provided to validate and underscore the effectiveness of this approach.
  Keywords: 𝐻∞ control; observer; UAV; neural networks RBF NNs; adaptive tracking.
  DOI: 10.1504/IJAAC.2025.10066218
   
  
• Adaptive sliding mode control law based on super twisting algorithm for trajectory tracking of quadrotors  
  by Biswapratim Roy, Mehul Menon, Aritro Dey, Jayati Dey 
  Abstract: This paper presents a robust control law design for trajectory tracking of quadrotors based on an adaptive super-twisting sliding mode control (A-STA-SMC). The dynamics of the quadrotors gets affected by significant nonlinearities and actuator saturation. Moreover, external disturbances and unknown parameter variation makes the tracking problem more challenging. The authors address this problem and design the proposed A-STA-SMC for satisfactory tracking performance of the quadrotors. The proposed A-STA-SMC is developed with a Proportional -Derivative (PD) structured sliding surface followed by super twisting reaching law. The gain of the super twisting algorithm is tuned adaptively that helps to eliminate chattering and demands lower control effort. The asymptotic stability of the proposed design is theoretically validated in Lyapunov’s sense. Simulation results demonstrates superior trajectory tracking, stability and control effort compared to the conventional Sliding Mode Control (SMC).
  Keywords: adaptation; chattering; control effort; quadrotor; robustness; sliding surface; super twisting; tracking problem.
  DOI: 10.1504/IJAAC.2025.10066500
   
  
• Wavelet based analysis and control of anti lock braking system with hybrid optimisation techniques  
  by Abhas Kanungo, Varun Gupta, Sourav Diwania, Sachin Sharma, Neeraj Kumar Gupta 
  Abstract: The integration of wavelet-based analysis and hybrid optimisation techniques in the control of anti-lock braking systems (ABS) presents a significant advancement in automotive safety and performance. Wavelet transforms, with their capability for multiresolution analysis and noise reduction, offer enhanced signal processing, enabling precise detection of wheel dynamics and road conditions. This leads to improved fault detection, adaptive control, and efficient brake pressure modulation, ensuring optimal braking performance under diverse and nonlinear conditions. Hybrid optimisation techniques, such as genetic algorithms and particle swarm optimisation, complement wavelet-based methods by optimising ABS performance across multiple objectives, such as safety, comfort, and efficiency. The synergy of these approaches results in a robust and responsive ABS that adapts in real-time to varying driving scenarios, enhancing vehicle stability, reducing stopping distances, and providing superior overall safety. This research underscores the potential of wavelet-based analysis and hybrid optimisation to revolutionise ABS technology, offering significant benefits in both conventional and autonomous vehicles.
  Keywords: ABS; anti-lock braking systems; wavelet transform; conventional and autonomous vehicles; hybrid optimisation techniques.
  DOI: 10.1504/IJAAC.2025.10066518
   
  
• Approximation of interconnected power system models using enhanced Schur method for balanced truncation and application to controller design  
  by Bala Bhaskar Duddeti, Veerpratap Meena 
  Abstract: This paper presents a novel method for reducing the complexity of high-order LTI MIMO systems. Utilising the Schur technique for balanced truncation (SMBT), the approach avoids the need for balancing transformation in obtaining the denominator coefficients of the reduced model. A straightforward mathematical procedure is employed to construct the numerator coefficients, enhancing steady-state approximation and eliminating the need for Routh tables, time moments, and Markov parameters. The reduced model is used to design a controller for the original plant using model-matching concepts in the Pade sense. The performance of the simplified model and controllers is evaluated through numerical examples, with improvements in time response characteristics, ISE, and RMSE over the existing SMBT method.
  Keywords: interconnected power system models; multi-input multi-output systems; higher order systems; order reduction; Markov parameters; time moments; Schur decomposition; controller design.
  DOI: 10.1504/IJAAC.2025.10066712
   
  
• Combined input-output linearisation and internal model control for parabolic trough solar plant  
  by Ahcene Triki, Ahmed Maidi, Jean-Pierre Corriou 
  Abstract: The parabolic trough solar plant (PTSP) is a widely used technology for alternative energy sources. The dynamic behaviour of this system is described by hyperbolic partial differential equations (PDEs), which makes control design a challenging problem. The use of a reduced or an approximate model leads to limited performance. Thus, to enhance the efficiency of the PTSP, sophisticated control strategies that rely on the PDE model are needed. In this paper, a control strategy that combines input-output linearisation and internal model control is proposed. Input-output linearisation is adopted to design a state feedback that yields a finite-dimensional closed loop system. To ensure robustness against disturbances and physical parameter uncertainties (such as specific heat capacity and density), an internal model controller is used to generate the external input for the linearising state feedback. Assuming actual daily profiles of solar radiation, the performance of the control scheme is evaluated through numerical simulations. The tracking capabilities of the proposed control scheme are demonstrated via simulation.
  Keywords: PTSP; parabolic trough solar plant; bilinear distributed parameter system; input-output linearisation; characteristic index; internal model control.
  DOI: 10.1504/IJAAC.2025.10066855
   
  
• Integration of machine learning techniques and various empirical models for showing the impact of tilt angle optimisation  
  by Kumari Namrata 
  Abstract: In present scenario the solar energy acts the best alternative for conventional energy. However, a hostile photovoltaic (PV) panel environment might result in inaccurate meta-data, which may arise challenges like processing complexity, significant biases, and data quality. Tilt angle and orientation of panels is crucial input to know PV efficacy. To gather metadata for distributed PV systems is challenging, resulting high complexity in control and inspections. This research focuses on to develop unique technique for estimating PV system tilt angles, for which three anisotropic and three isotropic empirical models for prediction of optimum tilt alignment of the PV module as per variation of time and season. Further machine learning models, i.e., Random Forest regression (RF), multilayer perceptron (MLP), and K-nearest neighbour (KNN) are used to validate the empirical analysis and it has been observed that RF has shown best result with R2 = 0.9644, root mean squared error (RMSE) 385.79.
  Keywords: solar irradiance; tilt angle; machine learning model; anisotropic; isotropic.
  DOI: 10.1504/IJAAC.2025.10067917
   
  
• Deep reinforcement learning LQR controller design for MIMO systems applied to gas production facility  
  by Kamel Ben Slimane, Zied Tmar, Mongi Besbes 
  Abstract: This paper addresses performance control in the synthesis of a stabilising controller for a gas production facility. The controller’s performance is closely linked to the pole values defined during synthesis. However, it is highlighted that these calculated pole values may not always be applicable due to the system’s physical constraints, such as the impossibility of reducing a biological chemical reaction from hours to microseconds. Initially, the controller is synthesised using an LQR controller with state estimators generated by a specific observer. An in-depth discussion of pole values is provided, referencing Hadamard’s lemma, Gerschgorin discs, and the Nyquist stability criterion. To enhance stabilisation performance, Deep Reinforcement Learning is employed to modify the poles by adjusting LQR values in a learning environment. The results demonstrate a successful integration of Gerschgorin discs early in the synthesis process, followed by Deep Reinforcement Learning improvements, generating physically feasible pole values that significantly enhance controller performance.
  Keywords: stabilising controller; controller synthesis; pole placement; LQR control; linear quadratic regulator; Gerschgorin discs; deep reinforcement learning; MIMO systems; multiple inputs and multiple outputs.
  DOI: 10.1504/IJAAC.2025.10067319
   
  
• Enhancing sensor node autonomy: CMOS energy harvesting from controller area network data lines  
  by Hatim Ameziane, Mourad Yessef, Ali Amkor 
  Abstract: This paper presents a novel approach to empower sensor nodes autonomously by harnessing energy from Controller Area Network (CAN) data lines using CMOS technology. The proposed energy harvesting system efficiently captures and stores energy from the CAN network, eliminating the need for frequent battery replacements and offering a sustainable and cost-effective solution for IoT applications. Through comprehensive simulations and experiments, the system's performance showcases remarkable metrics, including a rapid stabilization time of 520ns, ultra-low ground current of 10 µA, and an impressive energy efficiency of 89.1%. This innovative system not only demonstrates the effective extraction and regulation of energy from CAN data lines but also paves the way for sustainable and economical energy harvesting solutions in the realm of IoT applications, promising enhanced operational longevity for sensor nodes in diverse environments.
  Keywords: EHS; energy harvesting system; CAN; controller area network; WSN; wireless sensor nodes; duty cycle; CAN-ARINC825; SoC; system on chip; PM ICs; power management ICs.
  DOI: 10.1504/IJAAC.2025.10067354
   
  
• Automated defect detection in solar module electroluminescence images using YOLOv9 variants  
  by Yasmin Adel Hagag, Mohamed ElSobky, Ahmed M. Ibrahim, Ahmad Taher Azar, Zeeshan Haider 
  Abstract: Defective photovoltaic (PV) panels cause a reduction in energy generation. To maximize green energy production, close monitoring and improvement of the photovoltaic health index are essential. Current methods rely on visual inspection of electroluminescence (EL) images by experts which is time-consuming and requires highly trained personnel. This work presents an automated defect detection approach on PV panels using the state-of-the-art object detection model YOLOv9. Four variants of YOLOv9 (gelan-e, gelan-c, yolov9-c, and yolov9-e) are trained on three datasets (ELDDS, ELDDS1400C5, and PVEL-AD). Two similar datasets (ELDDS, ELDDS1400C5) are merged to make a larger dataset. The proposed YOLOv9-e model on merged data achieved the mAP@0.5 of 0.815 outperforming other approaches by 3.8$\%$. Notably, on the ELDDS1400c5 dataset commonly used for comparison, the Proposed YOLOv9-e variant achieves a competitive mAP@0.5 of 0.766 without architectural modifications compared to 0.777 for YOLOv5s.
  Keywords: photovoltaic defects; defect detection; solar panel defects; Deep Learning; object detection; solar energy.
  DOI: 10.1504/IJAAC.2025.10067355
   
  
• Sensor fault diagnostics and resilient control in PMSM drives for EVs using LADRC  
  by Sankhadip Saha, Urmila Kar 
  Abstract: The paper introduces a novel comprehensive strategy for detecting and categorising sensor faults and proposes a fault-resilient control (FRC) scheme based on linear active disturbance rejection control (LADRC) specifically designed for permanent magnet synchronous motor (PMSM) drives for electric vehicles (EVs). The approach includes a rapid yet effective method for identifying speed sensor faults without relying on estimated speed values, enhancing the autonomy of sensor diagnostic phases, and employing a discrete sliding mode observer (D-SMO) for speed sensorless fault-resilient control. Additionally, the paper suggests a hybrid method for localising and categorising current sensor faults, merging model-based and signal-based techniques. The proposed second-order LADRC technique is employed for speed regulation in field-oriented control (FOC), demonstrating its effectiveness in estimating and counteracting external disturbances while addressing current sensor fault detection challenges in EV applications. Simulation and experimental tests validate the efficacy of the proposed sensor diagnostic method for fault-resilient control of PMSM drives.
  Keywords: fault detection; fault categorisation; FRC; fault-resilient control; LADRC; linear active disturbance rejection control; D-SMO; discrete sliding mode observer; PMSM; permanent magnet synchronous motor; EV; electric vehicle.
  DOI: 10.1504/IJAAC.2025.10067563
   
  
• Fractional-order virtual inertia control based on a hybrid energy storage system for frequency control in an island microgrid  
  by Notchum Deffo Boris Arnaud , Bakouri Anass  
  Abstract: High penetration of renewable sources through microgrids leads to several instabilities, including frequency instability due to a lack of inertia and variability of solar and wind sources, as well as loads. In this paper, a control strategy based on virtual inertia control, also known as synthetic inertia control, is applied in an isolated microgrid (IMG) to ensure frequency stability. By analogy with conventional systems, the proposed virtual inertia controller is based on fractional order (FOVIC) and applied to the hybrid energy storage system. The parameters of these controllers are optimised by a novel metaheuristic algorithm known as the augmented grey wolf algorithm (AGWO), which is an improvement of grey wolf optimisation. The use of these allows better performance in terms of overshoot, and undershoot for frequency deviation compared to the entire virtual inertia control, the system without inertia controller, and another optimisation algorithm.
  Keywords: load frequency control; fractional order calculus; virtual inertia control; augmented grey wolf optimisation; hybrid energy storage system.
  DOI: 10.1504/IJAAC.2026.10067626
   
  
• Optimised fractional order PID controller for a 3 DOF Hover quadrotor: experimental validation and robustness analysis  
  by Djamel Dhahbane, Aimen Abdelhak Messaoui, Abdellali Kourtel, Ayoub Ferah 
  Abstract: This paper aims to implement a model-free linear control law based on fractional proportional integral derivative (FPID) regulator. The control approach is applied to a quadrotor system to stabilise its attitude through the three rotation axes (roll, pitch, and yaw), where the control parameters are optimised via the particle swarm optimisation (PSO)algorithm. The proposed controller is tested in simulation, experimentally validated on the3DOF Hover platform, and compared with two other methods: the linear quadratic regulator (LQR), the classical proportional integral derivative (PID) controller and the Backstepping method. Furthermore, robustness analysis is carried out to evaluate the performances of the investigated controller against internal and external disturbances. The obtained results have demonstrated the efficiency and usefulness of the investigated controller in terms of precision (less than 0.2 degrees in attitude error) and robustness in the presence of perturbed environments.
  Keywords: quadrotor; 3DOF Hover system; attitude control; LQR; linear quadratic regulator; backstepping; fractional PID; PSO algorithm; robustness analysis.
  DOI: 10.1504/IJAAC.2026.10067765
   
  
• Dissipativity analysis for nonlinear delayed differential systems with three kinds of time-varying delays  
  by Yushan Chang, Xiaona Yang, Xian Zhang 
  Abstract: This work investigates the problems of global exponential stability analysis and dissipativity analysis for a class of nonlinear delayed differential systems concurrently affected by time-varying distributed, leakage and transmission delays. Sufficient conditions to guarantee that the considered nonlinear delayed differential systems are globally exponentially stable with dissipative level are presented by proposing a direct method based on system solutions. The conditions given in this paper consist of only several simple inequalities which are easily realised by applying the software tool YALMIP. The method proposed in this paper is quite simple and avoids the construction of Lyapunov-Krasovskii functionals, thus significantly reducing the computational effort. Finally, two simulation examples illustrate the applicability of the theoretical results.
  Keywords: NDDSs; nonlinear delayed differential systems; dissipativity; global exponential stability; time-varying distributed delays; time-varying transmission delays; time-varying leakage delays.
  DOI: 10.1504/IJAAC.2026.10067915
   
  
• Error-based ADRC approach of lower knee exoskeleton system for rehabilitation  
  by Nasir Ahmed Alawad, Amjad Jaleel Humaidi, Ahmed Sabah Al-Araji 
  Abstract: In this study, active disturbance rejection control (ADRC) has been designed to control the exoskeleton system for rehabilitation at knee level and to replace the exercises made by physicians with systematic training devices. The time derivative of reference input and feed-back signals is an evitable in most ADRC schemes. To alleviate the burden due to derivative actions, the idea of proposing error-based ADRC (EADRC) has been introduced. In a conventional ADRC scheme, the extended state observer (ESO) is the core element of the controller to estimate both the states of the system and the exerted disturbance. The EADRC utilises the estimates in the error sense rather than the actual states. The EADRC technique is compared to traditional ADRC and the numerical results showed that the proposed EADRC outperforms the conventional ADRC in terms of tracking errors, noise and load rejection capabilities for the system subjected to noise and load uncertainties.
  Keywords: exoskeleton system; ADRC; active disturbance rejection control; robustness; stability; knee rehabilitation.
  DOI: 10.1504/IJAAC.2025.10061844
   
  
• Contamination detection in the cultivation of leukocyte based on image sparsity evaluation  
  by Lianghong Wu, Zhiyang Li, Liang Chen, Cili Zuo, Hongqiang Zhang 
  Abstract: The contamination in the cultivation of cells seriously affects the reliability and reproducibility of experimental results. Currently, the detection of contamination in cells relies heavily on manual observation, which is labour-intensive and time-consuming. In this paper, it proposes a sparse matrix clustering (SMC) method based on the principle of matrix sparsity to automatically detect the contamination in leukocytes. Firstly, the image segmentation and local adaptive binarisation techniques are used to eliminate the noise points and shadows. Then, a scoring map of image sparsity based on the pixel distribution of segmented images is proposed to index the pollution degree of the leukocyte. By dynamically determining the threshold for evaluating image sparsity based on the maximum distributed pixels on the scoring map, the image sparsity is used as a feature for contamination classification. Experimental results show that this method achieves an accuracy of 98.8% for detecting contamination in leukocyte culture images with fast detection speed, which can be used as an efficient cell contamination detection approach in the biomedical field.
  Keywords: image sparsity evaluation; image segmentation; local adaptive binarisation; leukocyte contamination; sparse matrix clustering; contamination detection; image feature extraction; image classification.
  DOI: 10.1504/IJAAC.2025.10062262
   
  
• Research on steelmaking-continuous casting cast batch planning based on an improved surrogate absolute-value Lagrangian relaxation framework  
  by Congxin Li, Liangliang Sun 
  Abstract: Cast batch planning (CBP) is the bottleneck of batch planning in the steelmaking-continuous casting-hot rolling (SM-CC-HR) section. With the rapid development of the market-oriented demand of steel enterprises to multiple species, small batches, and on-time delivery, the batch planning integrated production process has dramatically increased the flexibility of the CBP as well as the functional requirements of the time dynamic balance. Therefore, it is of great significance to research the method of CBP to improve production efficiency and reduce material and energy consumption. In this paper, based on the improved surrogate absolute-value Lagrangian relaxation (ISAVLR) framework, the heuristic method based on a multiplier iteration strategy with controllable gradient direction combined with a local search (LS) algorithm is proposed. The 'zigzagging' problem in the traditional Lagrangian relaxation (LR) is overcome and the solution efficiency is improved while the original problem is provided with tighter lower bounds. Finally, simulation experiments based on real production data verify the effectiveness of the proposed method.
  Keywords: steelmaking-continuous casting; ISAVLR; improved surrogate absolute-value Lagrangian relaxation; CBP; cast batch planning; heuristic.
  DOI: 10.1504/IJAAC.2025.10062754
   
  
• Model predictive control with constraints based on PSO and fuzzy logic applied to the control of coupled longitudinal-lateral dynamics of the autonomous vehicle  
  by Rachid Alika, El Mehdi Mellouli, El Houssaine Tissir 
  Abstract: In this paper, a strategy for controlling the longitudinal and lateral dynamics of an autonomous vehicle is developed. This strategy is based on the model predictive control (MPC) with constraints combined with the LPV form. The three degrees of freedom (3DOF) model of the autonomous vehicle is used. The cornering stiffness is approximated by a fuzzy logic type, Takagi-Sugeno, with the aim of finally approximating the nonlinear lateral forces. In order to improve the systems performance, constraints for controller inputs and also for system outputs are defined. The MPC weights are determined using the particle swarm optimisation (PSO). The objective of this strategy is to follow the reference trajectory of the autonomous vehicle while reducing the lateral and longitudinal displacement error. The steering angle and the longitudinal acceleration are the control inputs, the outputs of this system are the longitudinal velocity, the yaw angle, the longitudinal and lateral displacement. The system is multi-input and multi-output (MIMO) and has non-linear dynamics. Simulation results show some improvements over the literature.
  Keywords: autonomous vehicles; MPC; model predictive control; MPC constraints; LPV system; PSO; particle swarm optimisation; MIMO system; nonlinear dynamic; path planning; fuzzy logic.
  DOI: 10.1504/IJAAC.2025.10062896
   
  
• FPGA-based performance evaluation of backstepping control and computed torque control for industrial robots  
  by Arezki Fekik, Hocine Khati, Ahmad Taher Azar, Mohamed Lamine Hamida, Hakim Denoun, Nashwa Ahmad Kamal 
  Abstract: In this paper, a comparative study is conducted on two nonlinear control techniques: state feedback control through backstepping and computed torque control. The study focuses on their application to the industrial robot PUMA 560. The primary goal is to assess the trajectory tracking accuracy and speed achieved by these methods. To achieve this objective, both control techniques are employed on the Zed board Zynq FPGA platform, encompassing both simulation and hardware systems. Subsequently, the experimental results are thoroughly analysed and compared, aiming to accentuate the unique advantages and constraints associated with each method.
  Keywords: field-programmable gate array; FPGA; backstepping control; computed torque control; CTC; Zed board Zynq; PUMA 560.
  DOI: 10.1504/IJAAC.2025.10062960