International Journal of Automation and Control (30 papers in press)

Regular Issues

• 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
   
  
• 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
   
  
• 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
   
  
• A hybrid graph attention mechanism for load forecasting based on efficiently spatiotemporal feature extraction  
  by Jie Chen, Yajing Tang, Xiao Liu, Ling Wang, Mao Tan, Haodong Zhang 
  Abstract: Load forecasting is fundamental to optimising and dispatching power systems. Despite the efficiency of existing load forecasting methods, they fall short in extracting more comprehensive feature representations. To solve this problem, this paper presents a spatiotemporal load forecasting method that integrates key factors, including historical load patterns, footfall, and meteorological conditions. Our method leverages residual graph convolutional networks (ResGCN) and long short-term memory (LSTM) as the primary models for forecasting. Firstly, we pinpoint the most significant variables by correlation analysis. Subsequently, we extract spatiotemporal features from load graphs and input these features into our forecasting model. The model integrates a local-global graph attention (LGGA) mechanism to incorporate local and global information, enhancing the understanding of load data. Additionally, we employ a convolutional block attention module (CBAM) to fine-tune the feature representations, thereby improving model sensitivity. Experimental results demonstrate the superiority of our method.
  Keywords: load forecasting; spatiotemporal feature; graph attention mechanism; graph convolutional network; LGGA; local-global graph attention; CBAM; convolutional block attention module; ResGCN; residual graph convolutional network.
  DOI: 10.1504/IJAAC.2026.10068854
   
  
• Controlling and stabilising of non-holonomic wheeled mobile robot equipped with manipulator (WMRM)  
  by Jun Xiao  
  Abstract: Mobile robots have a significant contribution to many fields from research, and industry to healthcare and beyond. Wheeled mobile robots (WMR) equipped with manipulators provide a flexible and attractive solution to grasp the targeted object from an unapproachable area where human access is not easy/possible. This research aims to control and stabilize the non-holonomic wheeled mobile robot equipped with a manipulator (WMRM). In this study, the model references adaptive control (MRAC) in conjunction with regulation, pole-placement, and tracking (RST) control scheme is designed to control the non-linear behavior of the vehicle. The model used in this research has five degrees of freedom (DOF). Three DOFs for the vehicle and the manipulator has two DOFs. The designed control scheme stabilizes the disturbances caused by the manipulator due to its continuous movement. Simulation results are compared with the RST controller and the designed scheme ensures higher efficiency, accuracy, robustness, and convergence to stability.
  Keywords: adaptive RST controller; WMR; wheeled mobile robot; manipulation; degree of freedom; robotic arm based vehicle; robotic vehicle; control algorithm; non-holonomic vehicle.
  DOI: 10.1504/IJAAC.2026.10068883
   
  
• Research on multi-target tracking of moving observation stations based on IL-PSO algorithm  
  by Xinbiao Lu, Fang Li, Chenyang Hang 
  Abstract: Regarding how the positional arrangement of mobile sensors affects multi-target tracking accuracy, the interactive learning particle swarm optimisation (IL-PSO) algorithm is introduced. It uses observation stations’ motion models to predict multi-target states. First, targets are allocated for effective tracking. Then, joint probabilistic data association (JPDA), strong tracking filter (STF), and genetic algorithm particle filter (GAPF) algorithms are integrated to improve tracking accuracy. A comparative analysis based on IL-PSO shows its superior performance in metrics like relative point position error. This is due to its optimisation of station positions and the integration of algorithms enhancing precision and robustness.
  Keywords: GAPF; genetic algorithm particle filter; JPDA; joint probabilistic data association; multi-target tracking; PSO; particle swarm optimisation; STF; strong tracking filter.
  DOI: 10.1504/IJAAC.2026.10069803
   
  
• Design of a nonlinear PID controller for a twin rotor aerodynamic system  
  by Marwa Rasheed Ali, Omer Waleed Abdulwahhab 
  Abstract: This paper presents the implementation of an optimal nonlinear PID controller (N-PID) applied to a simplified and lab-scaled version of a real helicopter called a twin-rotor aerodynamic system. The controller is proposed to precisely track the desired trajectories for both the main and tail rotors. This controller is developed by adjusting the integral part of the conventional PID controller to become nonlinear by using nonlinear function. The N-PID is compared with the optimal linear PID controller (L-PID). The simulation results show that the N-PID controller outperforms the optimal L-PID controller. This is clear from the conclusion that the N-PID controller’s having lower performance indices than the L-PID controller’s, suggesting superior overall error minimisation. Some performance indicators are improved by the N-PID. Another comparison between the N-PID controller and the sliding mode controller (SMC) is carried out. The simulation results show that the N-PID controller outperforms the SMC in some performance indicators.
  Keywords: N-PID; nonlinear PID controller; optimal PID controller; twin rotor aerodynamic system; frequency response specifications.
  DOI: 10.1504/IJAAC.2026.10069866
   
  
• AMIGO and WC tuning based PID controller design for FESS integrated islanded microgrid  
  by R.S. Meena, Vinay Pratap Singh, A.V. Waghmare, Veerpratap Meena, Akhilesh Mathur 
  Abstract: To mitigate frequency deviations occurring in isolated microgrid, this study evaluates PID controller, designed using approximate m-constrained integral gain optimisation (AMIGO) and Wang Cluett (WC) tuning rules. Microgrid model considered for analysis includes both uncontrollable and controllable distributed generation units (DGUs) along with flywheel energy storage (FESS) as energy storing system. Additionally, the first order transfer function of FESS and DGUs, altogether produces a linearized system transfer function. Further for simplifying the analysis of mircrogrid, transfer function is approximated in first order plus time delay (FOPTD) form. The FOPDT model provides system gain, time constant, and delay time. These obtained values are utilized further to derive controller parameters. Tuning rules are implemented to finely tune controller parameters. Frequency analysis and transient response analysis are conducted for AMIGO-based and WC-based PID controllers. The controllers’ efficiency and applicability are further clarified by conducting a comparative analysis between AMIGO-based and WC-based PID controllers.
  Keywords: islanded microgrid; tuning; AMIGO method; PID controller; FOPDT model; FESS; flywheel energy storage.
  DOI: 10.1504/IJAAC.2026.10069867
   
  
• Perturbation estimation-based multivariable control of polymerisation reactor with input constraints  
  by Zahra Ahangari Sisi, Mehdi Mirzaei, Maryam Farbodi, Sadra Rafatnia 
  Abstract: The polymerisation reaction in a continuous stirred tank reactor (CSTR) is controlled as a multi-input multi-output (MIMO) nonlinear process. This study proposes a novel optimisation-based approach for estimating the perturbations of polymerisation reactor model. The proposed strategy compensates for disturbances and time-varying uncertainties by appending complementary terms to the model. Accordingly, a continuous predictive controller is designed based on the constructed model considering the limitations of control inputs. In the results, at first, by evaluating the open-loop performance, the effect of noise in the estimated model is attenuated by weighting the complementary terms. Subsequently, the better performance of the estimator based controller is demonstrated in comparing with the other methods. For example, for the same range of the control inputs, the proposed method shows respectively 5.6% and 40% reduction in the root means square (RMS) of tracking errors for the monomer concentration and the temperature with much less simulation time compared to the conventional predictive controller.
  Keywords: polymerisation reactor; model updating; multivariable control; constrained stability; perturbation estimation; stochastic stability.
  DOI: 10.1504/IJAAC.2026.10069946
   
  
• Design and investigations of MIT, fractional-order MIT and modified MIT rule-based model reference adaptive control for noninteracting and interacting two-tank coupled systems  
  by Dhananjay Gupta, Awadhesh Kumar, Vinod Kumar Giri 
  Abstract: The chemical process industries make extensive use of coupled tank systems (CTS) in their industrial applications. In this paper, a normal Massachusetts Institute of Technology (MIT) rule, fractional-order MIT (FOMIT) rule and modified MIT rule-based model reference adaptive controller (MRAC) have been designed to stabilise CTS. After the implementation of all three mentioned methods, it has been found that a stable closed loop system cannot be guaranteed by traditional MIT rule. To overcome this problem the FOMIT and PID enhanced MIT rule-based MRAC has been designed. The FOMIT rule-based MRAC method necessitates a few lower values of adaptation gains to achieve the desired response, while modified MIT rule-based MRAC shows the desired response at a very wide range of adaptation gain values. The research contributes to the understanding of the adaptability and robustness of control systems in the context of two-tank coupled systems.
  Keywords: CTS; coupled tank systems; adaptive control; adaptation gain; MIT rule; fractional-order MIT rule; modified MIT rule; MRAC.
  DOI: 10.1504/IJAAC.2026.10070039
   
  
• Observer-based nonlinear cascade control approach of rewinding systems with uncertainties and disturbances compensation  
  by Van Trong Dang, Thi Dieu Trinh Tran , Dinh Bao Hung Nguyen , Tung Lam Nguyen 
  Abstract: In this paper, the goal is to design a controller based on the backstepping technique and a nonlinear disturbance observer to control the tension and angular velocity of multiple-span rewinding systems under disturbance and uncertainty. The backstepping control technique is considered a control method with great potential for underactuated nonlinear objects with multi-input multi-output. However, there is currently no secure stability in operation when the system is ominously affected by disturbances and uncertainties. Therefore, a non-linear disturbance observer is integrated with the method to address that problem. This observer is designed with auxiliary state variables to reduce the measurement disturbance. The convergence of the proposed observer is taken into consideration and the stability of the whole system is demonstrated by choosing a proper Lyapunov function. The effectiveness of the proposed control schematic is tested through comparative simulations with other typical nonlinear control algorithms on a two-span web transport system.
  Keywords: multiple spans rewinding systems; back stepping technique; HGDOB; high-gain disturbance observer; cascade control structure; lumped uncertainties; disturbances.
  DOI: 10.1504/IJAAC.2026.10070127
   
  
• Learning manta ray foraging optimisation based on external force for parameters identification of photovoltaic cell and module  
  by Lin Zhang, Zuwei Huang, Donglin Zhu 
  Abstract: Photovoltaic systems, as a key area of research in the energy industry, face challenges from harsh environmental conditions that impact both power generation efficiency and service life. Therefore, constructing an accurate current-voltage model for solar cells is a complex issue. To address this, this paper proposes a Learning Manta Ray Algorithm based on External Force (EMRFO). The algorithm introduces two ways of random and opposition-based Learning in the initialisation process to construct the initial population. Additionally, a self-adaptive flip factor is introduced to optimise performance across varying environments. Lastly, a gravity centre learning mechanism based on external force is proposed, which utilises both internal and external population information to enhance development and exploration capabilities. Experimental results demonstrate that EMRFO exhibits strong optimisation performance. In solar cell parameter identification, the parameters obtained using EMRFO improve model accuracy.
  Keywords: solar cell; manta ray foraging optimisation; local opposition-based learning; somersault factor; centre of gravity learning; benchmark function; parameter identification.
  DOI: 10.1504/IJAAC.2026.10070128
   
  
• 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 minimise the makespan, a mathematical model of DFGSP/PM is developed and a multi-threaded parallel iterative greedy (MPIG) algorithm is proposed. A greedy NEH (GNEH) 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 paper proposes the design of a fractional-order proportional-integral-derivative (FOPID) controller for an interval-modelled Zeta converter, employing the Bode envelope method. The approach employs reduced-order modelling, employing dirct truncation and Routh-Padé approximation for numerator and denominator polynomials, respectively. The Zeta converter's mathematical model, derived via state-space averaging (SSA), is initially a fourth-order interval model, subsequently reduced to a first-order interval model. The primary objective is to design an FOPID controller that meets specific performance criteria, such as the desired gain crossover frequency and phase margin. To achieve this, the teacher-learning-based optimisation (TLBO) algorithm minimises 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
   
  
• 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 optimisation algorithm (WOA) to diagnose partial shading in photovoltaic (PV) systems. It features two WOA-ANN models: WOA-ANN-classification for detecting and classifying PV array states as normal or partially shaded, and WOA-ANN-localisation for pinpointing the shading location. The WOA was compared with other algorithms like grey wolf optimisation (GWO), particle swarm optimisation (PSO), and differential evolution (DE). The ANN was trained using metrics such as mean square error, CPU time, and training accuracy. Experimental results showed the WOA-ANN models outperformed others, with the classification model achieving 99.99% accuracy and the location model 99.96% accuracy. This hybrid methodology significantly enhances fault diagnosis accuracy in PV systems, supporting sustainable 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 utilisation 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 optimisation method is proposed for the step size factor in compact form dynamic linearisation 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
   
  
• 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; unmanned aerial vehicle; neural networks RBF NNs; adaptive tracking.
  DOI: 10.1504/IJAAC.2025.10066218
   
  
• 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
   
  
• 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: ETMs; event-triggered mechanisms; fractional-order time-delay neural networks; event-triggered control; LMIs; linear matrix inequalities.
  DOI: 10.1504/IJAAC.2025.10066017
   
  
• 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 maximise 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 onPVpanels 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 PVELAD). 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 ProposedYOLOv9-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