This paper presents a comprehensive bi-level convex optimization framework for eco-driving of fuel cell hybrid electric vehicles (FCHVs) at signalized intersections. The proposed approach analyses the complex co-optimization problem into upper-level speed planning using quadratic programming (QP) and lower-level energy management using Model Predictive Control (MPC) being adapted as QP. The upper level ameliorates vehicle’s path taking into consideration traffic light constraints, while the lower level works on fuel cell and battery power distribution through a predictive control strategy. Complete state-space matrices are derived from first principles, and the MPC problem is systematically reformulated into standard QP form for efficient real-time implementation.

Quadrotor unmanned aerial vehicles (UAVs) exhibit strongly coupled and nonlinear dynamics that make achieving stable attitude and position control a persistent challenge. Proportional-Integral-Derivative (PID) control remains the dominant strategy in quadrotor applications owing to its structural simplicity, yet its performance depends critically on gain selection, motivating continued interest in more effective tuning strategies. This study presents a comparative evaluation of PID tuning approaches applied to a six-loop cascaded quadrotor control architecture, with three inner loops stabilizing the roll, pitch, and yaw attitude angles and three outer loops regulating translational position. Controller gains are tuned offline using the Genetic Algorithm, the Equilibrium Optimizer, an Arithmetic Optimization Algorithm, and two hybrid Genetic Algorithm--Equilibrium Optimizer configurations, one sequential and one parallel, intended to combine the global search capability of the GA algorithm with the local search efficiency of the EO algorithm. Each optimizer minimizes an ITAE-based cost function, with IAE, ISE, and RMSE reported as secondary measures. The five strategies are benchmarked against the original, non-optimized PID controller on a piecewise waypoint trajectory and a continuous helical trajectory designed to test sustained multi-axis tracking. The hybrid configurations, particularly the parallel variant, consistently achieve the fastest convergence, lowest tracking error, and least overshoot across both scenarios, while the standalone Equilibrium Optimizer remains the weakest performer due to premature search stagnation. The results clarify the trade-offs between convergence speed and tracking accuracy across tuning strategies, offering practical guidance for selecting offline metaheuristic PID tuning approaches for quadrotor control.

The rapid growth of Internet of Things (IoT) devices and the emergence of 5G/6G networks have created major challenges in secure and reliable data transmission. Traditional cryptographic algorithms, while robust, often suffer from high computational complexity and latency, making them less suitable for large-scale, real-time applications. This paper proposes a chaos-based encryption framework that uses the Sprott chaotic oscillator to generate secure and unpredictable signals for encryption. To achieve accurate synchronization between the transmitter and the receiver, two bio-inspired metaheuristic algorithms—the Pachycondyla Apicalis Algorithm (API) and the Penguin Search Optimization Algorithm (PeSOA)—are employed to identify the optimal control parameters of the Sprott system. This optimization improves synchronization accuracy and reduces computational overhead. Simulation results show that PeSOA-based synchronization outperforms API in convergence speed and Root Mean Square Error (RMSE). The proposed framework provides robust, scalable, and low-latency encryption for IoT and 5G/6G networks, where massive connectivity and real-time data protection are essential.

This study investigates a fault-tolerant control strategy that maximises disturbance rejection in the fault-free model while ensuring stability under all fault circumstances. The Lyapunov stability criterion is used to guarantee stability in faulty scenarios, whereas the H∞ control approach is used to minimise the performance indicator, γ, in the fault-free model. The H∞ control approach simplifies the solution procedure by transforming the issue into a linear matrix inequality (LMI) by utilising the bounded real lemma (BRL). Every fault scenario is addressed iteratively by the design strategy.

In this paper, Genetic Algorithm optimization and fuzzy logic for diagnostic and fault detection of rotor broken bar in induction motor is developed. Fuzzy logic variables ranges are generally divided into set of membership functions where the form and interferences are regular. In this paper, fuzzy membership functions are not limited and can vary in the full range of corresponding variable. A Meta heuristic (Genetic algorithm) is then used to optimize membership parameters. This method is applied in diagnostic of a nonlinear system (Induction motor) for the rotor broken bar default. Simulation results are presented to show the effectiveness of the proposed approach.

In this work, an event triggered fault tolerant control strategy based predictive frame is investigated for high nonlinear systems. Taking into consideration the sensor malfunctions during energy production, a speed control approach of the wind turbine systems is proposed by involving state observer control scheme. Moreover, an event-triggered control criterion has used to improve the wind-turbine’s dynamic. Besides, the stability criterion is guaranteed for maintaining the robust performance against disturbances and satisfies the terminal constraints. To test the efficiency of the raised approach in this paper, simulation results is presented.

This paper presents an experimental evaluation of enhanced control strategies for photovoltaic (PV) water pumping systems. A Perturb and Observe (P&O) maximum power point tracking (MPPT) algorithm combined with a PID controller is augmented by two different anti-windup techniques to address actuator saturation. The proposed approaches are implemented and tested on a dedicated PV pumping test bench. Experimental results demonstrate that the anti-windup schemes significantly improve dynamic response, mitigate saturation effects, and enhance overall system efficiency compared to the conventional PID-based MPPT control.

This paper introduces a fault-tolerant fuzzy adaptive controller for a specific class of strict-feedback fractional-order nonlinear systems. Systems are susceptible to actuator and sensor failures, input saturation, and external disturbances. The proposed control scheme effectively addresses four types of faults that affect sensors and actuators, including additive faults such as bias, drift, and loss of accuracy, as well as the multiplicative fault of loss of effectiveness. In contrast to the complexity associated with the back-stepping technique, the proposed approach relies on state transformation. Fuzzy logic systems (FLS) are incorporated to approximate unknown smooth nonlinear functions. A tracking-error observer is introduced to estimate unknown new states, and the properties of the reciprocal function and the strictly positive real function (SPR) are leveraged to overcome the unavailability of original state variables. Stability analysis of the closed-loop system is conducted using the Lyapunov theory. The results of the numerical simulation are presented to validate the feasibility and effectiveness of the proposed controller.

This paper presents a fault-tolerant predictive control based on Particle Swarm Optimization, subject to sensors faults and constraints for a mobile robot. To enhancing trajectory-tracking accuracy, a PSO algorithm is proposed for the computation of best candidate predictive controls, based on minimizing a constrained objective criterion. Then, an update of the predictive controller parameters is defined for accurate position of mobile robot. In order to maintain the desired performances against sensor faults, an evaluation function in a PSO algorithm have been established, taking into account constraints and stability conditions in the reformulation of the new augmented system using a state observer. Simulation results prove the ability of the proposed strategy applying to a mobile robot for the trajectory-tracking.

Variety of synchronization based chaos has been introduced to secure the content of transmitted data during the last decade. The chaos is a particular unpredictable state of nonlinear system exhibiting irregular signals and very sensitive to initial conditions with broadband characteristic. Since the discovery of Pecora and Carroll, that two chaotic systems can synchronize, significant interest has been given to the use of these systems to secure transmissions. The simplified model of nonlinear piezoelectric resonator is assumed in order to give the analysis of chaotic behavior of Duffing function, with a significant increase of external energy. Chaotic systems could be used to create keys for encrypt and secure communication. We have presented in this paper enhanced chaotic bandwidth synchronization using an effective estimation algorithm to identify parameters of both chaotic systems in transmitter and in receiver. This can be achieved by control identification of unpredictability parameters of piezoelectric resonator using ant search optimization algorithm.

In this paper, a Fault-Tolerant Model Predictive Controller (FTMPC) is developed for linear Variable Parameter Systems (LPVs) subject to sensor faults and input constraints. First, an augmented state-space model contains both state variables and estimation error is used to synthesize a robust predictive controller while an observer is designed to estimate state variables and sensor faults. In addition, the optimization problem of the proposed design is formulated to reject disturbances by merging the disturbance estimates in the prediction model. For design purposes, the proposed optimization along with all constraints is expressed in terms of linear matrix inequalities (LMIs). Furthermore, sufficient stability conditions are derived using a Lyapunov approach to assure the convergence of the proposed method. Next, the control problem is obtained via solving these linear matrix inequalities (LMIs) constraints. Finally, the proposed approach performance is tested by controlling an electric circuit.

This paper proposes a new reformulation of the dynamic event-triggered estimation control approach. Based on state observer and via Linear Matrix Inequalities, a DC-DC switching power converters is investigated as discrete-time Markovian switching systems with modelling uncertainties, disturbances and time-delay. An event-triggered control law is implemented to enhance the dynamic of Buck DC-DC converters during the transient response of the voltage. Besides, the stability objective is guaranteed by the use of terminal equality constraints; in particular, to maintain a robust performance against load variation and time-delay. The obtained results show the efficiency of the proposed approach applied to buck DC-DC power converter.

An improved strategy for energy production is introduced based model predictive control, with a precise control instance using event triggered mechanism. Aiming to optimize the wind turbine speed for energy production while accounting for uncertainties and sensor malfunctions. The proposed strategy leverages model predictive control, a powerful control technique known for its ability to optimize system performance by considering future predictions of power generation. In this context, MPC is utilized to formulate an optimal control sequence for wind turbine operation, accounting for various factors such as wind speed fluctuations, turbine dynamics, and power output constraints. Unlike traditional time-triggered control schemes, our approach employs event-triggered sampling, which means that control updates are triggered only when specific events or significant changes occur in the system, thereby optimal control demands and improving efficiency.

This paper introduces the PMST-CHIO, a novel variant of the Coronavirus Herd Immunity Optimizer (CHIO) algorithm, exclusively tailored for individual unmanned aerial vehicle (UAV) path planning in complex 3D environments. While acknowledging and building upon the foundational principles derived from UAV swarm path planning research, the PMST-CHIO distinctively focuses on optimizing the trajectory of single UAVs. It innovatively integrates a parallel multi-swarm treatment mechanism, enhancing the standard CHIO’s exploration and exploitation capabilities significantly. This mechanism diverges from the swarm-based approaches by deploying multiple instances of the CHIO optimizer, each functioning autonomously within its sub-swarm, thereby facilitating independent path planning for individual UAVs. These multiple CHIO instances or CHIO candidates, operate in concert to determine the optimal and collision-free routes, taking into account the unique characteristics of individual UAVs and the intricacies of the service area. The algorithm incorporates two key mechanisms: 1) global exploitation, employing the best solution identified by the highest performing CHIO candidate across the swarms; and 2) strategic shift from parallel multi-swarm exploration to focused exploration by the top-performing CHIO candidate after a specific iteration threshold is reached. This adaptation significantly improves the algorithm’s global search efficiency, convergence behavior, and navigational accuracy under challenging environments. Extensive simulations and comparative studies validate that the PMST-CHIO can effectively overcome the limitations of the standard CHIO algorithm, yielding safer, shorter, and more compliant flight paths for individual UAVs in intricate 3D landscapes.

The Unmanned Combat Aerial Vehicle (UCAV) path planning is a typically complicated global optimization problem. It seeks an optimal or near-optimal flight path in a complex battlefield environment, characterized by a minimal military risk factor and less constrained. The sensitivity and the importance of this military areal task, whose slightest mistake can cost considerable damage, involves the use of highly sophisticated driving methods. In fact, Swarm intelligence algorithms are considered as one of very effective alternative used to deal with latter, due to their capability and flexibility to address complex optimization problems. In this paper, the Spider Monkey Optimization (SMO) algorithm is combined with the so-called Hill Climbing Optimizer (HCO) to improve its exploration and its exploitation capabilities. Indeed, the hybridization mechanism is based on the use of this optimizer under its standard form, to improve firstly each new Spider Monkey (SM) solution (position) generated in the SMO Local Leader Phase, and secondly each new Spider Monkey (SM) solution (position) produced in the SMO Global Leader Phase. Experimental results demonstrate that our proposed method is more competitive than other state-of-the-art evolutionary algorithms for UCAV path planning problem considering the quality and the stability of the final paths.

This brief presents a solution to the fault tolerant control problem for nonlinear systems subject to nonlinear non-affine actuator faults and external disturbance. To address the system nonlinearities and actuator faults, fuzzy inference systems are introduced into the controller construction. To this end, two controllers are proposed where the first one is an adaptive controller designed to mitigate system uncertainties and actuator faults, whereas the second one is a robust term aimed at reducing the approximation discrepancy and also to handle external disturbances. The proposed scheme enables automatic handling of external disturbances and actuator faults through online adaptation of the controller. The Butterworth filter is employed to overcome the algebraic loop issue, ensuring the best estimation of the ideal controller. The stability analysis is conducted using Lyapunov second method. Numerical simulations are performed to validate the effectiveness of the controller.

This paper introduces a novel approach for achieving optimal attitude control of a quadrotor in the presence of uncertainty, external disturbances, time-varying sensor faults, and nonaffine nonlinear actuator faults. The proposed method utilizes the Particle Swarm Optimization (PSO) technique to approximate adaptive parameters and fuzzy initial values, resulting in a reliable controller that can quickly compensate for changes in the starting point. Fuzzy systems are employed to estimate unknown nonlinearities, nonaffine nonlinear actuator faults, and time-varying sensor faults. To address approximation errors and external disturbances, a robust control term is incorporated. The issue of an algebraic loop is resolved using a Butterworth low-pass filter. Additionally, the proposed robust scheme effectively handles external disturbances without relying on approximations, and the controller is dynamically updated through online reconfiguration. The stability of the entire closed system is analyzed based on Lyapunov theory. Simulation scenarios are presented to demonstrate the efficacy and benefits of the proposed approach.

This paper provides a detailed analysis of the output voltage/current tracking control of a PWM DCDC converter that has been modeled as a Markov jump system. In order to achieve that, a dynamic sensorless strategy is proposed to perform active disturbance rejection control. As a convex optimization problem, a novel reformulation of the problem is provided to compute optimal control. Accordingly, necessary less conservative conditions are established via Linear Matrix Inequalities. First, a sensorless active disturbance rejection design is proposed. Then, to carry out the control process, a robust dynamic observer-predictive controller approach is introduced. Meanwhile, the PWM DC-DC switching power converters are examined as discrete-time Markovian switching systems. Considering that the system is subject to modeling uncertainties, time delays, and load variations as external disturbances, and by taking partial input saturation into account, the Lyapunov-Krasovskii function is used to construct the required feasibility frame and less conservative stability conditions. As a result, the proposed design provides an efficient control strategy with disturbance rejection and time-delay compensation capabilities and maintains robust performance with respect to constraints. Finally, a PWM DC-DC power converter simulation study is performed in different scenarios, and the obtained results are illustrated in detail to demonstrate the effectiveness of the proposed approach.

Unmanned Combat Aerial Vehicle (UCAV) path planning is a challenging optimization problem that seeks the optimal or near-optimal flight path for military operations. The problem is further complicated by the need to operate in a complex battlefield environment with minimal military risk and fewer constraints. To address these challenges, highly sophisticated control methods are required, and Swarm Intelligence (SI) algorithms have proven to be one of the most effective approaches. In this context, a study has been conducted to improve the existing Spider Monkey Optimization (SMO) algorithm by integrating a new explorative local search algorithm called Beta-Hill Climbing Optimizer (BHC) into the three main phases of SMO. The result is a novel SMO variant called SMOBHC, which offers improved performance in terms of intensification, exploration, avoiding local minima, and convergence speed. Specifically, BHC is integrated into the main SMO algorithmic structure for three purposes: to improve the new Spider Monkey solution generated in the SMO Local Leader Phase (LLP), to enhance the new Spider Monkey solution produced in the SMO Global Leader Phase (GLP), and to update the positions of all Local Leader members of each local group under a specific condition in the SMO Local Leader Decision (LLD) phase. To demonstrate the effectiveness of the proposed algorithm, SMOBHC is applied to UCAV path planning in 2D space on three different complex battlefields with ten, thirty, and twenty randomly distributed threats under various conditions. Experimental results show that SMOBHC outperforms the original SMO algorithm and a large set of twenty-six powerful and recent evolutionary algorithms. The proposed method shows better results in terms of the best, worst, mean, and standard deviation outcomes obtained from twenty independent runs on small-scale (D = 30), medium-scale (D = 60), and large-scale (D = 90) battlefields. Statistically, SMOBHC performs better on the three battlefields, except in the case of SMO, where there is no significant difference between them. Overall, the proposed SMO variant significantly improves the obstacle avoidance capability of the SMO algorithm and enhances the stability of the final results. The study provides an effective approach to UCAV path planning that can be useful in military operations with complex battlefield environments.

This paper presents a novel observer-based robust fault predictive control (OBRFPC) approach for a wind turbine time-delay system subject to constraints, actuator/sensor faults, and external disturbances. The proposed approach is based on an augmented state-space representation that contains state-space variables and estimation errors. The proposed augmented representation is then used to synthesize a robust predictive controller. In addition, an observer is developed and used to estimate both state variables and actuator/sensor faults. To ensure that the proposed approach has disturbance rejection capabilities, the disturbance estimates were merged with the prediction model. In addition, the disturbance rejection capabilities and fault tolerance were insured by formulating the control process as an optimization problem subject to constraints in terms of linear matrix inequalities (LMIs). As a result, the controller gains are acquired by solving an LMI problem to guarantee input-to-state stability in the presence of sensor and actuator faults. A simulation example is conducted on a nonlinear wind turbine (1 MW) model with 3 blades, a horizontal axis, and upwind variable speed subject to actuator/sensor faults in the pitch system. The results demonstrate the ability of the proposed method in dealing with nonlinear systems subject to external disturbances and keeping the control performance acceptable in the presence of actuator/sensor faults.