Fuzzy adaptive active disturbance rejection control incorporating HiTL for fractional-order multiagent formation under sensor failures

Adaptive fault-tolerant safety-guaranteed fuzzy event-triggered rendezvous control for heterogeneous USV-UUV systems.

This article addresses the formation control of under-actuated multiple autonomous surface vehicles (MASVs) with input quantization under communication delay conditions, which is influenced by external marine disturbances and internal model uncertainties. A two-level distributed guidance and quantization control architecture based on the Nussbaum function is proposed. At the communication level, a time-delay distributed event-triggered extended state observer (ESO) is introduced to estimate the state of the single virtual leader, thereby further conserving communication resources. At the control level, the distributed formation guidance laws based on ESO are proposed in the kinematic subsystem, enabling effective tracking of the ideal trajectory while estimating the states of neighboring agents and unknown ocean disturbances. In the dynamics subsystem, a fuzzy logic system is used to estimate the uncertain terms within the model, and a linear model is introduced to handle the input quantization process. Additionally, the fuzzy adaptive quantization tracking control laws based on the Nussbaum function are proposed to achieve accurate tracking of the guidance signals and reduce actuator execution frequency, which makes the proposed scheme more applicable to practical marine engineering scenarios. The stability of the designed control structure is proven based on stability theory, and all signals within the closed-loop control system are uniformly ultimately bounded. Simulation experiments validate the rationality and effectiveness of the proposed method.

This paper studies the problem of adaptive fuzzy output feedback funnel secure control for vehicle platoon systems under replay attacks, which contains nonlinear dynamics and unmodeled dynamic. Fuzzy system is adopted to identify unknown nonlinear dynamics, then fuzzy observer is designed to estimate the immeasurable states, and funnel functions are introduced to constrain the distance between each vehicle. By introducing Lipschitz conditions, it is possible to analyse the error changes of systems subjected to replay attacks and obtain the threshold for error changes. By using the changing supply function technique to address the unmodeled dynamics, an observer-based robust secure adaptive fuzzy funnel formation control scheme is developed. Based on Lyapunov stability theory, it can ensure all signals of the controlled system are bounded, and the desired spacing and avoid collision can be maintained. Finally, a simulation is considered to verify the effectiveness of the developed control algorithm.

Echo-state network based adaptive fuzzy sliding-mode consensus control scheme for nonlinear multi-agent systems with external disturbances.

Design and experimental validation of a new piecewise adaptive fuzzy logic controller with anti-windup for a synchronous buck-converter based photovoltaic emulator

Switching event-triggered-based adaptive fuzzy cooperative control for multiagent systems: A connectivity-preserving method with dynamic boundary adjustment

Adaptive fuzzy finite-time control for a class of stochastic nonholonomic systems with dead-zone input

Adaptive fuzzy event-triggered ice-breaking train formation control of autonomous surface vehicles with velocity constraint

Practically predefined-time adaptive fuzzy control for stochastic nonlinear systems with full state constraints and dead zones

This paper studies the adaptive fuzzy command-filtered backstepping output feedback control for discrete-time nonlinear multi-agent systems. The command filter is used to address the causality contradiction and the error compensation mechanism can remove the filter errors. Fuzzy logic systems are utilised to approximate the unknown nonlinearities of each agent, and the fuzzy state observer is designed to estimate the immeasurable states. Adaptive updating laws are incorporated into both the observer and controller to handle unknown parameters. By constructing weighted Lyapunov functions, the stability of the closed-loop system is rigorously analyzed, proving that all signals are uniformly ultimately bounded. An example of vehicle system is provided to demonstrate the effectiveness of the proposed control strategy.

With the advancement of autonomous technologies, the need for robust control strategies in unstructured environments is becoming increasingly important. The ability to track a trajectory and accurately control the motion of a robot is a key aspect of mobile robotics and is essential if wheeled mobile robots (WMRs) are to successfully perform tasks and function in the real world. Due to the complex and unstructured working environments, the control systems of WMRs must deal with difficulties such as extreme non-linear behaviors of the dynamic systems, unmodeled parameters of the systems, and external disturbances. This paper presents an adaptive fuzzy gain scheduling PID controller that addresses the challenges posed by structured uncertainties like kinematic wheel slips, random actuator noise, and external disturbances. The controller is based on a robust cascaded control structure that can be used for tracking the desired trajectory. The robustness of the controller is ensured by uniformly ultimately bounded analysis. The control system incorporates adaptive fuzzy logic and PID control to achieve a more advanced level of trajectory-tracking control. The control systems enhance fuzzy logic and PID control with an adaptive capability aimed at improving the systems' robustness against external disturbances. Changes in the robot dynamics or the environment may happen, but the controller can adjust its parameters in real time and achieve the desired result due to the controller's adaptation mechanism. The efficacy of the controller is validated using extensive simulations for tracking complex lemniscate (∞) curves. The results are compared with conventional PID and adaptive dynamic control methods to demonstrate that the proposed controller reduces the RMS tracking error. The results clearly demonstrate that the controller can reject disturbances while achieving precise navigation even for 100% variations in the parameters. The results presented in this paper provide a computationally efficient framework to bridge the gap between kinematics and dynamics for real-time robotic applications.

Abstract. This paper introduces an improved adaptive fuzzy sliding-mode control approach for semi-active seat suspension utilizing magnetorheological fluid (MRF) dampers. Firstly, the damping characteristic of the MRF damper was tested, and the dynamics model of MRF damper was established. Secondly, the 5-degree-of-freedom “human-seat” suspension system model was built and adaptively simplified, and a suitable adaptive control law was designed to estimate the perturbations generated during the simplification process of the human-seat model online. Based on the simplified model, a fuzzy algorithm was adopted to optimize the approach rate parameters in the sliding-mode control so as to improve the robustness of the system while guaranteeing the approach rate, and hyperbolic tangent function was employed to replace the sign function in the switching term to make the system more continuous during the switching process, which effectively reduces the “chatter” problem in the sliding-mode control. Thirdly, the dynamics model of the MRF damper is added into the sliding-mode control model to ensure the effectiveness of the MRF damper output control force. Finally, the effectiveness of the improved adaptive fuzzy sliding-mode control method was confirmed through simulation, demonstrating its capability to significantly reduce seat acceleration and suspension dynamic deflection under different working conditions compared with passive damping, skyhook control, and sliding-mode control.

Problem: Controlling nonlinearly parameterized systems with unknown disturbances remains challenging because classical adaptive approaches rely on separation-of-variables and reparameterization techniques, leading to increased parameter dimensions, conservative stability bounds, and implementation complexity. Objective: This paper develops a data-driven predictive fuzzy adaptive control (DD-PFAC) framework that eliminates the need for separation techniques while achieving superior tracking performance and formally certified stability. Novelty: The key innovation is a two-layer architecture. Layer 1 provides direct fuzzy approximation of composite nonlinear functions (system dynamics plus disturbance bound) without parameter reparameterization, reducing parameter complexity from O(qn) to O(nN). Layer 2 employs Hankel matrix-based predictive optimization to adaptively tune both control gains ci(k) and adaptation rates γi(k) online using 80–150 recent input–output samples. Methodology: A Lyapunov function augmented with a prediction-error term is used to prove uniform ultimate boundedness of all closed-loop signals. A projection-based recursive least-squares algorithm updates the gain parameters online while guaranteeing ci(k)≥cmin>0 at all times. Results: Comparative simulations demonstrate 31.4% reduction in integral square error, 27.8% reduction in mean absolute error, and 37.4% reduction in steady-state error versus traditional adaptive fuzzy control. A four-group ablation study confirms that adaptive gain scheduling contributes 27.7% and predictive compensation contributes 6.5% to the total MAE improvement. Robustness tests validate consistent 28–32% performance advantage across sinusoidal, pulse, step, and large-disturbance scenarios.

Purpose This paper aims to propose an adaptive fuzzy output feedback fault-tolerant control scheme for an active suspension system (ASS). Dual-channel event-triggered strategy (DCETS) is designed to regulate the control and output signal transmission respectively for ASS. Design/methodology/approach In contrast to conventional single-channel event-triggered control mechanism, the key advantage of this strategy lies in the ability to significantly reduce communication frequency in both forward and feedback channels while maintaining closed-loop stability. A fault compensation strategy is designed to address actuator failures through parameter estimation techniques, which guarantees the vertical state of the ASS remains stable even in case of actuator fault. Theoretical analysis shows that the proposed control scheme can guarantee the states of the system are bounded, and Zeno behavior is excluded. Finally, the effectiveness of the proposed algorithm is verified by a random pavement test. Findings An adaptive fuzzy output-feedback fault-tolerant control strategy with dual-channel event-triggered mechanism is proposed for ASS. Originality/value Adaptive fuzzy output-feedback fault-tolerant control strategy is synthesized with fault compensation and DCETS, enabling ASS to maintain performance under unmeasurable states while reducing communication resource consumption.

Abstract Abstract—This study investigates the efficiency and effectiveness of a Fuzzy Logic Adaptive Control (FLAC) system designed to regulate the TCS for longitudinal-wheel dynamics in In-Wheel Motorized Electric Vehicles (IWM-EVs), especially in challenging driving scenarios such as slippery ice roads. The FLAC system integrates a Fuzzy Logic Controller (FLC) and a Proportional-Integral (PI) controller, employing adaptive parameters based on wheel slip dynamics. Numerous controlled wheel slip models are compared through MATLAB simulations to identify the most stable and efficient approach. In addition to offline simulations, the proposed control strategy was validated through real-time Model-in-the-Loop (RT-MIL) implementation on an OPAL-RT platform to assess its performance under deterministic execution constraints. The real-time results confirm the robustness and practical feasibility of the FLAC-based traction control approach. A detailed analysis of the FLC-PI controller operations underscores its ability to dynamically adjust the torque requests to optimize the wheel slip and vehicle dynamics, further emphasizing the effectiveness of the FLAC system in enhancing vehicle control under diverse driving conditions.

The high-performance tracking control of steering angle for steer-by-wire (SBW) system is foundation of vehicle driving safety. However, the variable steering load will reduce the tracking performance in the form of system disturbance, thereby causing steering hysteresis, which is uncertain, nonlinear, difficult to be modeled precisely and addressed effectively. This leads the accurate and stable steering angle tracking control to remaining challenging. In order to solve this issue, this article proposes an adaptive pseudoinverse fuzzy control method using nonlinear-quantization neural network for SBW system. First, to dynamically describe the uncertain steering hysteresis of SBW system online, a pseudoinverse compensator is constructed using fuzzy nonlinear-quantization cerebellar model articulation neural network, which avoids the complex dynamics modeling and inverse calculation. The fuzzy logic system is introduced to nonlinearly quantize the neural network input to improve the compensation accuracy of pseudoinverse compensator without increasing the computational burden. Then, to reduce the tracking error caused by the system disturbance from the variable steering load, an adaptive fuzzy controller with adjustable fuzzy mapping is proposed to enhance the antidisturbance control ability in the tracking process of steering angle. Finally, the proposed control method is verified by a vehicle equipped with SBW system. Experimental results show that, the proposed control method can effectively reduce steering hysteresis caused by variable steering load, and improve the tracking accuracy and stability of steering angle of the SBW system.

Abstract This paper proposes an improved model predictive control (MPC) method. It is based on fuzzy adaptive control, targeting mobile robots that track nonlinear moving targets outdoors. The core of this method is multi-parameter dynamic decoupling. To adapt to the target’s motion characteristics, five independent fuzzy controllers are designed. They dynamically adjust MPC’s predictive time domain and other parameters. The method’s performance is compared with ordinary fuzzy control and conventional MPC. The comparison is done in simulations of the target’s nonlinear motion. Simulation results show three strengths of the proposed method: better trajectory fit, faster deviation convergence, and higher stability than the comparison algorithms. Moreover, compared with conventional MPC, its distance tracking accuracy rises by 54.8%, and its angular tracking accuracy by 39.6%. This method can effectively improve undesirable phenomena. One example is a track offset that may occur when using traditional algorithms. It can provide technical references for scenarios like agricultural plant protection. In the future, it can further optimize control rules and expand to multi-target tracking scenarios.

Fixed-time adaptive fuzzy event-triggered fault-tolerant containment control for nonlinear multi-agent systems

Fuzzy adaptive asymptotic tracking control for uncertain nonlinear systems with full-state constraints of arbitrary time