Adaptive Fuzzy Control Method Research Articles

Iterative learning resilient consensus of uncertain nonlinear multi-agent systems vulnerable to false data injection attacks

This work investigates the iterative learning resilient consensus of uncertain nonlinear high-order multi-agent systems (MASs) against false data injection (FDI) attacks. First, in order to reduce the impact of FDI attacks on the nonlinear MASs, a novel coordinate transformation technique is proposed, which is composed of the states after being attacked, and the Nussbaum gain technique is adopted to address the problem of unknown attack gains resulting from FDI attacks. Then, by employing compromised state variables, a fuzzy adaptive iterative learning resilient control method is presented based on the composite energy function, where unknown nonlinear functions are handled using fuzzy logic systems. The presented approach can guarantee that the outputs of all followers precisely track the leader in a limited time interval while ensuring that all closed-loop signals are bounded. Finally, a numerical simulation is provided to demonstrate the efficacy of the designed control strategy.

Adaptive fuzzy event-triggered control for a class of nonlinear time-delay multi-agent systems with dead zone and partial state constraints

This paper presents an adaptive fuzzy event-triggered control (ETC) method for a class of time-delay nonlinear multi-agent systems (MASs) with dead zone (DZ) and partial state constraints (PSCs). It should be pointed out that the states considered in this paper are unmeasurable and part of the system states are constrained by time-varying boundary. By utilizing fuzzy logic systems (FLSs) and backstepping design framework, a fuzzy state observer is constructed to estimate the unmeasured states, while the introduction of a time-varying barrier Lyapunov function (BLF) addresses the partial state constrained problem. Furthermore, during the design process of the state observer, compensation for the influence of the unknown DZ input on the system is achieved through utilization of known designed event-triggered input signal and adaptive parameter. Building upon this foundation, employing the Lyapunov stability theory and adaptive backstepping technique with ETC strategy, the developed distributed control mechanism demonstrates that all variables of the closed-loop system are bounded, the consensus error of the system can converge to a small region of origin and Zeno behavior can be ruled out. Finally, two simulation examples further illustrate the proposed control strategy and theorem.

Fault-tolerant control of bullet train based on fuzzy adaptive control

Abstract For the trajectory tracking problem of a single-particle train dynamics model under the condition of partial loss of power in the train power unit due to actuator failure during the operation of the EMU, considering the situation where the system exists with uncertain dynamics from the model and unknown external disturbers, a control method based on fuzzy adaptive fault-tolerant control method for EMU is proposed. Under the framework of the backstepping method, the fuzzy system is used to approach the system function, and a fuzzy adaptive controller is designed. It is proved that the designed control law can enable the train system to track the given speed and displacement trajectory under partial power loss. Using the CRH3 train as the research object, the results show that the designed controller can complete the trajectory tracking control task.

A Predefined Time Constrained Adaptive Fuzzy Control Method With Singularity-Free Switching for Uncertain Robots

A Predefined Time Constrained Adaptive Fuzzy Control Method With Singularity-Free Switching for Uncertain Robots

Variable Power Tracking Control Strategy of Doubly Fed Induction Generators for Fast Frequency Responses

Frequency regulation and droop control of doubly fed induction generators (DFIGs) can quickly respond to frequency changes and reduce the maximum rate of frequency (MROFF) in power systems. However, due to real-time dynamic changes in the MPPT control loop, the ability to improve the lowest frequency point is limited. Therefore, this article first describes an in-depth analysis of the dynamic characteristics of the incremental power of frequency regulation with droop control using an equivalent linear model. The limitations of improving the lowest frequency point under the influence of dynamic changes in the MPPT control loop are revealed. Secondly, to address the impact of these dynamics, an improved decoupling frequency regulation (IDFR) strategy based on power tracking is proposed, aiming to increase the maximum frequency deviation (MFD) and MROCOF. Then, in order to overcome the difficulty of adjusting control coefficients in the IDFR strategy, an adaptive control coefficient tuning fuzzy control method based on frequency deviation and ROCOF was proposed to flexibly adjust control requirements under various working conditions, thereby improving the control stability and performance of the system and effectively solving the problem of control coefficient allocation. Finally, to verify the frequency regulation performance of the proposed IDFR strategy under various operating conditions, simulations were conducted based on different disturbances and wind conditions. The results show that the proposed IDFR strategy significantly improves the system MFD and MROCOF improvement ability under various conditions.

Trajectory tracking control of autonomous space-based simulators for the on-orbit assembly of large space optical telescopes

To enhance the precision of on-orbit assembly for large space optical telescopes, the control of attitude motion in the Autonomous Space-based Simulator (ASS) is imperative. This paper presents a robust finite-time trajectory tracking control method aimed at improving the position and attitude tracking performance of ASS. Considering the dynamic characteristics inherent in ASS, we introduce an improved non-singular fast terminal sliding mode controller (INFTSMC) incorporating an adaptive fuzzy mechanism to attain finite-time error convergence and robust control. The adaptive fuzzy logic control (AFLC) method yields an equivalent gain, eliminating discontinuous switching in terminal sliding mode control (TSMC). This reduction minimizes chattering in control inputs and compensates for uncertainties and time-varying disturbances within the dynamic model. The proposed method’s effectiveness is validated through comprehensive simulation and experimental studies.

Adaptive fuzzy finite-time PID backstepping control for chaotic systems with full states constraints and unmodeled dynamics

To solve the problem that the controlled object and system model cannot be fully mastered or the system parameters cannot be well obtained through conventional measurement methods, in this paper, a novel adaptive fuzzy finite-time PID backstepping control method is developed for a class of nonlinear chaotic systems with full state constraints and unmodeled dynamics. The unmodeled dynamics are handled by employing a dynamic signal. To avoid the violations of full state constraints (FSCs), a barrier Lyapunov function is used to implement a PID backstepping framework, whose merit consists in that three control gain constants KP, KI and KD with corresponding proportional relationships are designed to further improve control precision. Besides, the PID control method also embodies three dynamic gain components ΔKP, ΔKI and ΔKD, all of which are free functions that can be self-tuning online, and have a significant link with the adaptive law and modify accordingly. Finally, theoretical analysis confirms that all signals are ultimately uniformly bounded, the error signal converges to a nearby area near the origin in a finite-time, and the FSCs are not smashed. The practicality of the proposed method is confirmed by two simulation studies using the second-order Duffing chaotic system and third-order Chua's circuit.

Adaptive decentralized fuzzy compensation control for large optical mirror processing systems

PurposeLarge optical mirror processing systems (LOMPSs) consist of multiple subrobots, and correlated disturbance terms between these robots often lead to reduced processing accuracy. This abstract introduces a novel approach, the nonlinear subsystem adaptive dispersed fuzzy compensation control (ADFCC) method, aimed at enhancing the precision of LOMPSs.Design/methodology/approachThe ADFCC model for LOMPS is developed through a nonlinear fuzzy adaptive algorithm. This model incorporates control parameters and disturbance terms (such as those arising from the external environment, friction and correlation) between subsystems to facilitate ADFCC. Error analysis is performed using the subsystem output parameters, and the resulting errors are used as feedback for compensation control.FindingsExperimental analysis is conducted, specifically under the commonly used concentric circle processing trajectory in LOMPS. This analysis validates the effectiveness of the control model in enhancing processing accuracy.Originality/valueThe ADFCC strategy is demonstrated to significantly improve the accuracy of LOMPS output, offering a promising solution to the problem of correlated disturbances. This work holds the potential to benefit a wide range of practical applications.

Adaptive Fuzzy Output Feedback Fault-Tolerant Control for Active Suspension Systems

In this paper, a novel adaptive fuzzy output feedback fault-tolerant control (FTC) method is proposed for active suspension systems. The quarter active suspension systems studied in this paper contains electromagnetic actuator faults and unavailable state variables, such as the velocity of active suspensions and the electric current intensity of electromagnetic actuators. First, the complex spring nonlinear dynamics are approximated by fuzzy logic systems (FLSs), and the unmeasurable states are estimated by a fuzzy state observer. Based on an adaptive backstepping technique along with specific Lyapunov functions, a novel adaptive fuzzy output feedback FTC design is presented. To eliminate the need for knowing efficiency indexes, a fault compensation strategy is also developed by utilizing parameter estimation technique. In the presence of electromagnetic actuator failure, all vertical vibration states of the active suspension system remain stabilized. Eventually, the feasibility of the proposed control algorithm is evaluated through random road surface and bump road surface tests.

Robust Adaptive Fuzzy Control for Second-Order Euler-Lagrange Systems With Uncertainties and Disturbances via Nonlinear Negative-Imaginary Systems Theory.

Ensuring robust and precise tracking control in the presence of uncertain multi-input-multi-output (MIMO) system dynamics and environmental variations is a significant challenge in the field of robust and adaptive control theory. While fuzzy control strategies have demonstrated good tracking performance in normal conditions, designing and tuning fuzzy controllers can be a challenging task in highly uncertain environments. In this study, we investigate a novel approach that combines robust nonlinear negative-imaginary (NI) systems theory with a self-adaptive fuzzy control scheme and the Lyapunov synthesis to develop a robust adaptive negative-imaginary-fuzzy (RANIF) control scheme. We optimize the critical parameters of the proposed fuzzy system using a self-tuning technique with a proportional-derivative sliding manifold. Furthermore, unlike the existing adaptive fuzzy control methods, we propose a small number of membership functions and systematically derive the fuzzy rules by employing Lyapunov, nonlinear NI, and dissipativity theories, which simplify the tuning process, work out the matter of "explosion of complexity", and reduce computational complexity. We demonstrate the global stability of the closed-loop system using nonlinear NI theory. To evaluate the effectiveness of our proposed approach, we present simulation results for two examples involving uncertain MIMO second-order Euler-Lagrange systems. These systems, known for their capacity to represent a diverse range of practical physical systems, serve as suitable testbeds for our methodology. Our results show that RANIF outperforms other control methods, such as nonlinear strictly NI-Fuzzy, fuzzy-logic control, model predictive control, and conventional PID control, in terms of robustness to disturbances and inestimable faults, trajectory tracking performance, and computational complexity.

Hysteresis Compensation-Based Intelligent Control for Pneumatic Artificial Muscle-Driven Humanoid Robot Manipulators With Experiments Verification

Pneumatic artificial muscles (PAMs), as a kind of soft actuators, can overcome compliance limitations of traditional rigid actuators to improve the adaptability of robots. However, some inherent strong nonlinearities and time-varying properties of PAMs, e.g., complex hysteresis and creep, may lead to a lot of control problems. In addition, PAM-driven systems are also faced with input constraints (e.g., deadzones, saturations, and unidirectional inputs), unknown/unmodeled dynamics and external disturbances, which badly degrade the control performance and even cause accidents. Therefore, this paper proposes a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">new</i> hysteresis compensation-based immersion and invariance (I&I) adaptive fuzzy control method for PAM-driven humanoid robot manipulators, which can approximate the unknown functions and estimate the unknown parameters. To our knowledge, this is the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">first</i> method for PAM-driven systems that compensates for system nonlinearities ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">not only</i> complex hysteresis, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">but also</i> input deadzones) by utilizing the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">prior</i> information in inverse hysteresis models, and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">simultaneously</i> estimates the unknown functions and parameters by designing a fuzzy update law and a parameter update law based on I&I methodology, respectively, which increases the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">control frequency</i> of systems and improves tracking performance during high-speed motions. Finally, we apply the proposed approach on a self-built PAM-driven humanoid robot manipulator to validate its effectiveness and robustness. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Faced with the practical requirements of robots that interact closely with humans, improving the adaptability and compliance of robots by utilizing soft actuators, such as PAMs, can satisfy current demands. Moreover, PAMs also have many expective characteristics (e.g., high power density, light material, low costs, clean power, etc.), which makes PAMs occupy an important status in the field of soft robotics. However, unknown parameters/structures, strong nonlinearities, and input constraints, may badly degrade the control performance of PAMs. Based on the above characteristics, this paper proposes a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">new</i> hysteresis compensation-based adaptive fuzzy controller for PAM-driven humanoid robot manipulators, which realizes <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">accurate tracking</i> control during high-speed motions by using inverse hysteresis models to compensate for strong nonlinearities in PAMs, and a fuzzy update law and a parameter update law based on I&I methodology are utilized to estimate unknown parameters/structures. In addition, the proposed controller can <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">simultaneously</i> compensate for input deadzones by utilizing the hysteresis information, which improves the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">control frequency</i> of the manipulators, and can rapidly suppress tracking errors. Experimental results are provided to validate the effectiveness of the presented method. In the future, more effective compensation methods, such as rate-dependent hysteresis models, will further be carried out to compensate for system nonlinearities.

A control method for water cannon of unmanned fireboats based on EGWO-ADFUZZY

In order to improve the accuracy and efficiency of marine firefighting operations, a control method for firefighting water cannon of unmanned fireboats based on EGWO-ADFUZZY is proposed in this paper. Firstly, the improved enhanced grey wolf optimization (EGWO) algorithm is used in conjunction with the jet trajectory model to design the EGWO main controller. Based on this controller, search range adjustment strategy and jet trajectory model correction strategy are proposed. Secondly, the online particle swarm optimization (PSO) algorithm is integrated with the fuzzy control algorithm to enhance its adaptive capability. As a result, the adaptive fuzzy (ADFUZZY) controller is designed to cope with ship motion disturbances. The experimental results show that, the EGWO-ADFUZZY controller can quickly perform firefighting operations within an error range of 5 × 10−2 m. Compared to the double adaptive fuzzy control method, it exhibits an efficiency improvement of approximately 40%. Furthermore, it further confirms that this method is capable of effectively countering the disturbances caused by ship motion, ensuring the efficiency of firefighting operations for unmanned fireboats at sea. This control method provides valuable insights and references for future research and engineering applications in marine firefighting.

Cross‐entropy‐based adaptive fuzzy control for visual tracking of road cracks with unmanned mobile robot

AbstractVisual tracking of road cracks in unstructured road environment was, is, and remains a crucial and challenging task, which plays a vital role in accurate crack sealing for automated road cracks repair. However, many problems have not been well solved in existing automated road cracks repair, such as the low automation due to partial dependence on manual and the interrupted traffic flow caused by the heavy equipment used. In this article, a cross‐entropy‐based adaptive fuzzy control (CEAFC) method is proposed, which reaches visual tracking with unmanned mobile robot (VT‐UMbot) for road cracks. Specifically, the CEAFC method uses cross‐entropy optimization iteration to tune parameters for the tracking controller, and fuzzy logic is constructed to explore robustness improvement. Moreover, a framework of VT‐UMbot based on a four‐wheel independent differential drive is established, and visual servo and tracking control are integrated into the system. Our experiment shows that the proposed method is extensively evaluated on three road cracks scenarios and achieves state‐of‐the‐art performance with high efficiency.

Improved Nonlinear Extended Observer Based Adaptive Fuzzy Output Feedback Control for a Class of Uncertain Nonlinear Systems With Unknown Input Hysteresis

This study focuses on the problem of adaptive fuzzy dynamic surface output feedback control for a class of uncertain nonlinear systems subjected to unknown input hysteresis. A Prandtl–Ishlinskii (PI) model is applied to the uncertain nonlinear system for describing the unknown input hysteresis, making the controller design feasible. In addition, a nonlinear extended state observer (NESO) is designed for simultaneously estimating the unmeasurable states and generalized disturbances, including the nonlinear hysteresis term of the PI model and external disturbances. In addition, a novel nonlinear function is designed to replace the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$fal(\cdot)$</tex-math></inline-formula> function of the general NESO to address a modification that increases the convergence speed. Considering the incorporation of the improved nonlinear extended state observer (INESO), an adaptive output feedback control scheme is proposed based on fuzzy logic system and dynamic surface techniques. A command filter is employed to avoid the “explosion of complexity” problem inherent in the backstepping technique, while compensating the filtering error caused by adopting the filter. The Lyapunov approach is used to demonstrate the stability of the entire closed-loop system. Experiments regarding a piezoelectric micro–positioning stage are conducted, the results of which illustrate that the proposed adaptive fuzzy output feedback control method can guarantee a satisfactory tracking performance.

Adaptive Fuzzy Command Filtered Tracking Control for Flexible Robotic Arm with Input Dead-Zone

In this paper, an adaptive fuzzy tracking control method is proposed to address the issues of dead-zone and unobservable states in a flexible robotic arm system. The control design process begins with the utilization of a fuzzy logic system to approximate the nonlinear functions present in the flexible robotic arm system. To estimate the unobservable states of the system, a state observer is then designed. To alleviate the computational complexity during controller design, the command filtering technique is introduced. Additionally, the Nussbaum function is incorporated to address the unknown control gain problem. The stability of the system can be verified through the design of a Lyapunov function. This study’s simulation results demonstrate that the designed control system can closely track the specified reference signals. The closed-loop system effectively controls the flexible robotic arm, as verified through experimentation.

Adaptive finite-time anti-disturbance fuzzy control for uncertain time-delay nonlinear systems and its application to SVC

The adaptive finite-time anti-disturbance fuzzy control method for n-dimensional uncertain nonlinear systems with input delay is proposed in this paper. For unknown nonlinear terms of the controlled system, the fuzzy logic systems (FLSs) are utilized to approximate them. To further improve the robustness of the controlled system, the disturbance observer is established to estimate external disturbances, facilitating the designed controller to compensate for the influence of external disturbances. Moreover, the Pade approximation method is used to transform the time-delay system model, which is convenient for controller design. With the aid of the finite-time command filtered backstepping technique, the adaptive finite-time disturbance rejection controller with low computational complexity is designed, which can compensate for the errors caused by the filters and assure that system state variables are stable and bounded in a finite time. In the end, the simulation for the single-machine infinite bus (SMIB) system with static var compensator (SVC) is presented to illustrate the effectiveness of the proposed control approach.

Hierarchical sliding mode-based adaptive fuzzy control for uncertain switched under-actuated nonlinear systems with input saturation and dead-zone

PurposeThis paper aims to study the issues of adaptive fuzzy control for a category of switched under-actuated systems with input nonlinearities and external disturbances.Design/methodology/approachA control scheme based on sliding mode surface with a hierarchical structure is introduced to enhance the responsiveness and robustness of the studied systems. An equivalent control and switching control rules are co-designed in a hierarchical sliding mode control (HSMC) framework to ensure that the system state reaches a given sliding surface and remains sliding on the surface, finally stabilizing at the equilibrium point. Besides, the input nonlinearities consist of non-symmetric saturation and dead-zone, which are estimated by an unknown bounded function and a known affine function.FindingsBased on fuzzy logic systems and the hierarchical sliding mode control method, an adaptive fuzzy control method for uncertain switched under-actuated systems is put forward.Originality/valueThe “cause and effect” problems often existing in conventional backstepping designs can be prevented. Furthermore, the presented adaptive laws can eliminate the influence of external disturbances and approximation errors. Besides, in contrast to arbitrary switching strategies, the authors consider a switching rule with average dwell time, which resolves control problems that cannot be resolved with arbitrary switching signals and reduces conservatism.

Finite-Time Adaptive Fuzzy Control for Three-Phase PWM Rectifiers With Improved Output Performance

For three-phase pulse width modulation (PWM) rectifiers, system uncertainties, such as load disturbances, parametric uncertainties, and grid disturbances, may result in significant DC-link voltage fluctuations, degrade the quality of grid currents, and even lead to system instability. To address this issue, a modified dynamic model of the PWM rectifier with system uncertainties is established, and a finite-time adaptive fuzzy control (FTAFC) method with the dual-loop structure is proposed. In the outer voltage loop, a finite-time adaptive law is used to maintain stable DC-link voltages despite load disturbances. In the inner power loop, an adaptive fuzzy technique is used to estimate and compensate system uncertainties, thus ensuring finite-time power tracking. It is confirmed that all signals of the closed-loop system are finite-time bounded. The experimental and simulation results demonstrate the effectiveness of the proposed method.

Fuzzy Control Based on Reinforcement Learning and Subsystem Error Derivatives for Strict-Feedback Systems With an Observer

In this paper, a novel optimized fuzzy adaptive control method based on tracking error derivatives of subsystems is proposed for strict-feedback systems with unmeasurable states. A cost function based on the tracking error derivative is used. It not only solves the problem that the traditional input quadratic cost function at the infinite time is unbounded, but also solves the problem that the optimal control input derived from the cost function with exponential discount factor cannot make the error asymptotically stable. Considering the case where the states are unmeasurable, a fuzzy state observer is designed which removes the restriction of the Hurwitz equation for the gain parameters. Based on reinforcement learning, the observer and error derivative cost function, an improved optimized backstepping control method is given. Using observed information and actor-critic structure to train fuzzy logic systems online, the control inputs are obtained to achieve approximate optimal control. Finally, all closed-loop signals are proved to be bounded by the Lyapunov method, and the effectiveness and advantages of the proposed algorithm are verified through two examples.

Impedance Force Control of Manipulator Based on Variable Universe Fuzzy Control

Impedance control is a classic and straightforward control method that finds wide applications in various fields. However, traditional constant impedance control requires prior knowledge of the environment’s stiffness and position information. If the environmental information is unknown, constant impedance control is not capable of handling the task. To address this, this paper proposes a variable universe fuzzy model reference adaptive impedance control method that achieves effective force tracking even in the presence of unknown environmental information. A variable universe fuzzy controller was employed to determine the impedance parameters. The force tracking error and its rate of change were used as two input parameters for the variable universe fuzzy controller, which utilizes fuzzy inference to obtain the incremental values of the impedance parameters. For the introduced model reference controller, a novel adaptive law was employed to obtain the coefficients for contact force and torque. Subsequently, the contact force of the manipulator in Cartesian space was taken as the research object, and a simulation model of a six-joint manipulator was established in MATLAB/Simulink. By comparing it with the constant impedance control method, the feasibility and effectiveness of this control approach were validated.