Adaptive Fuzzy Tracking Control Research Articles

Application of an Alternating Current Motor System Based on Fuzzy Adaptive Tracking Control for Nonlinear Systems

Application of an Alternating Current Motor System Based on Fuzzy Adaptive Tracking Control for Nonlinear Systems

Second-order DSC-based adaptive fuzzy tracking control for uncertain unmanned helicopter with fixed-time stability

Second-order DSC-based adaptive fuzzy tracking control for uncertain unmanned helicopter with fixed-time stability

Predefined time fuzzy adaptive tracking control for nonlinear cyber physical systems with unmeasurable states and FDI attacks

Predefined time fuzzy adaptive tracking control for nonlinear cyber physical systems with unmeasurable states and FDI attacks

Adaptive fuzzy fixed-time control for uncertain switched nonlinear systems with non-symmetrical dead-zone

This paper addresses fuzzy adaptive fixed-time tracking control problem for uncertain switched nonlinear systems with non-symmetrical dead-zone, even if the tracking control problem for subsystems is not solvable. A fixed-time stability criterion for switched nonlinear systems using MLF(multiple Lyapunov functions) method is presented. Then, it is applied to fixed-time tracking control of uncertain strict-feedback switched nonlinear systems. The design methods of fuzzy adaptive feedback controllers and a state-dependent switching law are given to achieve the boundedness of all the signals of the resulting closed-loop system and the fixed-time output tracking control. Nonsymmetrical dead-zone model is used to estimate an adaptive factor through less parameters, which related to fixed-time tracking control for the uncertain switched nonlinear systems. Two examples are given to verify the effectiveness of the proposed design method.

Adaptive Fuzzy Tracking Control and Its Application in Stochastic Biological Systems

Adaptive Fuzzy Tracking Control and Its Application in Stochastic Biological Systems

Adaptive Predefined-Time Fuzzy Tracking Control for Output Constrained Non-strict Feedback Nonlinear Systems with Input Saturation

Adaptive Predefined-Time Fuzzy Tracking Control for Output Constrained Non-strict Feedback Nonlinear Systems with Input Saturation

Adaptive Fuzzy Tracking Control With Global Prescribed-Time Prescribed Performance for Uncertain Strict-Feedback Nonlinear Systems.

For strict-feedback systems with mismatched uncertainties, adaptive fuzzy control techniques are developed to provide global prescribed performance with prescribed-time convergence. First, a class of prescribed-time prescribed performance functions are designed to quantify the performance constraints of the tracking error. Additionally, a novel error transformation function is provided to eliminate the initial value limitations and resolve the singularity issue in previous research. To ensure the convergence of the tracking error into a prescribed bounded region within a prescribed time and satisfactory transient performance, controllers with or without approximating structures are established. Notably, the settling time and initial condition of the prescribed performance function are completely independent of the initial tracking error and system parameters, thereby improving upon existing results. Furthermore, the disadvantage of the semi-global boundedness of tracking error induced by dynamic surface control can be eliminated through the use of a novel Lyapunov-like energy function. Finally, the effectiveness of the proposed strategies is validated through numerical simulations performed on practical examples.

A novel fixed-time fuzzy adaptive tracking control for nonlinear high-order systems with state constraints

A fixed time fuzzy adaptive tracking controller is presented for high-order nonlinear systems, which includes unknown nonlinear functions and satisfies full state constraints. This is a constructive control scheme based on integrator barrier Lyapunov function, unique dominant adaptive parameter related to fuzzy approximation, and complex high-order back-stepping technique, which ensures that the tracking error can be confined to a preassigned neighbourhood including the origin within a fixed time independent of initial values, and all the states remain the given constraints. A practical application to single joint manipulator tracking control is provided, where comparative simulation results are illustrated for two different preassigned tracking errors. Another numerical example is offered to further manifest the feasibility of the theoretical results and the effectiveness of the controller in this paper.

Optimization of Predefined-Time Agent-Scheduling Strategy Based on PPO

In this paper, we introduce an agent rescue scheduling approach grounded in proximal policy optimization, coupled with a singularity-free predefined-time control strategy. The primary objective of this methodology is to bolster the efficiency and precision of rescue missions. Firstly, we have designed an evaluation function closely related to the average flying distance of agents, which provides a quantitative benchmark for assessing different scheduling schemes and assists in optimizing the allocation of rescue resources. Secondly, we have developed a scheduling strategy optimization method using the Proximal Policy Optimization (PPO) algorithm. This method can automatically learn and adjust scheduling strategies to adapt to complex rescue environments and varying task demands. The evaluation function provides crucial feedback signals for the PPO algorithm, ensuring that the algorithm can precisely adjust the scheduling strategies to achieve optimal results. Thirdly, aiming to attain stability and precision in agent navigation to designated positions, we formulate a singularity-free predefined-time fuzzy adaptive tracking control strategy. This approach dynamically modulates control parameters in reaction to external disturbances and uncertainties, thus ensuring the precise arrival of agents at their destinations within the predefined time. Finally, to substantiate the validity of our proposed approach, we crafted a simulation environment in Python 3.7, engaging in a comparative analysis between the PPO and the other optimization method, Deep Q-network (DQN), utilizing the variation in reward values as the benchmark for evaluation.

Adaptive fuzzy tracking control for multiple‐input multiple‐output nonlinear systems with sensor faults and experiment in the flexible‐joint robot

AbstractThis paper is centered on the adaptive asymptotic tracking control of multi‐input multi‐output uncertain strict‐feedback nonlinear systems in the presence of both multiplicative and additive sensor faults. Through adaptive accommodation techniques, the proposed control scheme can dynamically compensate for sensor faults, thereby ensuring precise and stable control. The control scheme is devised using the backstepping design that incorporates positive integrable time‐varying functions, thereby ensuring the boundedness of all signals in the closed‐loop system and guaranteeing that the output tracking error asymptotically converges to zero. Additionally, extensive simulations and experimental evaluations conducted on the Quanser platform with 2‐link flexible‐joint robots demonstrate the efficacy of the proposed control scheme in mitigating the impact of sensor faults and achieving accurate tracking performance.

Finite-Time Prescribed Performance-Based Adaptive Fuzzy Tracking Control for Switched Nonlinear Systems with Output Dead Zone

Finite-Time Prescribed Performance-Based Adaptive Fuzzy Tracking Control for Switched Nonlinear Systems with Output Dead Zone

Adaptive Fuzzy Fixed-Time Control for Nonlinear Systems with Unmodeled Dynamics

This article concentrates on the problem of fixed-time tracking control for a certain class of nonlinear systems with unmodeled dynamics. Unmodeled dynamics are prevalent in practical engineering systems, such as axially symmetric systems like robotic arms, spacecraft, and missiles. In this paper, the fuzzy-logic systems (FLSs) are implemented to address the challenge of accurately approximating the unknown nonlinear terms that arise during the derived control algorithm process. By employing fixed-time command filters (FTCF), the “explosion of complexity” issues encountered in traditional backstepping methods will be effectively resolved. Moreover, error compensation mechanisms are derived to effectively mitigate the filtering errors that may arise from the FTCFs. The computational burden associated with FLSs is reduced through the utilization of the weight vector estimation method based on the maximal norm and an adaptive approach. A fixed-time adaptive fuzzy tracking controller is developed within the backstepping control framework to ensure the boundedness of all signals and achieve fixed-time convergence of the tracking error for the controlled system. Illustrative examples are conducted to illustrate the viability of the derived controller.

Synchronous MDADT-Based Fuzzy Adaptive Tracking Control for Switched Multiagent Systems via Modified Self-Triggered Mechanism

Synchronous MDADT-Based Fuzzy Adaptive Tracking Control for Switched Multiagent Systems via Modified Self-Triggered Mechanism

Adaptive Fuzzy Tracking Control for Stochastic Nonlinear Systems with Full-State Constraints

Adaptive Fuzzy Tracking Control for Stochastic Nonlinear Systems with Full-State Constraints

Fixed-Time Adaptive Fuzzy Tracking Control for High-Order Uncertain Nonlinear Cyber–Physical Systems Under Malicious Attacks

Fixed-Time Adaptive Fuzzy Tracking Control for High-Order Uncertain Nonlinear Cyber–Physical Systems Under Malicious Attacks

Observer-Based Adaptive Fuzzy Tracking Control for MIMO Nonlinear Systems with Asymmetric Time-Varying Constraints

Observer-Based Adaptive Fuzzy Tracking Control for MIMO Nonlinear Systems with Asymmetric Time-Varying Constraints

Adaptive fuzzy tracking control for a class of time-varying output constrained nonlinear systems with non-affine nonlinear faults

This paper investigates a tracking control issue for a class of time-varying output constrained nonlinear systems subject to non-affine nonlinear faults and virtual control coefficients. In the controller design process, the nonlinear function is approximated by fuzzy logic systems. Utilizing a feasible function to convert the system, a new transformation strategy is proposed to handle the system with time-varying output constraints or without output constraints. The mean value theorem is applied to split non-affine faults, and the Nussbaum-type function is used to eliminate the effect of the unknown directional affine variable gain of input resulted from non-affine faults. Combined with adaptive fuzzy backstepping technology and the error transformation function, a new control strategy is presented, which not only deals with the time-varying output constraint but also handles non-affine faults, effectively. Finally, all closed-loop system signals are bounded, and the tracking error can converge to a small preset set. Two simulations are performed to confirm the validity of the presented strategy.

Backstepping-based adaptive fuzzy tracking control for pure-feedback nonlinear multi-agent systems

This work developed an adaptive fuzzy tracking control strategy for pure-feedback nonlinear multi-agent systems via backstepping. The relevant unknown functions are addressed by fuzzy logic systems (FLSs), which take into account entirely unknown nonlinearities. The non-affine pure-feedback form is dealt with a Butterworth low-pass filter (BLF), and the converted systems can be used to construct a controller. The implementation of the first-order sliding-mode differentiator overcomes the difficulty of calculation explosion. The backstepping method is applied to design the virtual controller step by step, and the actual controller is designed successfully. Furthermore, through Lyapunov stability theory, the stability of systems is ensured, and that the tracking error is kept to a minimum. Finally, the simulation result shows the validity of the designed control strategy.

Command filtered adaptive fuzzy tracking control for uncertain stochastic nonlinear systems with event-triggered input

Command filtered adaptive fuzzy tracking control for uncertain stochastic nonlinear systems with event-triggered input

Cooperative Time-Varying Formation Fuzzy Tracking Control of Multiple Heterogeneous Uncertain Marine Surface Vehicles With Actuator Failures.

This article addresses the cooperative time-varying formation fuzzy tracking control problem for a cluster of heterogeneous multiple marine surface vehicles subject to unknown nonlinearity and actuator failures. The proposed cooperative control scheme consists of two parts: 1) a distributed time-varying formation observer and 2) a decentralized adaptive fuzzy tracking controller. The distributed observer is designed to obtain a predefined time-varying formation pattern under a directed communication topology. Subsequently, based on the states of the distributed observer, a decentralized fuzzy tracking control law is developed using fuzzy-logic systems and the adaptive approach. Lyapunov functions are constructed to guarantee that the controlled marine vehicles attain the desired time-varying formation with asymptotical stability of tracking errors. Finally, simulation results are presented to validate the efficacy of the proposed control methodology.