Adaptive Fuzzy Controller Design Research Articles

Design and implementation of a robust fuzzy adaptive PI controller for inherently unstable systems

Design and implementation of a robust fuzzy adaptive PI controller for inherently unstable systems

Fuzzy approximation-based reparametrization and adaptive control design of nonlinear aircraft dynamics

This paper conducts a new study on fuzzy approximation-based reparametrization and adaptive control design of non-canonical nonlinear aircraft dynamics, with particular focus on emphasizing the expansion of linearization-based adaptive flight control designs from local to semi-global scopes. The linearization-based adaptive control method has been employed for managing non-canonical nonlinear aircraft systems, owing to its capability in handling the complexity of aircraft system dynamics. To enhance the operational range of the linearization-based approach, this paper focuses on the aircraft longitudinal dynamic model in general non-canonical forms. A semi-global linearization-based adaptive control method is developed for this kind of system. Firstly, a new reparametrization is devised, leveraging local linearized models and a concept of relative degree. Subsequently, adaptive controllers are designed to guarantee stable and asymptotic output tracking for aircraft systems with various relative degrees. The efficiency of the newly developed adaptive control approach is validated through simulation outcomes.

Adaptive fuzzy controller design for an air handling unit

An adaptive control method based on a fuzzy state observer is designed for air handling unit (AHU) systems, where the intensity of humidity source and heat load are considered to be uncertain, and approximated by fuzzy logics. Besides, the supply air temperature is unmeasurable, which is observed by a state observer. To avoid the system equilibrium point offset, the prediction errors generated by the system and serial-parallel estimation models are introduced into the controller design based on backstepping. Finally, the superiority of proposed adaptive fuzzy controller is analyzed by simulation comparison with feedback linearization and PID controllers in two cases.

Model reference adaptive controller with interpretable fuzzy rules for linear MIMO systems

This paper presents a method for fuzzy model reference adaptive control system design for a partially known multi-input multi-output (MIMO) linear dynamic plant. Differential or convolutional equations can describe the plant to be controlled, and the fuzzy controller is assumed to be from the MIMO sector-bounded nonstationary nonlinearities class. The task is to obtain robust adaptive stabilization. Some absolute stability theorems and integral evaluations of the state and control vectors are applied in the MIMO fuzzy adaptive controller design procedure for the first time. A method for automatically obtaining a near-optimal nonlinear dynamic reference model is described. A practical step-by-step procedure of the MIMO adaptive fuzzy controller design is proposed, resulting in highly interpretable MIMO fuzzy control rules based on triangular fuzzy sets and strong triangular fuzzy partition. An example of the fuzzy controller design according to the proposed procedure is given.

Fixed-time adaptive fuzzy control with prescribed tracking performances for flexible-joint manipulators

In this paper, we study the tracking control problem of flexible-joint manipulators with uncertain unstructured dynamics, and develop a prescribed fixed-time solution scheme by combining direct adaptive fuzzy control design with a class of smooth functions. With our scheme, only one adaptive law needs to be calculated online, and also there are no complicated terms, e.g., the partial derivatives of some multivariable functions, involved in virtual controllers and the actual control input, which show that the raised scheme is computationally attractive. Moreover, the boundedness of all closed-loop signals, and the fixed-time convergence of tracking error to a prescribed interval can be established through a rigorous proof, and the obtained results are further confirmed by simulation tests.

Fuzzy wavelet neural adaptive finite-time self-triggered fault-tolerant control for a quadrotor unmanned aerial vehicle with scheduled performance

This paper focuses on fuzzy wavelet neural networks-based adaptive finite-time self-triggered fault-tolerant control design with assured tracking performance for a quadrotor unmanned aerial vehicle subject to unknown actuator faults. First, an improved finite-time performance function is integrated with the command-filtered backstepping control framework to assure the scheduled tracking performance, which can effectively constrain the transient fluctuations of the tracking error at fault occurrence. Then, two compensation mechanisms are developed to weaken the adverse impact induced by actuator faults and filter errors. Further, a fuzzy wavelet neural adaptive self-triggered fault-tolerant controller with guaranteed performance is designed to improve the fault insensitivity and tracking accuracy of the controlled vehicle, where the fuzzy wavelet neural networks are employed to approximate the unknown nonlinearities and the control signals are allowed to achieve irregular updating only when the pre-specified trigger protocol is violated. Stability analysis proves that the designed controller guarantees the attitude and position subsystems are practical finite-time stable, and the tracking errors are strictly confined to a preassigned region and never cross its allowed bounds. Finally, the validity of the developed control scheme is confirmed by the simulation results.

Adaptive Fuzzy Nonsingular Fixed-Time Control for a Class of MIMO Nonstrict Feedback System

This paper studies the adaptive fuzzy practical fixed-time control design for MIMO nonlinear systems with non-strict feedback. The extant practical fixed-time stability criterion cannot obtain an accurate settling time upper bound or exists some constraints on parameters that lead to a conservative result. Thus a novel practical fixed-time stability criterion is proved in this paper to resolve the above problem and can obtain the precise value of the settling time upper bound. The control singular issue caused by the fixed-time stability criterion form is avoided by introducing adding power integration method. Compared with other approaches, this method is easy to apply in control design, and it's convenient to prove its effectiveness. And fuzzy logic system is adopted to approximate the unknown nonlinear items. Combining the proposed fixed-time stability criterion, traditional backstepping scheme, and fuzzy logic system, an adaptive practical fixed-time controller is proposed. The simulation of the actual physical system demonstrates the effectiveness of the controller and practical fixed-time criterion.

Finite-Time Adaptive Fuzzy Event-Triggered Output-Feedback Containment Control for Nonlinear Multiagent Systems With Input Saturation

This paper considers the finite-time containment output-feedback control problem for a class of nonlinear multi-agent systems (MASs) with unmeasurable states and input saturation. Fuzzy logic systems (FLSs) and a smooth function are first employed to model unknown agent's subsystems and input saturation, respectively. Then, a novel fuzzy state observer is established via the intermittent output signal. By introducing first-order filter and using the sampled estimating states and triggered output signals, an event-triggered mechanism consisting of the sensor-to-controller and controller-to-actuator is formulated. Consequently, under the framework of the finite-time stability criterion and adaptive backstepping control design technique, a finite-time adaptive fuzzy event-triggered output-feedback containment control design method is proposed and the semiglobal finite-time stability of the controlled system is rigorously proved. Finally, the simulation results are given to confirm the effectiveness of the proposed control scheme.

Adaptive fuzzy control design for nonlinear systems with actuation and state constraints: An approach with no feasibility condition

In this article, an adaptive fuzzy tracking control scheme is developed for a class of pure-feedback uncertain nonlinear systems in the presence of time-varying full-state constraints (TFSCs), actuators’ nonlinearities and external disturbances. Fuzzy logic systems (FLSs) are employed as universal approximators to online estimate unknown nonlinear functions A barrier Lyapunov function (BLF) is used to deal with the state constraint problem. In contrast to numerous adjacent studies, this research diligently tackles the open problem relating to the virtual control laws (VCLs) feasibility in the BLF-based backstepping control design. The resolution to this problem involves formulating VCLs with predefined bounds. The utilization of disturbance observers within a backstepping framework allows for effective compensation of estimation errors arising from the implementation of a predefined bounded VCL. This approach also helps prevent the occurrence of the ”complexity explosion”, making it a practical solution. The control strategy being proposed guarantees that the output tracking error will effectively approach a small region near the origin. Additionally, all signals of the closed-loop system will remain uniformly ultimately bounded (UUB), and there will be adherence to all state-constraints, ensuring no violations occur. Ultimately, an illustrative simulation example is provided to demonstrate the efficacy of the theoretical findings.

Fuzzy adaptive PID speed controller design for modern elevator traction machine

With the development of the times, elevator has become an essential transportation in our life. In order to meet the comfort of elevator operation, this paper investigates the design of the speed control system of elevator permanent magnet synchronous traction motor in combination with the elevator operation curve. The whole system is based on SVPWM and uses direct torque control combined with fuzzy adaptive PID to achieve accurate control of motor running speed. It includes the input signal module, control module, drive module and feedback module. Firstly, the analysis of each module in the system is completed, and the connection between the modules is established, as well as the control system. Then case studies are carried out on MATLAB/SIMULINK, and the actual running speed curve of the elevator is obtained, and the simulation results are analyzed to verify the stability and reliability of this speed control system and complete the design.

Adaptive fuzzy control of underwater vehicle manipulator system with dead-zone band input nonlinearities via fuzzy performance and disturbance observers

In this paper, an adaptive fuzzy control design problem is investigated for an underwater vehicle manipulator system (UVMS) based on a fuzzy performance observer (FPOB) and fuzzy disturbance observer (FDO). The UVMS is considered as a nonlinear system comprising model uncertainties, external disturbances and dead-zone band input nonlinearities. To mitigate the combined effects of the dead-zone and the hysteresis, a novel pre-deadzone compensator is proposed. Then, fuzzy logic systems (FLSs) with online adaptations are utilized to evaluate the unknown components of the inertia matrix, the Coriolis matrix and the damping matrix. In addition, a fuzzy performance observer is constructed whose errors are used to estimate the external disturbances. Further, an H∞ fuzzy control technique is developed to reduce the errors in estimating the external disturbance. Then, the stability and tracking performance of the closed-loop system are analyzed using the Lyapunov stability theory. It is shown that all signals of the closed-loop system are uniformly ultimately bounded. Finally, simulations are performed to demonstrate the effectiveness of the proposed control scheme in addressing the tracking control problem of the UVMS in presence of the dead-zone band and disturbances.

Design of robust adaptive fuzzy control for uncertain bilateral teleoperation systems based on backstepping approach

AbstractIn this study, a novel method based on a robust adaptive fuzzy control approach is developed for nonlinear teleoperation systems. Its main objectives are to ensure system stability and properly mitigating parametric uncertainties stemming from external disturbances and un‐modelled dynamics. For the communication channel, instead of the direct transmission of environmental torque signals, the approximated environmental parameters by the fuzzy system are transmitted to the master side for the prediction of environmental torque, thus successfully avoiding the transmission of the power signals in the delayed communication channel and solving the passivity problem in the teleoperation system. Besides, a trajectory generator is employed in the master side, whereas a trajectory smoothing is provided in the slave side. Theoretically, it was proven that both position tracking and force feedback problems are attained. Using Lyapunov stability analysis, this work illustrates that the robust adaptive fuzzy controller based on the backstepping approach guarantees the system's asymptotic stability. Simulation results confirm the efficiency of the suggested control technique in achieving the stability and tracking objectives of the uncertain nonlinear teleoperation system.

Event-triggered adaptive fuzzy asymptotic control design for uncertain stochastic nonlinear systems

The adaptive fuzzy asymptotic tracking control (AFATC) method is presented for uncertain stochastic nonlinear systems by virtue of event-triggered mechanism. The fuzzy logic systems are borrowed to address system uncertainties. By applying the backstepping technique, the AFATC strategy is recursively constructed. Different from the existing approximation-based methods, the developed controller can achieve asymptotic convergence feature. Moreover, the event-triggered mechanism is integrated into the controller design process to reduce data transmission and unnecessary resource waste. By applying the Lyapunov analysis and several helpful lemmas, the stability and convergence of the considered systems are ensured. Finally, the availability of the theory analysis is verified by a chemical reactor system.

Fixed-time adaptive fuzzy SOSM controller design with output constraint

Fixed-time adaptive fuzzy SOSM controller design with output constraint

Nonlinear Adaptive Fuzzy Control Design for Wheeled Mobile Robots with Using the Skew Symmetrical Property

This research presents a nonlinear adaptive fuzzy control method as an analytical design and a simple control structure for the trajectory tracking problem in wheeled mobile robots with skew symmetrical property. For this trajectory tracking problem in wheeled mobile robots, it is not easy to find an analytical adaptive fuzzy control solution due to the complicated error dynamics between the controlled wheeled mobile robots and desired trajectories. For deriving the analytical adaptive fuzzy control law of this trajectory tracking problem, a filter link is firstly adopted to find the solvable error dynamics, then the research is based on the skew symmetrical property of the transformed error dynamics. This proposed nonlinear adaptive fuzzy control solution has the advantages of low computational resource consumption and elimination of modeling uncertainties. From the results for tracking two simulation scenarios (an S type trajectory and a square trajectory), the proposed nonlinear adaptive fuzzy control method demonstrates a satisfactory trajectory tracking performance for the trajectory tracking problem in wheeled mobile robots with huge modeling uncertainties and outperforms the existing H2 control method.

Fuzzy Control of Nonlinear Strict-Feedback Systems With Full-State Constraints: A New Barrier Function Approach

This article is concerned with adaptive fuzzy control design of nonlinear strict-feedback systems, in which system functions are unknown and system states are subjected to some constant constraints. The main control issue is to design an adaptive fuzzy controller such that the system output follows the reference signal, meanwhile, all the state variables abide by their constrained requirements. Differing from the existing way to build barrier function, the constructed virtual control signal is employed to build the barrier function. Furthermore, a backstepping-based adaptive fuzzy control design process is presented in this article. Compared to the control schemes in the existing literature on full-state constrained systems, the current control scheme has the following advantages: 1) the built virtual control signals in this article are ensured to satisfy corresponding state constraints, rather than assuming that they do, as in the existing articles; 2) the choice of initial values is independent of the norm of the virtual control signal. So, the conservatism of initial value selection is considerably reduced. It is shown that the proposed adaptive fuzzy controller not only ensures perfect tracking performance, but also ensures that all the states abide by the preassigned state constraints during the operation. At last, simulation study further verifies the efficacy of the proposed control strategy.

Adaptive fuzzy asymptotic control design for MIMO nonlinear systems with state constraints

This paper addresses the asymptotic control problem of uncertain multi-input and multi-output (MIMO) nonlinear systems. The considered MIMO systems contain unknown virtual control coefficients (UVCCs) and state constraints. A creative Lyapunov function by associating with the lower bounds of UVCCs is presented to counteract the adverse effect deriving from UVCCs. The state constraints are ensured by utilising the barrier Lyapunov function. Moreover, the asymptotic tracking controller is recursively constructed by combining the backstepping technique with fuzzy logic systems. The remarkable character of the designed controller is that the asymptotic tracking performance can be achieved by introducing some smooth functions into adaptive backstepping procedure. In contrast to the existing results, the conditions on the UVCCs are relaxed. Finally, the new control design is illustrated by a practical example.

Disturbance observer based adaptive fuzzy synchronization controller design for uncertain fractional-order chaotic systems

This study premeditated the synchronization of two fractional-order chaotic systems (FOCSs) with uncertainties and external disturbances. We utilized fuzzy logic systems (FLSs) to estimate unknown nonlinearities, and implemented disturbance observers to estimate unknown bounded external disturbances. Then, a robust control term was devised to compensate for the unavoidable approximation error of the fuzzy system. In addition, a sliding mode surface was devised to construct an adaptive fuzzy sliding mode controller (AFSMC) that can guarantee that the synchronization error converges to a small neighborhood of zero. Finally, the validity of the proposed control strategy was verified via a numerical simulation.

Adaptive Fuzzy Event-Triggered Tracking Control for Nonstrict Nonlinear Systems

This article addresses adaptive fuzzy event-triggered controller design for nonstrict nonlinear systems. First, for estimating the unmeasurable states, the high-gain fuzzy state observer is designed. By using backstepping technique, the adaptive fuzzy controller is designed. A new switching threshold event-triggered mechanism is given to decide when the controller needs to be updated and the Zeno behavior is avoided. Stability analysis shows that the tracking error can be arbitrarily small and all variables of the closed-loop system remain bounded. At last, the effectiveness of our control strategy is illustrated through simulation.

Prescribed Performance Fuzzy Adaptive Output Feedback Control for Nonlinear MIMO Systems in a Finite Time

This article studies the fuzzy adaptive output feedback control design problem for nonstrict feedback multi-input–multi-output nonlinear systems with full-states prescribed performance in finite time. Fuzzy logic systems are introduced to solve the problem of unknown nonlinear dynamics. And on this basis, a fuzzy-based state observer is designed to observe the unmeasurable state. Further, by combining the adaptive back-stepping control algorithm and the nonlinear filters, a novel dynamic surface control (DSC) method is proposed, which not only solves the computational complexity explosion problem inherent in the back-stepping control algorithm, but also improves the control performance, in contrast to the traditional DSC methods with linear filters. Besides, to further improve the tracking performance under the structure of full-states prescribed performance, a new Lyapunov function is designed considering the transform error constraint. Based on the Lyapunov theory, the stability of the closed-loop system is analyzed to ensure that all signals of the closed-loop system are semiglobal practical finite-time stability. Finally, to elaborate on the feasibility and effectiveness of the proposed control method, a simulation example is provided.