Adaptive fuzzy tracking control for a class of pure-feedback stochastic nonlinear systems with non-lower triangular structure

This paper focuses on the problem of approximation-based adaptive fuzzy tracking control for a class of stochastic nonlinear time-delay systems with a nonstrict-feedback structure. A variable separation approach is introduced to overcome the design difficulty from the nonstrict-feedback structure. Mamdani-type fuzzy logic systems are utilized to model the unknown nonlinear functions in the process of controller design, and an adaptive fuzzy tracking controller is systematically designed by using a backstepping technique. It is shown that the proposed controller guarantees that all signals in the closed-loop system are fourth-moment semiglobally uniformly ultimately bounded, and the tracking error eventually converges to a small neighborhood of the origin in the sense of mean quartic value. Simulation results are provided to demonstrate the effectiveness of our results. Further developments will consider how to generalize the proposed strategy to nonstrict-feedback nonlinear systems with input nonlinearities.

Adaptive fuzzy tracking control for a class of uncertain nonaffine nonlinear systems with dead-zone inputs

This paper researches the problem of adaptive fuzzy tracking control for stochastic nonlinear multi-agent systems subject to asymmetric hysteretic quantizer. We apply the fuzzy logic system to estimate the unknown nonlinear functions and stochastic disturbance term. Based on the sector bound property, we introduce a nonlinearity decomposition method of asymmetric hysteresis quantizer. According to the backstepping technique, a new adaptive fuzzy control protocol and adaptive law are developed. By using stochastic Lyapunov stability theory, it is testified that all the followers’ outputs converge to the neighborhood of the leader output. The simulation example is proposed to verify the availability of the control strategy.

Adaptive tracking control for a class of switched uncertain nonlinear systems under a new state-dependent switching law

SummaryThis paper studies the problem of adaptive fuzzy asymptotic tracking control for multiple input multiple output nonlinear systems in nonstrict‐feedback form. Full state constraints, input quantization, and unknown control direction are simultaneously considered in the systems. By using the fuzzy logic systems, the unknown nonlinear functions are identified. A modified partition of variables is introduced to handle the difficulty caused by nonstrict‐feedback structure. In each step of the backstepping design, the symmetric barrier Lyapunov functions are designed to avoid the breach of the state constraints, and the issues of overparametrization and unknown control direction are settled via introducing two compensation functions and the property of Nussbaum function, respectively. Furthermore, an adaptive fuzzy asymptotic tracking control strategy is raised. Based on Lyapunov stability analysis, the developed control strategy can effectually ensure that all the system variables are bounded, and the tracking errors asymptotically converge to zero. Eventually, simulation results are supplied to verify the feasibility of the proposed scheme.

This paper presents a theoretical study on a model-reference adaptive fuzzy logic controller for vector-controlled permanent magnet AC servomotor drives. In the proposed system, fuzzy logic is used to implement the direct controller as well as the adaptation mechanism. The operation of the direct fuzzy controller and the fuzzy logic based adaptation mechanism is studied. The control performance of the adaptive fuzzy controller is evaluated by simulation for various operating conditions. The results are compared with that provided by a nonadaptive fuzzy controller. The implementation of proposed adaptive fuzzy controller is also discussed.

Adaptive fuzzy asymptotic tracking control of uncertain nonaffine nonlinear systems with non-symmetric dead-zone nonlinearities

In this paper, temperature monitoring of sterilizing equipment system was established with the help of fuzzy and self tuning Adaptive fuzzy logic controller designed in Lab VIEW software. It combines the advantages of both fuzzy logic and self tuning Adaptive fuzzy logic controller. The implementation attempts to rectify the errors between the measured value and the set point which helps to achieve efficient temperature control. The Adaptive fuzzy controller uses defined rules to control the system based on the current values of input variables and temperature errors. The simulation results presented in order to evaluate the proposed method. The result shows that self tuning Adaptive fuzzy logic controller was tolerant to disturbance and the temperature control is most accurate.

In this paper, a T–S (Takagi–Sugeno) adaptive tracking algorithm control based on small gain theorem is proposed for an uncertain robot system with $$n$$-link manipulators. A nonzero time-varying parameter is introduced in the common T–S fuzzy logic system, the T–S type fuzzy logic system with updated parameters laws is build, then the new and original universal approximation with parameter is introduced. The approximation accuracy can be updated on-line by the parameters, which is not limited by the number of fuzzy rules. With the novel property of universal approximation, the proposed adaptive control can be synthetized to overcome the limitations such as on-line learning computation burden in conventional fuzzy logic systems. The originality T–S fuzzy logic system is used to compensate the unknown model of robot manipulators and the adaptive tracking control algorithm is designed with the new property of universal approximation. Based on the analysis of small gain theorem and ISS theory (input-to-state stability), all signals in closed-loop system can be guaranteed to be bounded, and the system can be extended from semi-global stability to global stability by employing the proposed adaptive control scheme. Finally, simulation results are shown to demonstrate the effectiveness of the adaptive control scheme.

In this paper, the problem of adaptive fuzzy output-feedback control is investigated for a class of uncertain switched nonlinear systems in strict-feedback form. The considered switched systems contain unknown nonlinearities, dead-zone, and immeasurable states. Fuzzy logic systems are utilized to approximate the unknown nonlinear functions, a switched fuzzy state observer is designed and thus the immeasurable states are obtained by it. By applying the adaptive backstepping design principle and the average dwell time method, an adaptive fuzzy output-feedback tracking control approach is developed. It is proved that the proposed control approach can guarantee that all the variables in the closed-loop system are bounded under a class of switching signals with average dwell time, and also that the system output can track a given reference signal as closely as possible. The simulation results are given to check the effectiveness of the proposed approach.

Adaptive fuzzy control for full states constrained systems with nonstrict-feedback form and unknown nonlinear dead zone

This paper investigates an adaptive fuzzy tracking control problem for nonlinear stochastic nonstrict-feedback systems with unknown control directions and actuator faults. Based on the monotonically increasing property of system bounding functions, a variable separation scheme is invoked to overcome the research difficulty from nonlower triangular structure. Combining adaptive backstepping method with fuzzy logic system's approximation capability, an adaptive fuzzy fault tolerant controller is designed. The presented control algorithm makes not only the tracking error as small as possible but also the closed-loop trajectory bounded in the sense of the four-moment. Finally, simulation results are depicted to address the effectiveness of the studied control method.

In this paper, an event-based adaptive fuzzy tracking control strategy for a class of uncertain nonlinear systems with limited control input and its rate of change is proposed. The unknown nonlinear function of the considered system is approximated by a fuzzy logic system (FLS). Furthermore, an event-triggered control method is developed to save the computing resources, in which the controller parameters are adaptively adjusted by a fuzzy controller. Moreover, it is proved by designing the Lyapunov function that the event-based controller can ensure that all signals of the controlled system are bounded. In addition, the tracking error can converge to a preset domain. Meanwhile, two practical simulations are given to demonstrate the effectiveness of the proposed method.

In this paper, an adaptive fuzzy output feedback control approach is investigated for a class of stochastic nonlinear strict-feedback systems without the requirement of states measurement. The stochastic nonlinear system addressed in this paper is assumed to possess unstructured uncertainties (unknown nonlinear functions) and, in the presence of unmodeled dynamics, dynamics disturbances. Fuzzy logic systems are used to approximate the unstructured uncertainties, and a fuzzy state observer is designed to estimate the unmeasured states. By combining the backstepping design technique with the stochastic small-gain approach, a new adaptive fuzzy output feedback control approach is developed. It is proved that the proposed control approach can guarantee that the closed-loop system is input-state-practically stability (ISpS) in probability, and the observer errors and the output of the system converge to a small neighborhood of the origin by appropriate choice of the design parameters. Simulation results are included to indicate that the proposed adaptive fuzzy control approach has a satisfactory control performance. In addition, the simulation comparisons with the previous methods show that the proposed adaptive fuzzy control approach has robustness to the dynamical uncertainties.

Adaptive fuzzy finite-time tracking control of nonlinear systems with unmodeled dynamics