Observer-based Adaptive Fuzzy Force Control for the Pneumatic Polishing System End-actuator with Uncertain Dynamic Contact Model

In this article, an observer-based fuzzy adaptive inverse optimal output feedback control problem is studied for a class of nonlinear systems in strict-feedback form. The considered nonlinear systems contain unknown nonlinear dynamics and their states are not measured directly. Fuzzy logic systems are applied to identify the unknown nonlinear dynamics and an auxiliary nonlinear system is constructed. Based on this auxiliary system, a fuzzy state observer is first designed to estimate the immeasurable states. By using the inverse optimal principle and adaptive backstepping design theory, an observer-based fuzzy adaptive inverse optimal output feedback control scheme is then developed. The proposed inverse optimal control scheme need not assume that the states are measurable. It also guarantees that the closed-loop system is semiglobally uniformly ultimately bounded, and achieves the optimal control objective as well. Finally, two simulation examples are provided to check the validity of the presented control method.

For a class of nonlinear systems with uncertainty, an observer-based robust direct adaptive fuzzy control scheme was proposed. In the case that the states of the system are not available· an observer is designed and an observer-based robust adaptive fuzzy control scheme that the generalized fuzzy hyperbolic model (GFHM) is employed to structure a fuzzy controller is developed. The overall control scheme can guarantee that the tracking error converges in the small neighborhood of origin, and all signals of the closed-loop system are uniformly bounded. A simulation example was proposed to demonstrate the effective of the proposed control algorithm.

In this paper, the algorithm design problem of intelligent ship autopilot with an unmeasured yaw rate is investigated based on adaptive fuzzy control. The fuzzy logic system (FLS) is employed to approximate the unknown function, and a fuzzy state observer is designed to estimate the unmeasured state. To achieve the specified performance constraints, the error transformed method is introduced. Based on adaptive backstepping method, an observer-based adaptive fuzzy output-feedback control scheme is developed. The proposed control scheme can reduce the conservativeness and the complexity of controller. It is proved that all the signals in the closed-loop system are bounded via Lyapunov theory. The efficacy of theoretical results is demonstrated by the simulations.

This article investigates the adaptive fuzzy output-feedback backstepping control design problem for uncertain strict-feedback nonlinear systems in the presence of unknown virtual and actual control gain functions and unmeasurable states. A fuzzy state observer is designed via fuzzy-logic systems, thus the unmeasurable states are estimated based on the designed fuzzy state observer. By constructing the logarithm Lyapunov functions and incorporating the property of the fuzzy basis functions and bounded control design technique into the adaptive backstepping recursive design, a novel observer-based adaptive fuzzy output-feedback control method is developed. The proposed fuzzy adaptive output-feedback backstepping control scheme can remove the restrictive assumptions in the previous literature that the virtual control gains and actual control gain functions must be constants. Furthermore, it can make the control system be semiglobally uniformly ultimately boundedness (SGUUB) and keep the observer and tracking errors to remain in a small neighborhood of the origin. The numerical simulation example is presented to validate the effectiveness of the proposed control scheme and theory.

In this paper, an adaptive fuzzy tracking control scheme based on fuzzy state observer is proposed for a class of uncertain non-strict feedback nonlinear systems with function constraints. In the first place, based on the approximation characteristic of fuzzy logic systems (FLSs), a fuzzy state observer is designed to estimate the immeasurable state variables in the controlled system. Next, under the framework of adaptive backstepping control technology, FLSs are selected not only to approximate unknown nonlinear functions, but also to avoid algebraic loop problem caused by non-strict feedback structure. At the same time, asymmetric Barrier Lyapunov functions (BLFs) are selected to solve the problem that system states are subject to function constraints, which are related to states and time. Then, Lyapunov stability theory is utilized to prove the stability of the controlled system, the realizability of function constraints and the convergence of output tracking errors. Finally, a simulation is given to check the effectiveness of the proposed control scheme.

In this paper, an observer-based indirect adaptive fuzzy output feedback control scheme is proposed for a biped robotic system. Fuzzy logic systems are employed to approximate the biped unknown nonlinear functions. Based on the fuzzy system, a state observer is designed for estimating the states of the controlled system. In addition, we use a regularized inverse function to overcome the control singularity problem. Based on Lyapunov stability analysis, convergence of the system states and boundedness of tracking errors can be achieved by the proposed control scheme. The experiment results are used to demonstrate the effectiveness and robustness of the proposed control scheme, and the tracking performance.

Observer-based adaptive fuzzy output constrained control for uncertain nonlinear multi-agent systems

This work studies an observer-based fuzzy adaptive control problem for the uncertain nonlinear time-delay systems with unknown virtual and actual control gain functions. The Lyapunov-Krasovskii function is utilized to eliminate the unknown time delays. In order to estimate the uncertain nonlinear functions, Fuzzy Logic Systems(FLSs) are quoted. Then the fuzzy state observer is devised to handle the unavailable state issue. By using the backstepping control technique and bounded control method, a novel observer-based fuzzy adaptive backstepping control approach is developed. The rationality of the presented control methods is demonstrated by means of the Logarithm Lyapunov functions.

This article is concerned with the finite-time containment control problem for nonlinear multiagent systems, in which the states are not available for control design and the control input contains time delay. Fuzzy-logic systems (FLSs) are used to approximate the unknown nonlinear functions and a novel distributed fuzzy state observer is proposed to obtain the unmeasured states. Under the framework of cooperative control and finite-time Lyapunov function theory, an observer-based adaptive fuzzy finite-time output-feedback containment control scheme is developed via the adaptive backstepping control design algorithm and integral compensator technique. The proposed adaptive fuzzy containment control method can ensure that the closed-loop system is stable and all followers can converge to the convex hull built by the leaders in finite time. A simulation example is provided to confirm the effectiveness of the proposed control method.

In this paper, an observer-based adaptive fuzzy backstepping control approach is proposed for a class of uncertain nonlinear stochastic systems with the immeasurable states and unknown-time-delays. The fuzzy logic systems are used to approximate the unknown nonlinear functions, and a fuzzy state observer is designed to estimate the unmeasured states. By combining the backstepping design technique, an observer-based adaptive fuzzy control method is developed. It is proved that the proposed adaptive fuzzy control approach can not only guarantee that all the signals of the closed-loop system are semi-globally uniformly ultimately bounded (SGUUB) in mean square, but also the output of the system converge to a small neighborhood of the origin.

In this study, an observer-based adaptive fuzzy controller is proposed for a quarter pneumatic active suspension considering the actuator failure and unknown dead zone of the air spring. To approximate the uncertain parameters, unmodeled dynamics, and unknown masses of passengers, fuzzy logic systems are employed. Besides, the system states are considered unmeasurable as the real suspension application which affects the control performance. To solve this problem, a fuzzy state observer is proposed to estimate the system states for the control design. Then, an adaptive command filtered control is developed to compensate for the effect of actuator imperfections and induce the issue of complexity problem. Furthermore, the tracking error of chassis movement is ensured by the prescribed performance. The system stability is analyzed by the Lyapunov theorem. Finally, the effectiveness of the designed control is justified by numerical simulation examples.

Observer-based fuzzy adaptive stabilization of uncertain switched stochastic nonlinear systems with input quantization

This paper is focused on the observer-based adaptive fuzzy control problem for nonlinear stochastic systems with the nonstrict-feedback form, in which some complicated and challenging issues including unmeasurable states, input quantization and actuator faults are addressed. The fuzzy logic systems are introduced to approximate the nonlinear functions existing in the control system. A fuzzy observer is designed to observe the unavailable state variables. In order to handle the negative effects resulting from input quantization and actuator faults, a damping term with the estimation of unknown bounds as well as a positive time-varying integral function are constructed, respectively. Furthermore, an observer-based adaptive fuzzy control scheme is proposed for the considered systems to compensate for the effects of input quantization and actuator fault based on adaptive backstepping approach. The proposed control strategy can guarantee that all the signals in the closed-loop system are bounded. Finally, simulation results are provided to illustrate the effectiveness of the proposed adaptive control scheme.

The paper proposes a solution to the problem of observer-based adaptive fuzzy control for MIMO nonlinear dynamical systems (e.g. robotic manipulators). An adaptive fuzzy controller is designed for a class of nonlinear systems, under the constraint that only the system’s output is measured and that the system’s model is unknown. The control algorithm aims at satisfying the \(H_\infty \) tracking performance criterion, which means that the influence of the modeling errors and the external disturbances on the tracking error is attenuated to an arbitrary desirable level. After transforming the MIMO system into the canonical form, the resulting control inputs are shown to contain nonlinear elements which depend on the system’s parameters. The nonlinear terms which appear in the control inputs are approximated with the use of neuro-fuzzy networks. Moreover, since only the system’s output is measurable the complete state vector has to be reconstructed with the use of a state observer. It is shown that a suitable learning law can be defined for the aforementioned neuro-fuzzy approximators so as to preserve the closed-loop system stability. With the use of Lyapunov stability analysis, it is proven that the proposed observer-based adaptive fuzzy control scheme results in \(H_{\infty }\) tracking performance.

Research of Pneumatic Polishing Force Control System Based on High Speed On/off with PWM Controlling