Event-based adaptive fuzzy tracking control for nonlinear systems with input magnitude and rate saturations

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

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

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

This paper proposes an adaptive fuzzy logic tracking control for unknown nonlinear systems having input time delay. In the controller design, a filtered tracking error to facilitate handling of the time delay and fuzzy approximation of nonlinear function are introduced. Lyapunov stability analysis shows that the designed tracking control yields bounded signals of the closed-loop system. Two simulation examples are provided to prove the effectiveness of the proposed controller in achieving tracking with bounded control signals.

This article addresses an event-triggered adaptive fuzzy tracking control problem for nonlinear systems with unknown control directions. To achieve the practical tracking, fuzzy logic systems are employed to approximate unknown nonlinear function. Nussbaum functions are adopted to address the problem of the unknown control directions. The newly designed controller not only guarantees the tracking error that converges to an arbitrary small neighborhood near the zero but also reduces the communication burden from the controller to the actuator. Moreover, the feasibility of the proposed event-triggered mechanism is verified by excluding Zeno behavior. Finally, the simulation result proves the effectiveness of the designed control scheme.

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

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.

In this paper, we study the application of fuzzy set theory, genetic algorithms and rough set theory techniques to the control of the rectifier. A fuzzy adaptive control scheme with the aid of rough set theory via genetic algorithms (GAs) finding the center parameters in place of the classical control is proposed. On the one hand, genetic algorithm gets optimal parameters of an accurate domain model, which tunes the scaling factors of fuzzy adaptive control but is hardly established in a non-liner system. On the other hand, fuzzy adaptive control deals with the dynamics and complexity of responses from rectifier of a HVDC system in the operation points, by adjusting its control parameters with the aid of rough tuner adaptively. Our study includes a brief introduction to fuzzy control and rough set algorithms, both theory and application. We also evaluate the performance of rough fuzzy control by simulation in the paper. The focus of our experiments is on the constant current control in rectifier side of HVDC system. The result shows there are many improvements offered by the fuzzy control scheme based on rough set theory in comparison with the only fuzzy control scheme in the rectifier side of HVDC system.

Adaptive fuzzy tracking controller design is addressed for a class of uncertain nonlinear system in strict-feedback form. The system dynamics are unknown, and external disturbances satisfy triangular bounds. We utilize Tagaki-Sugeno (T-S) fuzzy logic system to approximate the unknown system functions. The proposed scheme can guarantee the ultimate uniform boundedness of all the signals in the closed-loop system. Compensated tracking errors are employed to construct Lyapunov candidate function. The proposed design circumvents the repeated differential of virtual control law, which renders the controller structures simple and easy to implement. Simulation researches are carried out. The simulation results demonstrate the effectiveness and usage of the proposed design.

Event-triggered low-computation adaptive output-feedback fuzzy tracking control of uncertain nonlinear systems

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

This paper addresses the problem of state estimation and the tracking control scheme combining fuzzy control technique for a class of MIMO nonlinear systems, in which some variables are not measurable, with plant uncertainties and external disturbances First, the nonlinearities are approximated using neural networks, and then are modeled based on linear difference inclusions(LDI), which makes the structure of the desired filter simpler and parameter turning easier. A unified framework is established to solve the addressed H ∞ filtering problem by exploiting linear matrix inequality (LMI) approach. The proposed control law is based on indirect adaptive fuzzy control and uses two on-line estimations. The adaptive fuzzy tracking control using Variable Structure (VS) control technique is derived based on Lyapunov criterion to resolve system uncertainties and external disturbances. This is done in such a way that all states of the system are bounded and the H ∞ tracking performance is achieved. Finally, A numerical example is investigated to demonstrate the feasibility and the effectiveness of the proposed methodology.

A reinforced adaptive fuzzy sliding-mode controller was developed to track the desired state for a certain class of nonlinear dynamic system. Initially, adaptive fuzzy logic was used to derive the update laws for approximating the uncertain nonlinear functions of a dynamic system. Subsequently, a reinforced adaptive mechanism and sliding-mode control were incorporated into the adaptive fuzzy control scheme. In the reinforced adaptive mechanism, both the model and controller parameters were adjusted simultaneously to attenuate the effects caused by the unmodeled dynamics and disturbances. Performance analysis demonstrated the superiority of the proposed reinforced adaptive law over the conventional direct adaptive scheme regarding faster tracking and convergence of parameters. To verify its effectiveness and extend its application, the proposed reinforced adaptive fuzzy sliding-mode controller was applied to balance a two-wheeled robot steering on a bumpy road. A number of simulations and experiments were performed. In addition, a conventional direct adaptive scheme under the same environment was also performed for comparison. These results demonstrated that the balance performance of a nonlinear dynamic two-wheeled robot improved because of embedding the proposed reinforced adaptive fuzzy sliding-mode controller into the microcontroller.

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