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

Adaptive fuzzy control for pure-feedback systems with full state constraints and unknown nonlinear dead zone

In this brief, a novel direct adaptive fuzzy tracking control (DAFTC) scheme for marine vehicles with fully unknown parametric dynamics and uncertainties is proposed. The significant contributions of the DAFTC approach are as follows. First, in the backstepping framework, fully unknown parametric dynamics and uncertainties are encapsulated into a lumped nonlinearity function encompassing system states and virtual control signals. Second, the integrated nonlinearity function is further identified online by an adaptive fuzzy approximator that synthesizes a model-free control scheme (termed DAFTC) without requiring any a priori knowledge of the model. Third, tracking errors are proven to be uniformly ultimately bounded (UUB) and can converge to an arbitrarily small neighborhood of zero in a finite time. Simulation studies and comprehensive comparisons demonstrate that the proposed DAFTC scheme has remarkable performance and is superior in both tracking accuracy and unknown parametric dynamics compensation.

In this paper, the direct adaptive fuzzy robust control is applied to the attitude stabilization control of the satellite, and the detailed design procedure of the fuzzy control system is presented. The controller directly acting on the controlled system doesn't require the modeling of the uncertain nonlinear system. The direct adaptive fuzzy controller is designed for the satellite, and the robust control term is added to the system in order to resist the external disturbances. The adaptive law of the rule parameter is derived. Simulation results show that precise attitude control is accomplished in spite of the uncertainty in the system.

Application of adaptive fuzzy control to ac machines

SummaryThis paper presents an adaptive fuzzy control approach of multiple‐input–multiple‐output (MIMO) switched uncertain systems, which involve time‐varying full state constraints (TFSCs) and unknown disturbances. In the design procedure, the fuzzy logic systems are adopted to approximate the unknown functions in the systems. The adaptive fuzzy controller is set up by backstepping technique. According to the tangent barrier Lyapunov function (BLF‐Tan), a novel adaptive MIMO switched nonlinear control algorithm is designed. Under the rule of arbitrary switchings and the proposed control laws, it is demonstrated that all signals in the resulted system are semiglobally uniformly ultimately bounded (SGUUB) and the tracking error converges to a small neighborhood of zero with TFSCs. Furthermore, the simulation example validates the effectiveness of presented control strategy.

formula omitted] tracking-based decentralized hybrid adaptive output feedback fuzzy control for a class of large-scale nonlinear systems

In this paper, an adaptive fuzzy control scheme for fuzzy systems with multiple delayed state perturbations, time- varying uncertain parameters, and disturbances. These delayed state perturbations, uncertainties, and external disturbances in the fuzzy systems are assumed to be any uncertain bounded but unknown continuous functions. An improved adaptation law with sigma-modification is proposed to estimate such unknown bounds, and on the basis of the updated values of these unknown bounds, a continuous memoryless state feedback controller is constructed for achieving robust stability of the closed-loop system. Based on Lyapunov stability theorem, the proposed adaptive robust fuzzy controller can guarantee that the state converges asymptotically to zero in the presence of multiple delayed state perturbations, time-varying uncertain parameters, and disturbance. In addition, it is also shown that this improved adaptation law with sigma-modification can be applied to the general adaptive control problems to obtain some more exact control results. Finally, an inverted pendulum system is given to demonstrate the validity of the proposed design method.

This paper solves the problem of adaptive fuzzy inverse compensation control for an uncertain nonlinear system whose actuator is subjected to generalized dead-zone nonlinearity. By defining a continuous connection function and combining with the mean-value theorem, the generalized dead zone is first decomposed into a nominal asymmetric dead zone multiplying an uncertain continuous input function. Afterward, a smooth inversion and its parameterization are further proposed such that a new expression of adaptive asymmetric dead-zone compensation error is established in Theorems 1 and 2. With such an expression, the fuzzy systems can be successfully embedded into a compensation structure to indirectly handle uncertain input dynamics. In addition, a separation scheme is developed to construct two online estimators. Based on the above design procedure, an adaptive inverse compensator for generalized dead zone is built eventually. With the backstepping iteration design of compensator input, an adaptive fuzzy controller is developed to establish the closed-loop system stability. Finally, two simulations are conducted to illustrate the effectiveness and applicability of the proposed control scheme.

Robust fixed-time H∞ tracking control of UUVs with partial and full state constraints and prescribed performance under input saturation

Many modern control systems become gradually more complicated and, consequently, the approach to control design approaches is both difficulty and complex. Moreover, if such a complex interconnected system is subjected to non‐smooth nonlinearities in the actuator, then unexpected difficulties, degradation or, even worse, instability will arise in the system performance. Therefore, a new decentralized sliding mode control (DSMC) approach for such a class of complex interconnected systems subjected to non‐smooth (deadzone) nonlinearities is proposed in this paper. Based on sliding mode control (SMC) theory, the proposed DSMC laws guarantee the global reaching condition of the sliding mode in uncertain complex interconnected systems with deadzone nonlinearities, that is, they can ensure that the sliding mode is reached in finite time and with prescribed transient behavior. In the sliding mode, the investigated uncertain complex interconnected system with deadzone nonlinearities in the actuator still are insensitive to system uncertainties and external disturbances. The proposed DSMC laws can work effectively for uncertain complex interconnected systems either with or without deadzone nonlinearities in the actuator. However, this cannot be guaranteed by the traditional DSMC design for systems without input deadzone nonlinearities. Furthermore, the sliding motion can be controlled to converge within a specified exponential speed. Finally, two illustrative examples with a series of computer simulations are presented to demonstrate the effectiveness of the proposed DSMC laws.

In this paper, an adaptive fuzzy output constrained control design approach is addressed for multi-input multioutput uncertain stochastic nonlinear systems in nonstrict-feedback form. The nonlinear systems addressed in this paper possess unstructured uncertainties, unknown gain functions and unknown stochastic disturbances. Fuzzy logic systems are utilized to tackle the problem of unknown nonlinear uncertainties. The barrier Lyapunov function technique is employed to solve the output constrained problem. In the framework of backstepping design, an adaptive fuzzy control design scheme is constructed. All the signals in the closed-loop system are proved to be bounded in probability and the system outputs are constrained in a given compact set. Finally, the applicability of the proposed controller is well carried out by a simulation example.

This article proposes an adaptive fuzzy controller for a class of uncertain strict-feedback nonmatching nonlinear single-input single-output systems with fuzzy dead zone and full time-varying state constraints. The states considered here are immeasurable and full states of the systems are constrained in a bounded set with time-varying regions. Following the adaptive backstepping design framework, the tangent barrier Lyapunov functions are introduced to the integrated design to address the problems in such systems. Fuzzy logic systems are used to identify the unknown smooth functions and unknown parameters. An input-driven observer is designed to estimate the immeasurable states. To distinguish the conventional deterministic dead zone models, the output of dead zone is uncertainty. The form of indeterminate dead zone as a combination of a liner and a disturbance-like term is extended by the fuzzy algorithms. Even though the output of dead zone is fuzzy and adopting the integrated design, the proposed fuzzy controller can ensure that all the signals in the closed-loop systems are semiglobal uniformly ultimately bounded and guarantee the tracking performance. Finally, simulation results are shown to verify the effectiveness and reliability of the proposed approach.

Distributed adaptive fuzzy control for multi-agent systems with full state constraints and unmeasured states

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.

To achieve better adaptation properties, the proposed controller is a weighted combination of indirect and direct adaptive fuzzy controls such that both fuzzy descriptions and control rules can be incorporated under a united controller design at the same time. By using fuzzy systems and robust control technique, the interconnections between subsystems are extended to general unknown nonlinear functions. No a priori knowledge of lower and upper bounds on lumped uncertainties is required to construct each local controller. The resulting closed-loop large-scale system is proved to be asymptotically stable. The controller design is applicable to an automated highway system, and simulation results confirm its practical usefulness.