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In this article, a fuzzy adaptive output feedback control strategy is designed for a class of uncertain nonlinear switched system with full state constraints under arbitrary switching signal. The states of the system studied in this article are unmeasurable, so a fuzzy observer is designed to estimate the unmeasurable states. At the same time, in order to ensure that the states of the system do not violate the constraints related to the desired output and states, the log-type barrier Lyapunov function method is selected to solve this constraint problem. Finally, through Lyapunov stability theory analysis, it is found that the designed control strategy can ensure that all signals in the closed-loop system are bounded, and the states of the system do not violate their corresponding constraints. In addition, a numerical simulation verifies the effectiveness of the control strategy. [ABSTRACT FROM AUTHOR]

In this article, an adaptive tracking control approach is developed for a class of strict-feedback nonlinear systems with time-varying full state constraints. As a breakthrough in this system, the special function constraints (whose constraint boundary is relevant to both state variables and time) are considered, which are rarely studied by research work. And there is no doubt that this method increases the complexity of designing this scheme. Furthermore, the time-varying integral barrier Lyapunov functions combining with backstepping technique is introduced to break the limitation of traditional methods as well as achieve the full state constraints. Meanwhile, fuzzy logic systems are selected to approximate unknown nonlinear functions. It is verified that all closed-loop signals are bounded and all states are forced in the time-varying boundness. In addition, the proposed control strategy has a good performance. The effectiveness of the theoretical analysis results is proved via a simulation example. [ABSTRACT FROM AUTHOR]

Aiming at the research content of tracking control for a class of nonlinear uncertain switched systems including full state constraints, a novel event-triggered adaptive fuzzy output feedback control scheme is given. The systems studied need to use the approximation principle of fuzzy logic systems (FLSs) to solve the nonlinear smooth function with unknown terms. For ensuring that all states of the systems are within the time-varying constraint limits, the stability of the switched systems is verified by utilizing tangent barrier Lyapunov function (Tan-BLF). Based on the potential barrier Lyapunov function (BLF) and backstepping recursive construction method, the adaptive law, controller and event-triggered mechanism of the subsystem are designed. The proposed method will make that the signal is bounded. Moreover, the tracking error can be adjusted to the neighborhood closed to the origin. Simulation examples indicate the feasibility of the method. [ABSTRACT FROM AUTHOR]

This article addresses a new adaptive fuzzy fast finite-time state-constraint protocol for leader-follower formation control. Each agent in uncertain nonlinear dynamic multiagent systems is represented by second-order integrator, which synchronously governs its position and velocity. The fuzzy logic systems are employed to compensate and approximate uncertain functions. On the premise of maintaining formation structure and coupling communication topology, time-varying transformation equations containing exponential signals are introduced to ensure that state capability boundaries for different physical quantities of agents are not violated. It not only guarantees own state performance and collision avoidance among agents, but also realizes the specified transient and steady formation performance. Furthermore, focusing on convergence rate, the adaptive fuzzy fast finite-time strategy is designed that can guarantee all agents will follow the desired formation configuration in fast finite-time. Through the abovementioned approaches provide a good way to improve the convergence and ensure the security for decentralized formation control. Finally, the validity of the theoretical method is proved by fast finite-time stable theory and Lyapunov stability theory. The effectiveness of the protocol is verified by digital simulation and simulation comparison. [ABSTRACT FROM AUTHOR]

This article investigates an adaptive fuzzy tracking control approach via output feedback for a class of switched uncertain nonlinear systems with full-state constraints under arbitrary switchings. The adaptive observer and controller are designed based on fuzzy approximation. The main characteristic of discussed systems is that the state variables are not available for measurement and need to be kept within the constraint set. In order to estimate the unmeasured states, the adaptive fuzzy state observer is constructed. To guarantee that all the states do not violate the time-varying bounds, the tangent barrier Lyapunov functions (BLF-Tans) are selected in the design procedure. Based on the common Lyapunov function method, the stability of considered systems is analyzed. It is demonstrated that all the signals in the resulting system are bounded, and all the states are limited in their constrained sets. Furthermore, the simulation example is used to validate the effectiveness of the presented control strategy. [ABSTRACT FROM AUTHOR]

A novel adaptive control scheme is developed for active suspension systems (ASSs) based on neural networks (NNs) and backstepping control strategies. Since the springs and piecewise dampers are nonlinear, the unknown internal dynamics are approximated by radial basis function neural networks (RBFNNs). Then, to solve the time-varying constrains of both vertical displacement and corresponding speed in vehicle body, the Tangent Barrier Lyapunov Functions (TBLFs) are incorporated into the controller design. Furthermore, the adaptive controller and adaptive laws are designed to improve the riding comfortable, handling stability and driving safety. In the end, the simulation results show the effectiveness and feasibility of the proposed adaptive algorithm compared with unconstrained adaptive approach. [ABSTRACT FROM AUTHOR]

A reliable, efficient, and simple control is presented and validated for a quarter-car active suspension system equipped with an electro-hydraulic actuator. Unlike the existing techniques, this control does not use any function approximation, e.g., neural networks (NNs) or fuzzy-logic systems (FLSs), while the unmolded dynamics, including the hydraulic actuator behavior, can be accommodated effectively. Hence, the heavy computational costs and tedious parameter tuning phase can be remedied. Moreover, both the transient and steady-state suspension performance can be retained by incorporating prescribed performance functions (PPFs) into the control implementation. This guaranteed performance is particularly useful for guaranteeing the safe operation of suspension systems. Apart from theoretical studies, some practical considerations of control implementation and several parameter tuning guidelines are suggested. Experimental results based on a practical quarter-car active suspension test-rig demonstrate that this control can obtain a superior performance and has better computational efficiency over several other control methods. [ABSTRACT FROM AUTHOR]

In this article, an adaptive sliding-mode control scheme is developed for a class of uncertain quarter vehicle active suspension systems with time-varying vertical displacement and speed constraints, in which the input saturation is considered. The integral terminal SMC is adopted to improve convergence accuracy and avoid singular problems. In addition, neural networks are used to model unknown terms in the system and the backstepping technique is taken into account to design the actual controller. To guarantee that the time-varying state constraints are not violated, the corresponding Barrier Lyapunov functions are constructed. At the same time, a continuous differentiable asymmetric saturation model is developed to improve the stability of the system. Then, the Lyapunov stability theory is used to verify that all signals of the resulting system are semi globally uniformly ultimately bounded, time-varying state constraints are not violated, and error variables can converge to the small neighborhood of 0. Finally, results of the simulation of the designed control strategy are given to further prove the effectiveness. [ABSTRACT FROM AUTHOR]

This article proposes an adaptive finite-time tracking control based on fuzzy-logic systems (FLSs) for an uncertain nonstrict nonlinear multi-input–multi-output (MIMO) full-state-constrained system with the coupled uncertain dead-zone input. By using three kinds of FLSs: the uncertain system, the uncertain dead zone, and the uncertain input transfer inverse matrix are approximated using the system function FLS, dead-zone FLS, and input transfer inverse matrix FLS, respectively. After defining the barrier Lyapunov function, the fuzzy-based adaptive tracking controllers are designed, and the fuzzy weights are updated through the proposed adaptive laws. Then, based on the extended finite-time convergence theorem, with the design parameters chosen properly, the target uncertain nonlinear system is guaranteed to be semiglobal practical finite-time stable (SGPFS); and the full-state constraints are not violated while avoiding the effects of the dead zones. Furthermore, a simulation is presented to verify the validity of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This article concentrates on the event-driven controller design problem for a class of nonlinear single input single output parametric systems with full state constraints. A varying threshold for the triggering mechanism is exploited, which makes the communication more flexible. Moreover, from the viewpoint of energy conservation and consumption reduction, the system capability becomes better owing to the contribution of the proposed event-triggered mechanism. In the meantime, the developed control strategy can avoid the Zeno behavior since the lower bound of the sample time is provided. The considered plant is in a lower triangular form, in which the match condition is not satisfied. To ensure that all the states retain in a predefined region, a barrier Lyapunov function (BLF) based adaptive control law is developed. Due to the existence of the parametric uncertainties, an adaptive algorithm is presented as an estimated tool. All the signals appearing in the closed-loop systems are then proven to be bounded. Meanwhile, the output of the system can track a given signal as far as possible. In the end, the effectiveness of the proposed approach is validated by an aircraft wing rock motion system. [ABSTRACT FROM AUTHOR]

In this article, the adaptive sliding mode (ASM) control scheme of half-car active suspension systems with prescribed performance is studied. Because of the affected by model uncertainty, time-varying parameter, pavement roughness excitation, etc., the study of suspension systems can be regarded as the multivariable nonlinear control problem. First of all, the prescribed performance function (PPF) is applied to constrain the displacement and pitch angle of the suspension systems to ensure the transient and steady-state suspension responses. Second, an integral terminal sliding mode control method with strong robustness is put forward, which can make the system converge rapidly in a finite-time when it is far from the equilibrium point, solve the singularity problem in the control process, and reduce the chattering phenomenon in the traditional sliding mode control. Then, the neural networks (NNs) approximation characteristics are used to deal with unknown items in the design of the controller, and the Lyapunov stability theory is employed to analyze the stability of the closed-loop system. In the end, the comparative simulation results demonstrate the feasibility and effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]