Search

In this paper, by adopting sliding mode control, an adaptive memoryless control scheme has been developed for uncertain Rössler chaotic systems with unknown time delays. Firstly, the proposed adaptive control can force the trajectories of controlled Rössler time-delayed chaotic systems into the specified sliding manifold. Then, the Riemann sum is introduced to analyze the stability of the equivalent dynamics in the sliding manifold. The control performance can be predicted even if the controlled systems have unmatched uncertainties and unknown time delays, which have not been well addressed in the literature. Numerical simulations are included to demonstrate the feasibility of the proposed scheme. [ABSTRACT FROM AUTHOR]

The leader‐following consensus problem of a class of non‐strict feedback multi‐agent systems with unknown disturbances, this paper develops a novel adaptive event‐triggered control strategy ground on the consideration of the full state constraints. Combining the dynamic surface control and event triggering mechanism, the unknown external disturbances are estimated by designing a disturbance observer. In order to deal with unknown functions, this paper employs the neural networks. On purpose of keeping the output variable within a constraint boundary, the barrier Lyapunov function is chosen during the design course. Then, through the backstepping method and the Lyapunov stability theorem, it is proved that all signals in the closed‐loop systems are bounded, and the control performance of the closed‐loop system is ensured by selecting design parameters reasonably. Eventually, the availability of the control strategy is guaranteed through experimental example. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive neural network (NN) controller is proposed for a class of nonlinear active suspension systems (ASSs) with hydraulic actuator. To eliminate the problem of “explosion of complexity” inherently in the traditional backstepping design for the hydraulic actuator, a dynamic surface control technique is developed to stabilize the attitude of the vehicle by introducing a first-order filter. Meanwhile, the presented scheme improves the ride comfort even when the uncertain parameter exists. Due to the existence of uncertain terms, the NNs are used to approximate unknown functions in the ASSs. Finally, a simulation for a servo system with hydraulic actuator is shown to verify the effectiveness and reliability of the proposed approach. [ABSTRACT FROM AUTHOR]

In this paper, a reinforcement learning-based adaptive control algorithm is proposed to solve the tracking problem of a discrete-time (DT) nonlinear state and input time delayed system of the wheeled mobile robot (WMR). With the typical model of the WMR transformed into an affine nonlinear DT system, a delay matrix function and appropriate Lyapunov–Krasovskii functionals are introduced to overcome the problems caused by the state and input time delays, respectively. Furthermore, with the approximation of the radial basis function neural networks (NNs), the adaptive controller, the critic NN, and action NN adaptive laws are defined to guarantee the uniform ultimate boundedness of all signals in the WMR system, and the tracking errors convergence to a small compact set to zero. Two examples of simulation are given to illustrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, the global robust output regulation problem of a class of uncertain nonlinear systems is investigated by the event-triggered adaptive control law for the case of unknown control direction. The Nussbaum-type function-based technique is proposed to tackle the presence of unknown control direction. Then, by applying the adaptive control technique and the internal model principle, a new event-triggered adaptive control method is proposed. With the proposed corresponding event-triggered mechanism, the output regulation of the nonlinear systems can be realized regardless of the unknown control direction, meanwhile the Zeno behavior can be excluded. Finally, a numerical example is presented to verify the effectiveness of the proposed control law. [ABSTRACT FROM AUTHOR]

In this article, we study the control problem of the vehicle active suspension systems (ASSs) subject to actuator failure. An adaptive control scheme is presented to stabilize the vertical displacement of the car-body. Meanwhile, the ride comfort, road holding, and suspension space limitation can be guaranteed. In order to overcome the uncertainty, the neural network is developed to approximate the continuous function with the unknown car-body mass. Furthermore, to improve the transient regulation performance of ASSs when the actuator failure occurs, we propose a novel control scheme with the prescribed performance function to characterize the tracking error convergence rate and maximum overshoot in ASSs. Then, the stability of the proposed control algorithm can be proven based on the Lyapunov theorem. Finally, the comparative simulation results of two actuator failure types (i.e., the float fault and the loss of effectiveness fault) are given to demonstrate the effectiveness of the proposed control schemes. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive neural network (NN) control scheme is proposed for a quarter-car model, which is the active suspension system (ASS) with the time-varying vertical displacement and speed constraints and unknown mass of car body. The NNs are used to approximate the unknown mass of car body. It is commonly known that the stability and security of the ASSs will be weakened when the constraints are violated. Thus, the control problem of the time-varying vertical displacement and speed constraints for the quarter-car ASSs is a very important task because of the demand of the handing safety. The time-varying barrier Lyapunov functions are used to guarantee the constraints of the vertical displacement not violated, and it can prove the stability of the closed-loop system. Finally, a simulation example for the ASSs is employed to show the feasibility and rationality of the proposed approach. [ABSTRACT FROM AUTHOR]

In this paper, we address an adaptive control problem for a class of nonlinear strict-feedback systems with uncertain parameter. The full states of the systems are constrained in the bounded sets and the boundaries of sets are compelled in the asymmetric time-varying regions, i.e., the full state time-varying constraints are considered here. This is for the first time to control such a class of systems. To prevent that the constraints are overstepped, the time-varying asymmetric barrier Lyapunov functions (TABLFs) are employed in each step of the backsstepping design and we also establish a novel control TABLF scheme to ensure the asymptotic output tracking performance. The performances of the adaptive TABLF-based control are verified by a simulation example. [ABSTRACT FROM AUTHOR]

The external disturbance, model uncertainty and actuator faults can affect performance even the safety of flight. To tackle such problems, an adaptive fault-tolerant control method is presented integrated with fast terminal sliding mode control (FTSMC) technology and neural network (NN) for the attitude system of a quadrotor unmanned aerial vehicle, where the NN is employed to approximate the uncertain terms in the system. First, for the loss of effectiveness, an adaptive law is developed to estimate the unknown fault coefficient. Then considering the unknown upper bound of the total disturbance, an adaptive law is designed to predict the unknown upper bound. Furthermore, considering the transient response, a FTSMC scheme is developed for the attitude system, which has better convergence rate than conventional terminal sliding mode control. Finally, the contrast test is performed to verify the effectiveness and superiority of the proposed control method. [ABSTRACT FROM AUTHOR]

In this paper, we aim to solve the optimal tracking control problem for the Henon Mapping chaotic system using the direct heuristic dynamic programming (DHDP) setting with filtered tracking error. The fuzzy logic system is used to approximate the long-term utility function. Compared with the results for chaotic discrete-time system, the cost of the controller is reduced. The Lyapunov analysis approach is utilized to prove the stability of the chaotic system. It is shown that the tracking error, the adaptation law and the control input retain the property of uniformly ultimate boundedness. A simulation example is given to demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This article focuses on the adaptive control problem for Arneodo chaotic systems. It is considered a phenomenon of single state constraint for the systems. In the proposed scheme, the barrier Lyapunov function approach was successfully used to prevent the single state from violating constraint conditions. In the current circumstances, a great many of the results for chaotic systems neglect the situation of constraint. Finally, it is proved that all the signals in the Arneodo chaotic system are bounded. A representative example is proposed in numerical simulations compared with existing results. The performance of the proposed control scheme was validated by using a simulated example. [ABSTRACT FROM PUBLISHER]

The paper presents an improved adaptive sliding mode control method based on fuzzy neural networks for a class of nonlinear systems subjected to input nonlinearity with unknown model dynamics. The control scheme consists of the modified adaptive and the compensation controllers. The modified adaptive controller online approximates the unknown model dynamics and input nonlinearity and then constructs the sliding mode control law, while the compensation controller takes into account the approximation errors and keeps the system robust. Based on Lyapunov stability theorem, the proposed method can guarantee the asymptotic convergence to zero of the tracking error and provide the robust stability for the closed-loop system. In addition, due to the modification in controller design, the singularity problem that usually appears in indirect adaptive control techniques based on fuzzy/neural approximations is completely eliminated. Finally, the simulation results performed on an inverted pendulum system demonstrate the advanced functions and feasibility of the proposed adaptive control approach. [ABSTRACT FROM AUTHOR]

In this paper, a stable reinforcement learning control approach using neural networks (NNs) is developed for the trajectory tracking of an n-link rigid robot manipulator. The considered systems are in discrete time form. The proposed controller design consists of two NNs. One is the critic network that is used to approximate the long-term cost function, whereas other is that the action NN is employed to generate the system input. Then, an optimal control input can be obtained compared with other robot manipulator systems. Using the Lyapunov approach, the tracking error and weight estimates are proven to be semi-global uniformly ultimately bounded. A simulation example is employed to illustrate the effectiveness of the proposed controller. [ABSTRACT FROM PUBLISHER]

This paper designs a fuzzy adaptive control approach for the Continuous Stirred Tank Reactors (CSTRs) with full state constraints and actuator faults. Using the backstepping design technique and Integral barrier Lyapunov function (IBLF), the full state constraints are handled in the design process of the adaptive controller. Besides, there exist unknown internal dynamics in the CSTRs system, which are identified by the fuzzy logic system. It is shown that the constraints are not overstepped and the CSTRs operate reliably is ensured, despite the presence of system nonlinearities, stuck faults and loss of control effectiveness. Based on the Lyapunov stability theory, the proposed approach can ensure that all the signals of the CSTRs system are globally bounded. Finally, the simulation results on CSTRs are showed to reveal the availability of the developed control scheme. [ABSTRACT FROM AUTHOR]