Search

An adaptive dynamic surface control (DSC) scheme is proposed for the multi-input multi-output attitude control of near-space hypersonic vehicles (NHV). The proposed control strategy can improve the control performance of NHV despite uncertainties and external disturbances. The proposed controller combines dynamic surface control and radial basis function neural network (RBFNN) and is designed to control the longitudinal dynamics of NHV. The DSC technique is used to handle the problem of “explosion of complexity” inherent to the conventional backstepping method. RBFNN is used to approximate the unknown nonlinear function, and a robustness component is introduced in the controller to cancel the influence of compound disturbance and improve robustness and adaptation of the system. Simulation results show that the proposed strategy possesses good robustness and fast response.

This paper proposes an anti-disturbance control scheme for the near space vehicle (NSV) based on terminal sliding mode (TSM) technique and disturbance observer method. To tackle the system uncertainty and the time-varying unknown external disturbance of the NSV, a disturbance observer based on TSM technique is designed which can render the disturbance estimate error convergent in finite time. Furthermore, an auxiliary design system is introduced to analyze the input saturation effect. Based on the developed disturbance observer and the auxiliary design system, an anti-disturbance attitude control scheme is developed for the NSV using the TSM technique to speed up the convergence of all signals in closed-loop system. For the closed-loop system, the stability is rigorously proved by using the Lyapunov method and we guarantee the finite time convergence of all closed-loop system signals in the presence of the integrated affection of the system uncertainty, the input saturation, and the unknown external disturbance. Simulation study results are given to show the effectiveness of the developed TSM anti-disturbance attitude control scheme using the disturbance observer and the auxiliary system for the NSV.

This paper describes the design of guaranteed transient performance based attitude control for the near space vehicle (NSV) with control input saturation using the backstepping method. To improve the robust controllability of the NSV, the parameter adaptive method is used to tackle the integrated effect of unknown time-varying disturbance and control input saturation. Based on the backstepping technique and parameter estimated outputs, a robust attitude control scheme is proposed for the NSV with input saturation. A novel robust attitude control scheme is then proposed based on a prescribed performance bound (PPB) which characterizes the convergence rate and maximum overshoot of the attitude tracking error. The closed-loop system stability under both the developed robust attitude control schemes is proved using Lyapunovs method and uniformly asymptotical convergence of all closed-loop signals is guaranteed. Finally, simulation results are given to show the effectiveness of both the proposed robust constrained attitude control schemes.

In this article, robust attitude control schemes are proposed for the near-space vehicle with the backlash-like hysteresis and the time-varying unknown disturbance using backstepping technique. In the robust attitude control design, the backlash-like hysteresis is described by a dynamic equation which is decomposed as a linear term and a nonlinear term. To handle the nonlinear term in the dynamic equation of the backlash-like hysteresis and the time-varying unknown disturbance, they are treated as a compounded disturbance and the nonlinear disturbance observer is designed to estimate them. Based on the developed nonlinear disturbance observer, the robust backstepping attitude control is firstly developed for the near-space vehicle. Then, the robust sliding mode attitude control scheme is presented for the near-space vehicle to further enhance the robust attitude control performance. The convergence of all closed-loop signals is rigorously proved via Lyapunov analysis method under the developed robust attitude control schemes. Finally, simulation results are presented to illustrate the effectiveness of the proposed attitude control approaches.

An adaptive robust attitude tracking control law based on switched nonlinear systems is presented for a variable structure near space vehicle (VSNSV) in the presence of uncertainties and disturbances. The adaptive fuzzy systems are employed for approximating unknown functions in the flight dynamic model and their parameters are updated online. To improve the flight robust performance, robust controllers with adaptive gains are designed to compensate for the approximation errors and thus they have less design conservation. Moreover, a systematic procedure is developed for the synthesis of adaptive fuzzy dynamic surface control (DSC) approach. According to the common Lyapunov function theory, it is proved that all signals of the closed-loop system are uniformly ultimately bounded by the continuous controller. The simulation results demonstrate the effectiveness and robustness of the proposed control scheme.

In this paper, a robust synchronization control scheme is proposed for chaotic systems in the presence of system uncertainties and unknown external disturbances. For the synchronization error system, the compound disturbance which is estimated using the disturbance observer cannot be directly measured. If the gain matrix is properly chosen, the disturbance observer can approximate the unknown compound disturbance well. And then, the constrained robust synchronization control scheme is presented for uncertain chaotic systems based on the output of disturbance observer. In the design of a robust synchronization control scheme, the effect of unknown control input constraint has been explicitly considered to guarantee the synchronization performance. Numerical simulation results are presented to illustrate the effectiveness of the proposed constrained synchronization control scheme for uncertain chaotic systems.

This paper is concerned with the problem of hypersonic vehicle (HSV) attitude control system in uncertain flight conditions. The problem can be expressed as the adaptive robust control for a class of uncertain nonlinear systems. Based on the description of the aerodynamic structure and the model of flight control system of a certain kind of HSV, the ideal nonlinear generalized predictive control (NGPC) law based on uncertain nonlinear model is raised first to optimize the receding-horizon criterion of tracking errors. Then the online support vector regression (SVR) is employed to identify the uncertain item in the ideal control law. It is the compensating part of the controller. In addition, the stability of the close-loop system is analyzed using the Lyapunov method. The developed control strategy is well-implemented in this HSV attitude control system, and the simulation results compared with both nominal NGPC and RBF neural network disturbance observer show the good robustness and disturbance attenuation ability of this strategy and demonstrate the efficiency of online SVR algorithm.

A robust adaptive fuzzy control scheme is presented for a class of chaotic systems with nonaffine inputs, modeling uncertainties and external disturbances by using backstepping approach. Fuzzy logic systems (FLS) are employed to approximate the unknown parts of the virtual control and practical controls. The main characteristics of the scheme are that the number of the online adaptive parameters is no more than two times of the order of chaotic system and the tracking errors are guaranteed to be uniformly asymptotically stable with the aid of additional adaptive compensation terms. Lorenz system, Chen system, Lu system and Liu system are presented to illustrate the feasibility and effectiveness of the proposed control technique.

The full state hybrid projective synchronization, which includes complete synchronization, anti-synchronization and projective synchronization as its special item, is a novel type of chaos synchronization, so the research in this area has high practical significance. This paper is involved with full state hybrid projective synchronization of a new hyperchaotic system. Based on the stability criterion of linear system, full state hybrid projective synchronization is achieved by appropriate linear separation. Numerical simulation is presented to demonstrate the effectiveness of the proposed method.

In this paper, a robust control scheme is proposed for a class of time-delay uncertain nonlinear systems with unknown input using the sliding mode observer. The sliding mode state observer is given with radial basis function neural networks, and then the robust control scheme is presented based on the designed sliding mode observer. The developed observer-based control scheme consists of two parts. One term is a linear controller and the other term is a neural network controller. Using the Lyapunov method, a criterion for bounded stability of the closed-loop system is developed in terms of linear matrix inequalities. Finally, a simulation example is used to illustrate the effectiveness of the proposed robust control scheme.

As the velocity of near-space vehicle (NSV) is hypersonic, the system should be influenced by strong disturbance and uncertainty. It is difficult to design the flight control system. Nonlinear Generalized Predictive Control (NGPC) method is investigated based on the mechanism model. However, the control performance of NGPC method based on nominal model will be degraded for uncertain systems. Therefore, concerning about a class of uncertain nonlinear systems, the ideal NGPC law based on uncertain model is raised first in this paper. Then a sliding mode disturbance observer (SMDO) is designed to estimate the compound disturbance which includes internal uncertainty, modeling error, and external disturbance. On the other hand, the outputs of SMDO are integrated with nominal NGPC law to eliminate the hybrid disturbance. Furthermore, the stability of the close-loop system is analyzed using the Lyapunov method. Finally, the developed control strategy is used in a hypersonic NSV attitude control system, and simulation results show good robustness and disturbance attenuation ability of this strategy.

This paper presents a novel fuzzy tracking control method, which is used for the reentry attitude tracking problem of a reusable launch vehicle. Based on Takagi-Sugeno fuzzy description of reentry attitude dynamics, the tracking error of attitude angles is studied. Without the need of an augmented system, exponential stability of the tracking error can be obtained by a fuzzy feedforward controller. In addition, to achieve a good tracking performance, H∞ and decay rate ν control methods are considered. The solutions to the robust fuzzy tracking control problem can be obtained by applying linear matrix inequalities and fuzzy logic controller tools. The simulation results demonstrate the effectiveness of the proposed method.

A sliding mode synchronization controller is presented with RBF neural network for two chaotic systems in this paper. The compound disturbance of the synchronization error system consists of nonlinear uncertainties and exterior disturbances of chaotic systems. Based on RBF neural networks, a compound disturbance observer is proposed and the update law of parameters is given to monitor the compound disturbance. The synchronization controller is given based on the output of the compound disturbance observer. The designed controller can make the synchronization error convergent to zero and overcome the disruption of the uncertainty and the exterior disturbance of the system. Finally, an example is given to demonstrate the availability of the proposed synchronization control method.

In this paper, a sliding mode observer scheme of sensor fault diagnosis is proposed for a class of time delay nonlinear systems with input uncertainty based on neural network. The sensor fault and the system input uncertainty are assumed to be unknown but bounded. The radial basis function (RBF) neural network is used to approximate the sensor fault. Based on the output of the RBF neural network, the sliding mode observer is presented. Using the Lyapunov method, a criterion for stability is given in terms of matrix inequality. Finally, an example is given for illustrating the availability of the fault diagnosis based on the proposed sliding mode observer.

In this paper, the sliding-mode lag synchronization control scheme is proposed based on the neural network to synchronize two different delayed chaotic systems. An integral delayed sliding surface is presented to design the sliding mode control. The lag synchronization controller is achieved by combining the RBF (radial basis function) neural network with sliding-mode control. Numerical simulations are presented to demonstrate the effectiveness of the proposed sliding-mode lag synchronization control scheme.

The problem of observer design for a class of nonlinear uncertain time-delay systems satisfying Lipschitz condition was considered. By constructing the appropriate Lyapunov Krasovskii functional and using recursive method, the state observer making the observer error asymptotically convergence to zero is constructed. By using the linear matrix inequality approach, the solution of observer gain matrix which is dependent on the size of the delay, is also given and proved. The conclusions extend and improve some results in the literature. .

A decentralized feedback control scheme is proposed to synchronize linearly coupled identical neural networks with time-varying delay and parameter uncertainties. Sufficient condition for synchronization is developed by carefully investigating the uncertain nonlinear synchronization error dynamics in this article. A procedure for designing a decentralized synchronization controller is proposed using linear matrix inequality (LMI) technique. The designed controller can drive the synchronization error to zero and overcome disruption caused by system uncertainty and external disturbance.

In this paper, a robust adaptive control scheme is proposed for a class of uncertain MIMO time delay systems based on backstepping method with Radical basis function(RBF) neural network. The system uncertainty is approximated by RBF neural networks, and a parameter update law is presented for approximating the system uncertainty. In each step, the control scheme is derived in terms of linear matrix inequalities (LMI's). A robust adaptive controller is designed using backstepping and LMI method based on the output of the RBF neural networks. Finally, an example is given to illustrate the availability of the proposed control scheme.

For higher-order nonlinear system, firstly, improved higher-order sliding mode differentiators are used as indirect disturbance observers to approximate the mismatched compound disturbance except the one which exists in the last subsystem. The estimated errors are arbitrarily small. To avoid the algebraic loop, three schemes are proposed to obtain the estimation value of mismatched compound disturbance of the last subsystem, and the error is bounded. Then, nonsingular recursive terminal sliding mode controller is designed and necessary conditions are given. Finally, it is proved that the system is stable and the tracking error is arbitrarily small. Simulation applied to a near space vehicle has proved all conclusions.

A synchronization controller is designed based on disturbance observer for two uncertain chaotic systems in this paper. The compound disturbance of the synchronization error system consists of non-linear uncertainties and exterior disturbance of two chaotic systems. The compound disturbance observer is proposed based on RBF neural networks and the parameter's update law is given for monitoring the compound disturbance. The output of the compound disturbance observer is used to design synchronization controller. The designed synchronization controller can make the synchronization error converge to zero. Finally, an example is given to demonstrate the availability of the proposed synchronization control method.

The discussion is devoted to the adaptive H∞ control method based on RBF neural networks for uncertain nonlinear systems in this paper. The controller consists of an equivalent controller and an H∞ controller. The RBF neural networks are used to approximate the nonlinear functions and the approximation errors of the neural networks are used in the adaptive law to improve the performance of the systems. The H∞ controller is designed for attenuating the influence of external disturbance and neural network approximation errors. The controller can not only guarantee stability of the nonlinear systems, but also attenuate the effect of the external disturbance and neural networks approximation errors to reach performance indexes. Finally, an example validates the effectiveness of this method.