Adaptive Fault Tolerant Control of Multi-time-scale Singularly Perturbed Systems

This paper studies the fault tolerant control, adaptive approach, for linear time-invariant two-time-scale and three-time-scale singularly perturbed systems in presence of actuator faults and external disturbances. First, the full order system will be controlled using ε-dependent control law. The corresponding Lyapunov equation is ill-conditioned due to the presence of slow and fast phenomena. Secondly, a time-scale decomposition of the Lyapunov equation is carried out using singular perturbation method to avoid the numerical stiffness. A composite control law based on local controllers of the slow and fast subsystems is also used to make the control law ε-independent. The designed fault tolerant control guarantees the robust stability of the global closed-loop singularly perturbed system despite loss of effectiveness of actuators. The stability is proved based on the Lyapunov stability theory in the case where the singular perturbation parameter is sufficiently small. A numerical example is provided to illustrate the proposed method.     

Adaptive Fuzzy Back-stepping Control of a Flexible Air-breathing Hypersonic Vehicle Subject to Input Constraints

The design of an adaptive fuzzy back-stepping tracking control for flexible air-breathing hypersonic vehicle (FAHV) with actuator constraints is discussed. Fuzzy logic systems (FLSs) are applied to approximate the total uncertainty of FAHV model. The minimal-learning-parameter (MLP) algorithms are applied for FLSs to reduce the quantity of calculation. For the sake of preventing explosion of differentiation term in back-stepping control, the derivatives of virtual control laws can be calculated by sliding mode differentiators. Particularly, the novel auxiliary systems are explored to cope with the inputs saturation. Finally, reference trajectory tracking simulation shows the auxiliary systems have better performances than the existing algorithms when the inputs are saturated.     

BTT导弹的自适应模糊反演控制律设计

结合反演控制、自适应控制和模糊控制方法,针对具有非匹配不确定性的BTT导弹非线性动力学模型,设计了一种基于自适应模糊控制的BTT导弹反演控制律.针对传统反演控制律设计的不足,采用模糊控制法逼近BTT导弹控制系统中带有未知成分的非线性函数,实现了无需精确数学模型的全新控制律,避免了因建模误差对控制带来的不良影响.设计了自适应控制律以调整设计参数的变化,利用李亚普诺夫方法证明了系统的稳定性和收敛性.仿真结果表明,所设计的控制律对导弹控制系统中存在的非匹配不确定因素具有较强的鲁棒性和自适应性.     

Adaptive Back-stepping Neural Control for an Embedded and Tiltable V-tail Morphing Aircraft

International Journal of Control, Automation and Systems - This paper presents an adaptive back-stepping neural control (ABNC) method for the coupled nonlinear model of a novel type of embedded...     

Control Synthesis of Singularly Perturbed Fuzzy Systems

This paper considers the problem of designing stabilizing and H_infin controllers for nonlinear singularly perturbed systems described by Takagi-Sugeno fuzzy models with the consideration of the bound of singular perturbation parameter e. For the synthesis problem of simultaneously designing the bound of e and stabilizing or H_infin controllers, linear matrix inequalities (LMI)- based methods are presented. For evaluating the upper bound of e subject to stability or a prescribed H_infin performance bound constraint for the resulting closed-loop system, sufficient conditions are developed, respectively. For the stabilizing and H_infin control synthesis without the consideration of improving the bound of e, new design methods are also given in terms of solutions to a set of LMIs. Examples are given to illustrate the efficiency of the proposed methods.     

Adaptive Neural Control of a Hypersonic Vehicle in Discrete Time

The article investigates the discrete-time controller for the longitudinal dynamics of the hypersonic flight vehicle with throttle setting constraint. Based on functional decomposition, the dynamics can be decomposed into the altitude subsystem and the velocity subsystem. Furthermore, the discrete model could be derived using the Euler expansion. For the velocity subsystem, the controller is proposed by estimating the system uncertainty and unknown control gain separately with neural networks. The auxiliary error signal is designed to compensate the effect of throttle setting constraint. For the altitude subsystem, the desired control input is approximated by neural network while the error feedback is synthesized for the design. The singularity problem is avoided. Stability analysis proves that the errors of all the signals in the system are uniformly ultimately bounded. Simulation results show the effectiveness of the proposed controller.     

奇异摄动系统的鲁棒控制与仿真研究

研究参数不确定奇异摄动系统的状态反馈镇定问题,得到基于线性矩阵不等式（LMI）的系统状态反馈可镇定的充分条件,给出状态反馈控制器的设计算法。算例验证了该方法的可行性与有效性。而且,这种方法同时适用于标准奇异摄动系统和非标准奇异摄动系统的控制。最后通过例子进行了详细的仿真研究。     

Composite Fuzzy Control of Nonlinear Singularly Perturbed Systems

This paper presents the composite fuzzy control to stabilize the nonlinear singularly perturbed (NSP) systems with guaranteed H^infin control performance. We use the Takagi-Sugeno (T-S) fuzzy model to construct the singularly perturbed fuzzy (SPF) systems. The corresponding fuzzy slow and fast subsystems of the original SPF system are also obtained. At first, a set of common positive-define matrices and the controller gains are determined by the Lyapunov stability theorem and linear matrix inequality (LMI) approach. Then, a sufficient condition is derived for the robust stabilization of NSP systems. The composite fuzzy control will stabilize the original NSP systems for all epsivisin(0,epsiv^*) and the allowable perturbation bound epsiv^* can be determined via some algebra inequalities. A practice example is adopted to demonstrate the feasibility and effectiveness of the proposed control scheme     

A Sampled-Data Singularly Perturbed Boundary Control for a Heat Conduction System With Noncollocated Observation

This note presents a sampled-data strategy for a boundary control problem of a heat conduction system modeled by a parabolic partial differential equation (PDE). Using the zero-order-hold, the control law becomes a piecewise constant signal, in which a step change of value occurs at each sampling instant. Through the ‘lifting’ technique, the PDE is converted into a sequence of constant input problems, to be solved individually for a sampled-data formulation. The eigenspectrum of the parabolic system can be partitioned into two groups: a finite number of slow modes and an infinite number of fast modes, which is studied via the theory of singular perturbations. Controllability and observability of the sampled-data system are preserved, irrelevant to the sampling period. A noncollocated output-feedback design based upon the state observer is employed for set-point regulation. The state observer serves as an output-feedback compensator with no static feedback directly from the output, satisfying the so-called ‘low-pass property’. The feedback controller is thus robust against the observation error due to the neglected fast modes.      

A Robust Adaptive Grid Method for a System of Two Singularly Perturbed Convection-Diffusion Equations with Weak Coupling

A system of singularly perturbed convection-diffusion equations with weak coupling is considered. The system is first discretized by an upwind finite difference scheme for which an a posteriori error estimate in the maximum norm is constructed. Then the a posteriori error bound is used to design an adaptive gird algorithm. Finally, a first-order rate of convergence, independent of the perturbation parameters, is established by using the theory of the discrete Green’s function. Numerical results are presented to illustrate support our theoretical results.     

Nonlinear and Adaptive Suboptimal Control of Connected Vehicles: A Global Adaptive Dynamic Programming Approach

This paper studies the cooperative adaptive cruise control (CACC) problem of connected vehicles with unknown nonlinear dynamics. Different from the present literature on CACC, data-driven feedforward and optimal feedback control policies are developed by global adaptive dynamic programming (GADP). Due to the presence of nonvanishing disturbance, a modified version of GADP is presented. Interestingly, the developed policy is guaranteed to globally stabilize the vehicular platoon system, and is robust to unmeasurable nonvanishing disturbance. Numerical simulation results are presented to validate the effectiveness of the developed approach.     

高超声速飞行器神经网络动态逆姿态控制器设计

以一类通用高超声速飞行器纵向模型为研究对象,推导了飞行器纵向姿态运动方程。考虑受到飞行器附加攻角扰动以及气动参数不确定性的影响,对运动方程中速率变化快慢不同的攻角和俯仰速率分别引入精确线性化动态逆反馈,并利用神经网络对系统逆反馈误差进行补偿,有效地抑制了模型参数摄动,实现了对攻角指令的精确跟踪。将设计的控制器对高超声速非线性模型进行系统闭环仿真,仿真结果说明所设计的姿态控制器具有抗参数摄动的性能,能够满足高超声速飞行器复杂飞行条件下的姿态控制要求,具有较强的鲁棒性。     

Design of Adaptive Robot Control System Using Recurrent Neural Network

The use of a new Recurrent Neural Network (RNN) for controlling a robot manipulator is presented in this paper. The RNN is a modification of Elman network. In order to solve load uncertainties, a fast-load adaptive identification is also employed in a control system. The weight parameters of the network are updated using the standard Back-Propagation (BP) learning algorithm. The proposed control system is consisted of a NN controller, fast-load adaptation and PID-Robust controller. A general feedforward neural network (FNN) and a Diagonal Recurrent Network (DRN) are utilised for comparison with the proposed RNN. A two-link planar robot manipulator is used to evaluate and compare performance of the proposed NN and the control scheme. The convergence and accuracy of the proposed control scheme is proved.     

Robust Control of Uncertain Markov Jump Singularly Perturbed Systems

In this paper,we study the robust control for uncertain Markov jump linear singularly perturbed systems(MJLSPS),whose transition probability matrix is unknown.An improved heuris- tic algorithm is proposed to solve the nonlinear matrix inequalities.The results of this paper can apply not only to standard,but also to nonstandard MJLSPS.Moreover,the proposed approach is independent of the perturbation parameter and therefore avoids the ill-conditioned numerical prob- lems.     

高超声速飞行器姿态控制系统设计

研究气动特性是飞行器姿态稳定性的保证,高超声速飞行器采用姿态控制有助于提高作战效能及生存能力.针对高超声速飞行器作战环境复杂,大气密度偏差大、力/力矩系数不准确造成气动参数偏差较大等特点,采用参数空间方法来设计姿态控制系统.首先建立适用于姿态控制系统的高超声速数学模型,在高超声速气动特性条件下,提出三回路姿态稳定控制系统,根据参数空间方法的原理设计出各回路控制器,最后进行仿真分析验证控制系统的性能.仿真结果表明当气动参数存在较大偏差时,采用基于参数空间法设计的高超声速姿态控制系统可以确保对指令的精确跟踪,并且具有较强的鲁棒稳定性.     

Nonlinear Robust Adaptive Deterministic Control for Flexible Hypersonic Vehicles in the Presence of Input Constraint

A nonlinear deterministic robust control scheme is developed for a flexible hypersonic vehicle with input saturation. Firstly, the model analysis is conducted for the hypersonic vehicle model via the input‐output linearized technique. Secondly, the sliding mode manifold is designed based on homogeneity theory. Then an adaptive high order sliding mode control scheme is proposed to achieve tracking for the step change in altitude and velocity for hypersonic vehicles where the uncertainty boundary is unknown. Furthermore, the control input constraint is investigated and another new adaptive law is proposed to estimate the uncertainties and to guarantee the stability of the system with input saturation. Finally, the simulation results are provided to demonstrate the effectiveness of the proposed method.     

Robust Adaptive Synchronization Control for a Class of Perturbed and Delayed Neural Networks

This paper is concerned with the asymptotic synchronization problem of a general neural network using the robust adaptive control technique. It is considered a class of modified Cohen–Grossberg neural networks which is supposed to undergo unknown perturbations caused by state-independent nonlinearities and bounded mixed time-varying delays on neuron amplification and activation functions. An adaptive compensation control strategy is proposed to ensure the elimination of the perturbed and delayed effects by means of adaptive estimations of unknown controller parameters. Through Lyapunov stability theory, it is shown that the proposed adaptive compensation controllers can guarantee the asymptotic synchronization of neural networks without knowing the knowledge of bounds of nonlinearities and delays. A numerical example is provided to illustrate the effectiveness of the developed techniques.     

自适应模糊神经网络控制器设计的线性化方法

基于Ｔ－Ｓ模糊推理系统模型构造一个简化形式的Ｆｕｚｚｙ神经网络（ＦＮＮ），应用Ｓｔｏｎｅ－Ｗｅｉｅｒｓｔｒａｓｓ逼近定理证明了这种ＦＮＮ网络对非线性连续函数的全局逼近性质，并利用Ｃｌａｒｋｅ一步加权最优预报控制性能指标及前向ＦＮＮ网络辨识器模型的线性化思想，提出一种间接Ｆｕｚｚｙ神经网络自适应控制算法，仿真结果证实了该算法的有效性。     

电动车控制系统设计

控制系统是电动车电气系统的核心,采用AVR Mega8作为主控芯片,成功实现了蓄电池能量的制动回馈,大大提高了能量利用效率,在电路板设计方面也有所创新.通过实践检验,该系统运行稳定,性能良好,符合设计要求.     

弹性体高超声速飞行器自适应控制器设计

研究控制器优化设计问题,为能使飞行控制系统精确跟踪控制指令,并抑制住机体的结构弹性振动,研究了一种带自适应结构滤波器的LQR积分控制器.其中,LQR积分状态反馈控制器用来跟踪飞行速度与航迹倾角指令,结构滤波器用来抑制弹性模态.由于在高超声速飞行过程中结构的振动频率与阻尼比是随时间变化的,造成弹性振动偏差大.为此设计了一种鲁棒估计器来在线辨识弹性模态,并将辨识的结构自然频率作为结构滤波器的中心频率.通过在吸气式高超声速飞行器纵向非线性模型上进行仿真验证,结果显示控制方法能很好地满足跟踪指令与结构弹性抑制的要求.