Adaptive tracking control of uncertain MIMO nonlinear systems with input constraints

In this paper, adaptive tracking control is proposed for a class of uncertain multi-input and multi-output nonlinear systems with non-symmetric input constraints. The auxiliary design system is introduced to analyze the effect of input constraints, and its states are used to adaptive tracking control design. The spectral radius of the control coefficient matrix is used to relax the nonsingular assumption of the control coefficient matrix. Subsequently, the constrained adaptive control is presented, where command filters are adopted to implement the emulate of actuator physical constraints on the control law and virtual control laws and avoid the tedious analytic computations of time derivatives of virtual control laws in the backstepping procedure. Under the proposed control techniques, the closed-loop semi-global uniformly ultimate bounded stability is achieved via Lyapunov synthesis. Finally, simulation studies are presented to illustrate the effectiveness of the proposed adaptive tracking control.     

基于滤波反演法的参数不确定自动引导车的运动控制

针对参数不确定的自动引导车的运动控制问题,应用Backstepping方法设计自适应控制器,并运用Lyapunov稳定性理论与Barbalat定理证明了系统的稳定性;同时利用进化规划算法优化控制器参数,通过跟踪微分器对输入信号与虚拟控制信号进行滤波处理并提取微分信号,避免了对虚拟控制信号的解析求导,简化了控制器的设计过程。与传统PID控制的对比仿真结果表明,所提出的自适应控制策略能较好地补偿系统参数摄动的影响,提高了自动引导车的轨迹跟踪性能和鲁棒性。     

受限的非仿射非线性系统的自适应控制

针对受限的非仿射非线性系统,结合自抗扰思想提出了非仿射系统的扩张状态观测器(ESO)设计,从而将辅助系统设计技巧拓展到了非仿射系统,然后利用反演和指令滤波器设计了自适应控制器,为受限的不确定非仿射系统提供了新的设计思路.为了补偿受限带来的影响,引入了辅助系统,它的状态被用来补偿跟踪误差.指令滤波器用来处理虚拟控制受限问题,同时获得虚拟控制导数的估计,避免了backstepping中对它的繁琐计算,扩张状态观测器被用来估计系统的未知非仿射非线性项和外部干扰.利用输入状态稳定性(ISS)分析了闭环系统的全局一致有界稳定性.最后仿真结果验证了该设计方案的有效性.     

Time-varying sliding mode control for a class of uncertain MIMO nonlinear system subject to control input constraint

To solve the regulator problem of a class of uncertain MIMO nonlinear systems subject to control input constraint, three types of time-varying sliding mode control laws are proposed. The sliding surfaces pass the initial value of the system at the initial time, and are shifted/rotated towards the predetermined ones. The controller parameters are optimized by genetic algorithm (GA). Lyapunov method is adopted to prove the stability and robustness to the parameter uncertainties and external disturbance. By me...     

Adaptive tracking control for multiagent systems with event-triggered communication and asymmetric input saturation

In this paper, the adaptive tracking control problem is investigated for the multiagent systems with event-triggered (ET) communication and asymmetric input saturation. By adopting an auxiliary system, the problem of asymmetric input saturation is successfully handled. Two ET mechanisms are employed in the controller-to-actuator channel and communication channel respectively to economize the limited communication resources. The update frequency of the controller can be reduced by devising a novel switching ET mechanism, which can unify the three existing ET schemes. Based on a backstepping technique, a distributed ET controller is devised, which only requires the sampled value of neighboring states. Due to the discontinuity of the ET state signals, the repetitive differentiation of virtual control laws will not be computed. To solve this problem, the predesigned differentiable partial derivatives of virtual control laws are used to construct the ET virtual control laws. By applying the Lyapunov stability method, it is proved that the desired tracking performance and the stability of the closed-loop system can be guaranteed. Finally, a simulation example demonstrates that the proposed control strategy is effective.     

控制方向未知的SISO非仿射系统间接自适应模糊输出反馈控制

针对一类单输入单输出(SISO)非仿射非线性系统控制方向未知时出现的控制器奇异问题,提出了一种间接自适应模糊控制方案.利用中值定理将非仿射系统转化为仿射系统,通过模糊逻辑系统逼近该仿射系统中的未知函数,并构造模糊控制器,同时利用Lyapunov稳定性定理设计自适应律,最终克服了控制器的奇异问题;在此基础上,通过构造观测器估计跟踪误差,设计输出反馈自适应模糊控制器,解决了状态不可测时系统控制器设计难题,采用Lyapunov稳定性定理证明控制器能使得跟踪误差收敛同时闭环系统所有信号均有界.仿真结果验证了所设计控制方案的可行性与有效性.     

Finite time adaptive filter control of nonlinear systems with actuator faults and state constraints

The finite time adaptive filter control problem is studied for strict-feedback nonlinear systems with actuator faults and state constraints in this paper. First, with the aid of the backstepping technique, the controller and the adaptive laws are designed to compensate the actuator faults and parameter uncertainties. Then, the dynamic surface control is used to establish a first-order filter to alleviate computational burden for the reason of the derivative of virtual control laws. In addition, none of the system states violate predefined constraint boundaries by utilizing a log-type barrier Lyapunov function. Furthermore, the boundedness can be ensured for all the closed-loop signals, and better tracking performance of the system is obtained within a finite-time. Finally, the simulation examples are shown to prove the feasibility and effectiveness of the proposed strategy.     

具有参数不确定性的轮式移动机器人自适应backstepping控制

针对参数不确定的轮式移动机器人的轨迹跟踪问题,设计自适应跟踪控制器.基于移动机器人的动力学模型,采用backstepping积分方法,通过逐步递推选择适当的Lyapunov函数,设计基于状态反馈的自适应控制器,并进行了相应的稳定性分析.与传统PID控制进行仿真对比,结果表明提出的自适应控制策略能较好地补偿系统参数摄动的影响,提高了移动机器人的轨迹跟踪性能和鲁棒性.     

含齿隙非线性的双电机驱动伺服系统控制研究——基于反演积分自适应方法

针对存在齿隙非线性的双电机驱动伺服系统的控制问题,给出了双电机驱动伺服系统的模型,应用反演积分自适应控制方法,引入虚拟控制量和位置跟踪误差的积分,确保系统跟踪误差能够渐近稳定地趋于零。通过逐步递推选择Lyapunov函数,设计了基于状态反馈的自适应控制器,并进行了稳定性分析。通过与常规PID控制的仿真结果相比较,表明所提出的控制策略能较好地补偿齿隙非线性的影响,提高了系统的位置跟踪性能和鲁棒性。     

Adaptive dynamic surface control for disturbance attenuation of nonlinear systems

This paper proposes an adaptive dynamic surface control (DSC) approach for disturbance attenuation of uncertain nonlinear systems in the parametric strict-feedback form. In the proposed control system, a smooth projection algorithm is employed to train uncertain parameters. The proposed DSC system can overcome the complexity of an actual controller caused by the recursive differentiation of virtual controllers and parameter adaptation laws in the backstepping design procedure. From Lyapunov stability analysis, it is shown that the proposed controller has H_∞ tracking performance to attenuate external disturbances     

Adaptive CMAC-based supervisory control for uncertain nonlinear systems.

An adaptive cerebellar-model-articulation-controller (CMAC)-based supervisory control system is developed for uncertain nonlinear systems. This adaptive CMAC-based supervisory control system consists of an adaptive CMAC and a supervisory controller. In the adaptive CMAC, a CMAC is used to mimic an ideal control law and a compensated controller is designed to recover the residual of the approximation error. The supervisory controller is appended to the adaptive CMAC to force the system states within a predefined constraint set. In this design, if the adaptive CMAC can maintain the system states within the constraint set, the supervisory controller will be idle. Otherwise, the supervisory controller starts working to pull the states back to the constraint set. In addition, the adaptive laws of the control system are derived in the sense of Lyapunov function, so that the stability of the system can be guaranteed. Furthermore, to relax the requirement of approximation error bound, an estimation law is derived to estimate the error bound. Finally, the proposed control system is applied to control a robotic manipulator, a chaotic circuit and a linear piezoelectric ceramic motor (LPCM). Simulation and experimental results demonstrate the effectiveness of the proposed control scheme for uncertain nonlinear systems.     

Neural model-based adaptive control for systems with unknown Preisach-type hysteresis

An adaptive control scheme is presented for systems with unknown hysteresis. In order to handle the case where the hysteresis output is unmeasurale, a novel model is firstly developed to describe the characteristic of hysteresis. This model is motivated by Preisach model but implemented by using neural networks (NN). The main advantage is that it is easily used for controller design. Then, the adaptive controller based on the proposed model is presented for a class of SISO nonlinear systems preceded by unknown hysteresis, which is estimated by the proposed model. The hws for model updating and the control hws for the neural adaptive controller are derived from Lyaptmov stability theorem, therefore the semi - global stability of the closed-loop system is guaranteed. At last, the simulation results are illuswated.     

基于虚约束的Acrobot动态伺服控制统一设计方法

提出一种基于虚约束的统一设计方法,以解决Acrobot系统中动态伺服控制问题,使系统沿着经过目标点的周期轨迹运动.将虚约束设计、虚约束作用下系统零动态微分方程分析以及轨道周期性判定相结合,获得了符合目标的周期轨道方程;基于Lyapunov方法设计了光滑反馈控制器,克服了基于线性二次型调节器（LQR）的控制器对零动态微分方程解析解的依赖性问题.实际算例的仿真结果表明了统一设计方法的有效性.     

一类纯反馈力学系统的自适应模糊动态面控制

针对一类纯反馈形式的不稳定力学系统,提出自适应模糊动态面控制方法。在一般动态面控制的设计框架下,引入模糊系统逼近模型的未知函数,设计自适应律在线估计模糊系统权参数和模型未知参数,通过Lyapunov方法证明得出闭环系统半全局稳定。该策略避免了传统反演设计存在的“微分爆炸”现象,并且解决了纯反馈系统控制设计中通常存在的循环设计问题。仿真结果表明,控制系统能够克服不确定性,且能够简单有效地实现跟踪控制。     

Adaptive Backstepping Tracking Control of a 6-DOF Unmanned Helicopter

This paper presents an adaptive backstepping control design for a class of unmanned helicopters with parametric uncertainties. The control objective is to let the helicopter track some pre-defined position and yaw trajectories. In order to facilitate the control design, we divide the helicopter's dynamic model into three subsystems. The proposed controller combines the backstepping method with online parameter update laws to achieve the control objective. The global asymptotical stability (GAS) of the closed-loop system is proved by a Lyapunov based stability analysis. Numerical simulations demonstrate that the controller can achieve good tracking performance in the presence of parametric uncertainties.      

Robust parametric CMAC with self-generating design for uncertain nonlinear systems

In this paper, a robust parametric cerebellar model articulation controller (RP-CMAC) with self-generating design, called RPCSGD, is proposed for uncertain nonlinear systems. The proposed controller consists of two parts: one is the parametric CMAC with self-generating design (PCSGD), which is utilized to approximate the ideal controller and the other is the robust controller, which is designed to achieve a specified H^∞ robust tracking performance of the system. The corresponding memory size of the proposed controller can be suitably constructed via the self-generating design. Thus, the useless or untrained memories will not take possession of the space. Besides, the concept of sliding-mode control (SMC) is adopted so that the proposed controller has more robustness against the approximated error and uncertainties. The stability of the system can be guaranteed surely due to the derivations of the adaptive laws of the proposed RPCSGD based on the Lyapunov function. Finally, the proposed controller is applied to the second-order chaotic system and the one-link rigid robotic manipulator. The tracking performance and effectiveness of the proposed controller are verified by simulations of the computer.     

考虑输入约束的发电机汽门非线性自适应控制

研究具有汽门控制和阻尼系数不确定的单机无穷大系统的暂态稳定性问题. 通过引入切换机制, 解决了输入控制量幅值约束问题. 基于主汽门控制和快速汽门控制思想, 建立了一个由汽门控制的单机无穷大汽轮发电机切换系统模型. 应用本文提出的改进自适应Backstepping方法设计了切换子系统的非线性自适应控制器和参数替换律, 同时构造出切换系统的共同Lyapunov函数, 从而保证了切换系统的稳定性. 由于设计过程中, 未用到任何线性化方法, 因而所得控制器充分利用了系统的非线性特性. 仿真结果表明了设计方法的有效性和优越性.     

基于反馈增益的AUV稳定神经网络反步变深控制

为解决自主水下航行器的变深控制问题,提出一种基于反馈增益的反步控制方法.首先,通过设计控制器参数消除部分非线性项,在保证系统稳定性的同时设计神经网络控制器来补偿纵倾运动中的模型不确定性;然后,通过自适应鲁棒控制器对神经网络的逼近误差予以消除,以加快神经网络的收敛学习速度,神经网络权值和逼近误差估计的学习律可由李雅普诺夫稳定性理论推导得出,保证了闭环系统的一致最终有界性;最后,通过仿真实验验证了所提出方法的有效性.     

双电机驱动伺服系统的反推自适应控制

针对存在未知齿隙等非线性的双电机驱动伺服系统的控制问题,给出了双电机驱动伺服系统的模型,应用反推控制方法,引入虚拟控制量的概念,通过逐步递推选择Lyapunov函数,在不用知道系统内部不确定性参数的前提下,设计了基于状态反馈的自适应控制器,并进行了稳定性分析.通过与常规PID控制的仿真结果比较,表明提出的控制策略提高了系统的位置跟踪性能和鲁棒性.最后在实际系统中进行实验,结果表明所提出的控制策略是有效的.     

非线性系统的神经网络自适应滤波器

针对一类具有未知非线性函数和未知虚拟系数的二阶非线性系统，提出了一种神经网络自适应滤波跟踪方法，用RBF神经网络估计出系统中不确定项，将神经网络与滑模控制方法相结合，用李雅普诺夫稳定性分析方法证明了滤波器内的所有信号均有界，选择的神经网络权值调整规律可以消除颤振，抑制外部随机干扰。