Robust Mrac Of A Nonautonomous Parabolic System With Spatiotemporally Varying Coefficients And Bounded Disturbance

In this paper, a robust model reference adaptive control of a parabolic system with unknown spatiotemporally varying coefficients and disturbance is investigated. In the adaptive control of time‐varying plants, the derivative of a Lyapunov function candidate, which allows the derivation of adaptation laws, is not negative semi‐definite in general. Under the assumption that the disturbance is uniformly bounded, the proposed robust adaptive scheme guarantees the boundedness of all signals in the closed loop system and the asymptotic convergence of the state error near to zero. With an additional persistence of excitation condition, the parameter estimation errors are shown to converge near to zero as well. Simulation results are provided.     

机器人隐式自适应控制

本文提出了一种隐式机器人自适应逆动态控制方法,不用估计机械手的参数,而根据不确定性界的函数直接修正反馈增益。对于机器人模型中的不确定性,本文从理论上证明了跟踪误差的终结有界性,该方法具有设计方便,计算量小的优点,仿真结果验证了该方法的有效性。     

Robot control and parameter estimation with only joint position measurements

Most adaptive control schemes for rigid robots assume velocities measurements to be available. Although it is possible to measure velocities by using tachometers, this increases costs and the signals delivered may be contaminated with noise. Since the use of encoders allows to read joint position pretty accurately, it is desirable to estimate joint velocities through an observer. This paper presents an adaptive scheme designed in conjunction with a linear observer. Boundedness of the estimated parameters and uniform ultimate boundedness for the tracking and observation errors are guaranteed. Experimental results are included to support the developed theory.     

Variable Upper Bounding Approach for Adaptive-Robust Control in Robot Control

This paper presents a new adaptive-robust control law for robot manipulators with parametric uncertainty. Stability of the uncertain system has been guaranteed using the Lyapunov theory and the control law is derived by means of analytical approach. In this scheme, the manipulator parameters are determined with an estimation law, and both adaptive gain and additional control input are also updated as a function of the estimated value. The proposed adaptive control input includes a parameter estimation law as an adaptive controller and an additional control input vector as a robust controller. The developed approach has the advantages of both adaptive and robust control laws, and besides it eliminates the disadvantages of them.     

神经网络自适应滑模控制的不确定机器人轨迹跟踪控制

提出一种针对机器人跟踪控制的神经网络自适应滑模控制策略。该控制方案将神经网络的非线性映射能力与滑模变结构和自适应控制相结合。对于机器人中不确定项,通过RBF网络分别进行自适应补偿,并通过滑模变结构控制器和自适应控制器消除逼近误差。同时基于Lyapunov理论保证机器手轨迹跟踪误差渐进收敛于零。仿真结果表明了该方法的优越性和有效性。     

基于分层模糊系统的直接自适应控制

为解决模糊控制器中规则数目随系统变量呈指数增长的问题,利用分层模糊系统设计了一类非线性系统的直接自适应模糊控制器,并证明了所提出的设计方法不但能保证闭环系统的一致有界性。而且可使跟踪误差收敛到原点的小领域内,通过对倒立摆控制的仿真研究验证了该方法的有效性。     

机器人轨迹跟踪的间接自适应模糊控制(英文)

为把已有的模糊控制算法应用于多自由度机器人的实时控制中,将间接自适应模糊控制算法从单输入单输出(SISO)系统推广至多输入多输出(MIMO)系统,并给出了系统收敛的严格数学证明.另外对于n关节的机器人轨迹跟踪问题,设计了一种新型控制器能够保证系统的最终一致有界性(u.u.b.).通过对具有远程独立电机驱动的双连杆机械臂的仿真试验证明了该方案的可行性.     

A Frequency-Dependent Direct Adaptive Scheme for Robot Manipulators: A Case of Model-Following Motion Control

This paper presents a new frequency-dependent direct adaptive scheme for the optimal and/or suboptimal tracking of the motion of a prescribed model. The idea is based on a closed-loop control scheme in which an _{2 H} optimal/suboptimal controller is applied in parallel with a direct adaptive technique to guide a robot manipulator to follow a certain prescribed trajectory. The H ₂ compensators have to be proper and positively bounded with respect to all dynamic frequencies. Robustness issues are addresses in the paper by lumping all the nonlinear dynamic terms such as the centrifugal and Coriolis effects as well as the mechanical and electrical friction forces of the robot arm, into a general unstructured uncertainty term.     

Adaptive Task-Space Control of Flexible-Joint Manipulators

This paper considers the motion control and compliance control problemsfor uncertain rigid-link, flexible-joint manipulators, and presents newadaptive task-space controllers as solutions to these problems. The motioncontrol strategy is simple and computationally efficient, requires littleinformation concerning either the manipulator or actuator/transmissionmodels, and ensures uniform boundedness of all signals and arbitrarilyaccurate task-space trajectory tracking. The proposed compliant motioncontrollers include an adaptive impedance control scheme, which isappropriate for tasks in which the dynamic character of theend-effector/environment interaction must be controlled, and an adaptiveposition/force controller, which is useful for those applications thatrequire independent control of end-effector position and contact force. Thecompliance control strategies retain the simplicity and model independenceof the trajectory tracking scheme upon which they are based, and are shownto ensure uniform boundedness of all signals and arbitrarily accuraterealization of the given compliance control objectives. The capabilities ofthe proposed control strategies are illustrated through computer simulationswith a robot manipulator possessing very flexible joints.     

A Neural Network Adaptive Controller for End-effector Tracking of Redundant Robot Manipulators

In this paper we propose a neural network adaptive controller to achieve end-effector tracking of redundant robot manipulators. The controller is designed in Cartesian space to overcome the problem of motion planning which is closely related to the inverse kinematics problem. The unknown model of the system is approximated by a decomposed structure neural network. Each neural network approximates a separate element of the dynamical model. These approximations are used to derive an adaptive stable control law. The parameter adaptation algorithm is derived from the stability study of the closed loop system using Lyapunov approach with intrinsic properties of robot manipulators. Two control strategies are considered. First, the aim of the controller is to achieve good tracking of the end-effector regardless the robot configurations. Second, the controller is improved using augmented space strategy to ensure minimum displacements of the joint positions of the robot. Simulation examples are also presented to verify the effectiveness of the proposed approach.     

一类中立时滞系统的一种改进的鲁棒自适应控制设计

The problem of adaptive robust control is addressed for a class of neutral delay systems. All uncertainties are assumed to be bounded by unknown constants. An improved adaptation law is proposed to estimate the square of these unknown bounds. Then, by making use of the updated values of the squared unknown bounds, an adaptive controller is designed to make the solution of the resultant closed-loop system uniformly ultimately bounded. Furthermore, this method avoids chattering and improves the performance. An example is given to illustrate the effectiveness of this method.     

Switched adaptive tracking control of robot manipulators with friction and changing loads

A switched adaptive controller is designed for robot manipulators with friction and changing loads. The nonlinear friction is depicted by a nonlinear friction model, and a switched nonlinear system is used to model the parameter jump caused by load change. Hyperstability theory is used in the designing procedure, which provides more options for adaptive laws than Lyapunov theory. In the presence of friction and changing loads, asymptotic tracking is achieved under arbitrary switching, which is not able to accomplish by a non-switched adaptive controller. The proposed method is validated by a simulation of a 2 degree of freedom manipulator.     

A Stable Neuro-Adaptive Controller for Rigid Robot Manipulators

In this paper a controller based on neural networks is proposed toachieve output trajectory tracking of rigid robot manipulators. Neuralnetworks used here are one hidden layer ones so that their outputs dependlinearly on the parameters. Our method uses a decomposed connectioniststructure. Each neural network approximate a separate element of thedynamical model. These approximations are used to perform an adaptive stablecontrol law. The controller is based on direct adaptive techniques and theLyapunov approach is used to derive the adaptation laws of the netsparameters. By using an intrinsic physical property of the manipulator, thesystem is proved to be stable. The performance of the controller depends onthe quality of the approximation, i.e. on the inherent reconstruction errorsof the exact functions.     

全局稳定的PD+前馈机器人鲁棒自适应控制

研究应用PD+前馈控制结构的不确定性机器人轨迹跟踪问题.在忽略摩擦力和外部扰动情况下,设计了一大类综合的自适应控制策略,能保证系统全局的渐近稳定;在摩擦力和外部扰动存在时,提出两种新颖的鲁棒自适应混合控制方法,不仅可以保证闭环系统的全局稳定性,同时还能给出系统清晰的暂态性能.严格的理论证明和二自由度机器人的仿真验证了控制器的有效性.