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On Robust Tracking Control Strategies For Multi-link Robot Manipulators

Posted on:2016-12-07Degree:DoctorType:Dissertation
Country:ChinaCandidate:S X WangFull Text:PDF
GTID:1108330464469543Subject:Control theory and control engineering
Abstract/Summary:PDF Full Text Request
Robot is a highly complicated system with time-varying, strong coupling and nonlinear dynamic characteristics. It also contains various uncertain factors, such as unmodeled dynamics, parameter measurement errors, load changes, external disturbances and the friction between joints, etc. So, it is unable for us to obtain the accurate dynamic model of robot. These uncertainties will affect the control performance and dynamic quality of the system seriously. Especially for the high precision, high performance and high speed robot system, the control studies for uncertain robot with modeling error and external disturbance are particularly important.Having investigated the trajectory tracking control for uncertain robot with modeling error and external disturbance, some effective control strategies and control methods are presented. The main contents are as follows:1. The dynamic model of rigid robot manipulator and its basic properties are introduced respectively, and some important mathematical concepts and definitions are also given for the rest study. Then, we study the robust trajectory tracking control problem of rigid robot system. In order to suppress the influence of nonlinear, time-varying, load change and other uncertain factors on the tracking quality of the robot, two robust control strategies based on neural network are designed. One is the robust tracking control strategy based on neural network for the uncertain robotic system. The robust compensation controller is designed to eliminate the influence of the uncertainties caused by the model parameters and disturbance, then a neural network is employed to learn the unknown upper bound of the uncertainty because it has the ability to approximate any function with any precision. We proved that the closed-loop error system is asymptotically stable. Finally simulation results show the proposed control scheme has a good robustness and control performance.The other one is a novel robust tracking control scheme for robot manipulator based on fuzzy neural network. The proposed control strategy consists of a robust compensating controller to eliminate the influence of the uncertainties, a RBF neural network to learn the unknown upper bound of system uncertainties, and a fuzzy logic controller to adjust the width of boundary layer of the robust compensator. The control strategy reduces the chattering control signal effectively.2. Two indirect adaptive fuzzy robust tracking control schemes are presented. First, an adaptive fuzzy H∞ robust control scheme is presented for a class of uncertain continuous-time multi-input and multi-output nonlinear dynamic systems. Within this scheme, fuzzy systems are employed to approximate the plant’s unknown nonlinear functions and H∞ robust control term is used to compensate for approximation errors and external disturbances. Secondly, an adaptive fuzzy compensation robust control strategy is proposed. Adaptive fuzzy systems are employed to approximate the plant’s unknown nonlinear functions, all parameter’s adaptive laws and robust control terms are derived based on Lyapunov stability analysis. By doing so, the stability and asymptotic convergence of tracking errors to zero can be guaranteed.The effectiveness of the proposed control scheme is demonstrated by the simulation on a two-link robot manipulator.3. For the parameter uncertainties and external disturbance of the robotic system, two adaptive robust tracking control strategies are presented. Firstly, the proposed control law is comprised of a computed torque controller, which is introduced to guarantee the nominal system asymptotically track the desired trajectory; an adaptive robust controller, the robust control law is employed to compensate the effects of the uncertainties, which can be adjusted by adaptive law. Secondly, a neural network based robust adaptive trajectory tracking control scheme is proposed. In the presented control method, RBF neural network is employed to approximate the model uncertainties, the robust controller is designed to eliminate the effect of the approximation error and an adaptive control is used to adjust the parameter of robust controller automatically. Finally, simulation results are presented to demonstrate the effectiveness and priorities of the proposed control scheme.4. Aiming at the flexible-link manipulator suppress the beam elastic vibration while precisely track a desired trajectory, the paper decouples the flexible manipulator system into rigid motion and flexible vibration two different time scale subsystems based on singular perturbation theory and presents two simple composite control schemes. For the rigid motion, i.e. the slow subsystem, a variable structure controller and a PD controller is employed to track the desired trajectory, for flexible vibration, i.e. the fast subsystem, an optimal controller is used to suppress the vibration. The simulation results demonstrate the effectiveness of the proposed control strategy. Furthermore, the impact of the joint inertia on the control performance of flexible manipulator is analyzed and discussed.Finally, the main research contents and innovation points are summarized, and the problems for further research are prospected.
Keywords/Search Tags:robot manipulator, trajectory tracking, neural network, adaptive fuzzy control, adaptive robust control, variable structure control, singular perturbation, vibration suppression
PDF Full Text Request
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