Research And Design Of Robust Controller For Robot System With Uncertainty

Not only robot is a high complicated time-varied, strong-coupled, nonlinear systems, and subjected to various kinds of uncertainties, such as external disturbances, imprecise measurements, varying load, unmodeled dynamics and so on. Therefore it is difficult to obtain a complete mathematical model of the robot system. During the robot trajectory tracking, some problems, which restrict the development and application of robot, such as the failure of robot components and the solution of inverse kinematics. With the development of society and progress of science, It is important significance in theory and practicability to the realization of high speed, high precision and great capability robots that modeling and solving the above problems.In-depth researched on the uncertain structures and parameters of robotic model in the trajectory tracking some effective control strategies are presented. The main contents can be outlined below:(1) The dissertation gives a list about recent robotic control strategies, Analyze and compares their advantages and disadvantages.(2) A robust adaptive controller with guaranteed transient performance under a desired compensation adaptation law is developed for trajectory tracking control of robot manipulator in the presence of parametric uncertainties and external disturbances. the regressor matrix of adaptive scheme is function of the desired joint position and velocity, so it has best computationally efficient for real-time calculating, compensate nonlinear friction and external disturbances via thought of variable structure, so the global asymptotic stability is ensured.(3) For the chattering problem of routine sliding mode control, this paper proposes a synergetic control algorithm by adaptive neural network and Second order sliding mode control. Design a second order sliding mode control method with novelty and facility, and the chattering problem is avoided effectively, Neutral network is used to adaptive learn and compensate the unknown nonlinear model. The global asymptotic stability is guaranteed.(4) This paper propose synergetic control algorithm by adaptive neural network and variable structure. Neutral network is used to adaptive learn and compensate the nominal model, the desired joint angel values as the input signals of the neutral network, the strict assumption of control inputs about Conventional neural network is solved. The learning algorithm for the free neutral network parameters are presented by Lyapunov direct method. With the control law is designed by combining a dynamic compensator and continuous sliding mode control. The global exponential asymptotic stability is obtained with guaranteed transient performance.All the strategies suggested in this dissertation are proved strictly in theories, their validity are verified in simulation. In addition, the strategies proposed in this dissertation not only can be used in uncertain robot control but also be applied in other analogous nonlinear mechanical systems.