Neural Network Control Of A Class Of Nonlinear Systems With Application To Robotic Manipulators

The robotic manipulator system which includes kinematic model and dynamics model is a class of nonlinear systems and widely used in industrial applications. The tasks of robotic manipulators are described in Cartesian space. Most of the reported control methods need to resolve the inverse kinematic model and transform the tasks into that of the motors of robotic manipulators. And then closed loop control in low level is performed. However, this means open loop control for tasks of robotic manipulators and would degrade the coordination ability of the joints of the robotic manipulator. There are many advanced control methods for robotic manipulators. However, most of them are only simulation cases and lack of experiment process on physics systems. On the other hand, the common control platform of robotics has two kinds:closed platform and open platform. Though the open platform has solved the problems of closed platform, the users have to spend much time to programme except for controller design since the development tools are VC++and other similar programme language. Therefore it is urgent to build a control platform to easily test complex controller.Supported by the "985" project process industry integrated automation innovation platform in Northeastern university, this thesis proposes control methods for robotics manipulators and builds a rapid control platform for robotic manipulators. Furthermore, the given methods are conducted on the presented platform.The main contributions can be summarized as:(1) Three kinds of neural network control methods are proposed to deal with the nonlinear problems in robotic manipulators.(a) The dynamics model of robotic manipulators with linear friction model can be transformed as a class of second order strict feedback nonlinear system. For such systems, the neural networks based linear sliding model control and the neural networks based terminal sliding model control methods are presented. The two methods possess a structure of PD control plus compensator, which can be easily applied in industrial applications. Moreover, finite time convergence of the tracking error can be realized for the neural networks based terminal sliding model control method.(b) The kinematic model and dynamics model of robotic manipulators with linear friction model can be transformed as strict feedback multi-input-multi-output nonlinear system. A neural networks based cascade controller is designed by a backstepping procedure for such system. The advantages of this method are listed as:(i) This method has a cascade structure. Hence, the controller can be put into practice step by step. Moreover, when a subsystem changes, only the related controller is redesigned without the need of redesigning the whole controller, which is easier to be applied than the reported methods for strict feedback systems. (ii) This method adopts nonlinear design tools. Therefore the control performance can be improved and the stability analysis can be critically addressed compared with the cascade control methods in industry.(c) The dynamics model of robotic manipulators with nonlinear friction model can be transformed as a class of nonlinear input output discrete system. PD controller including feedforward and neural network compensator is proposed for such system. If only the input and output signals of the system are known, the complex state observer will be not required, which is easy to be applied in industry applications.(2) A rapid prototype technology based experiment platform is designed and developed. The platform can be fully integrated with Matlab/Simulink. The development of control arithmetic, the real time control code, and data gathering can be performed by Matlab/ Simulink. Complex control methods can easily be carried out in the experiment platform.(3) Three kinds of neural network control methods proposed above are carried out in the experiment platform of the robotic manipulator. The detailed experiment results demonstrate the control performance of the proposed methods compared with other controllers. Moreover, the cascade structure is used to deal with contour following task of the robotic manipulator and a neural network based contour following controller is proposed. This controller can improve the coordination ability of the joints of the robotic manipulator and alleviate the "radius reduction" problem. Hence the machining speed and precision of production can be improved.