Motion Control Using On-Line Simulation and Rule Based Control

Abstract

This paper presents an adaptive-predictive algorithm applied in motion control. The basic idea of the algorithm is the on-line simulation of the future behavior of the control system, by using a few candidate control sequences. Then, using the rule based control, these simulations are used to obtain the 'optimal' control signal. The efficiency and applicability of the proposed algorithm in motion control are demonstrated through applications. I. INTRODUCTION Today, many industrial systems are still controlled by simple PID algorithms, despite the better performances usually provided by systems developed following the modern control theory. This is probably not only due to the quite surprising efficacy of this simple control method, but also to the higher computational load and design effort required by most of the more sophisticated control techniques. PID controllers can be used to control a wide range of different processes, need only rough process models to be easily tuned and give pretty good set-point tracking performances (1). On the other hand it is clear that PID performances, although satisfactory, could be improved when dealing with highly nonlinear processes, or processes featuring unmodeled dynamics and external disturbances. This is especially needed in those applications where highest accuracy is required, like in robot manipulator joint position control. The closed mechanical chains make the dynamics of parallel manipulators coupled and highly nonlinear. To minimize the tracking errors, the dynamical forces need to be compensated by the controller. This control should ensure the best possible compliance of planned trajectory with taking into account the maximum available torques of the drives and effective cooperation between drives. To overcome such restrictions, many advanced control strategies have been developed in the past, showing that a good control schema could both ease the design and improve performances over process variations from the model. An adaptive-predictive controller is described by using a model to compute the predicted process outputs. The parameters of model are obtained through an identification algorithm. Also, a cost function related to the closed loop performance of the system is defined, and the control signal is obtained by means of minimization the cost function. Finally, the first of these signals is applied in the process (2). The performance of an adaptive-predictive controller could become unacceptable due to a very inaccurate model, thus requiring a more accurate model. This task is an instance of closed-loop identification and adaptive control. The difficulty of closed-loop identification is that the input of process to be identified is not directly selected by the designer but ultimately by the feedback controller. A solution to increase the performances is to use multiple models (3). Usually, the cost function is defined by using the output prediction error relative to the system setpoint and the weighted control signal: