Intelligent robust control of precision positioning systems using adaptive neuro fuzzy inference system

Abstract

Recently, there has been an increasing interest in the application of robust control theory for Precision Positioning Systems (PPS). This is mainly driven by the need to provide guaranteed stability in spite of uncertainties and disturbances associated with these systems. However, robust control techniques require a dynamic model of the plant under study and bounds on modelling uncertainty to develop control laws with guaranteed stability. Although identification techniques for modelling dynamic systems and estimating model parameters are well established, very few procedures exist for estimating uncertainty bounds. A conservative bound is usually chosen to ensure robust stability for a reasonable range of variations about the nominal model. Nevertheless, high performance requirement of PPS will be severely affected. In this research an intelligent uncertainty function is developed to improve the performance of H‡ robustly controlled high precision positioning system in terms of reduced conservatism. The proposed approach can be systematically applied. First, the nominal model of the positioning system is identified; output performance and control signal requirements are then determined by proper selection of performance and control weighting functions. Adaptive Neuro Fuzzy Inference System (ANFIS) is used to produce the uncertainty bounds of model uncertainty that results from unmodeled dynamics and parameter variations. The synthesis of the H‡ controller will incorporate these weighting functions. Then to further improve the controlled system performance, an unconstrained optimization procedure is developed to obtain the best possible performance weighting function. Moreover, an intelligent disturbance weighting function is developed to eliminate the effect of crosstalk between the axes. v-gap metric is utilized to validate the identified uncertainty set for robust controller design. ƒÊ-analysis is used to evaluate the robustness of the system. The computational time and number of iterations of the proposed intelligent estimation method are decreased to < 0.1 of that required by a neural network method with less or equal v-gap metric value. Simulation and experimental results using different servo motion plants reveal the advantages of combining intelligent uncertainty identification and robust control. Improved performance has been achieved for rotational motion, single axis and two-axis servo systems. Settling time <0.8 seconds, rise time < 0.5 and steady state error within sensor resolution are achieved for the rotational motion system. In the case of the X-Y positioning systems, tracking errors are reduced to less than 100% of that obtained using a well tuned conventional PID controller and less than 10% of that obtained using a nominal H‡ robust controller. v-gap metric value of <1.0 and larger stability region can be readily obtained for both cases. Robust stability and performance are also guaranteed. The generality of the problem formulation enables the application for more complicated systems.