Control Of Position System Based On Sliding Mode Theory And Friction Compensation

With the development and technology progress of control theory,electronic technology,machine manufacturing technology and related field, high accuracy motion control is widely used in industrial robot,ultra-precision machine tool and semiconductor manufacturing, etc. High accuracy can improve the quality of the product, hence, the research and development of motion control technology can bring enormous economical benefit for social economical construction.For position motion control with high tracking accuracy and smooth movement performance, the existence of friction is the obstacle to improve the system motion performance, it will not only cause steady-state tracking errors, but also produce crawl and limit cycle oscillation of system. Friction is a very complex nonlinear phenomenon, which includes hysteresis,stick-slip,Stribeck effect and velocity dependence, and has the time varying characteristic. Therefore, understanding the property of friction, thorough analysis of the influence of nonlinear friction on dynamic performance of control system and eliminating the negative impact will bring aspiring effect on the improvement of system performance.When we investigate the friction issue, two main methods are involved, which are hardware technique and software technique. Hardware technique mostly includes mechanical design, which focuses on how to work out low frictional mechanical parts, exemples include the adoption of high quality lubrication oil, high precision roll guideway, well lubricated,special dense ball bearing, which are used to reduce friction, this hardware method can reduce nonlinear friction from root, however, it will increase the equipment cost, so it is not applicable in terms of economy and has some limitation; another method takes software into consideration, which starts with the modeling of friction and the friction compensation, study how to overcome nonlinear friction, design motion control strategies and friction compensation methods, which is significantly meaningful to the improvement of system control accuracy and economical benefits. Sliding mode control (also called variable structure control), is proposed by Soviet researcher Emelyanov in the 1950s, then this theory is further developed by Utkin and Itkis et al.. The sliding mode motion of sliding mode control is invariant to system disturbance and perturbation, which is helpful to overcome the nonlinear friction in position control system.Thus, we will investigate the influence of nonlinear friction on position motion control in terms of software technique, mainly explore and study how to overcome the effect of friction when the system run at low speed, and design motion control strategies and friction compensation methods based on sliding mode control theoty. The main works in this thesis can be introduced as follows:1. A kind of fuzzy sliding mode compensation method is designed for nonlinear position control system, comparision analysis is conducted about the influence of different inputs of controller on system performance, the validity of controller is verified through experiments.2. An adaptive sliding mode friction compensation method is proposed, this method makes use of terminal sliding mode idea to design sliding mode function, which makes the tracking error converge to origin point in finite time and avoid the problem of conventional sliding surface that tracking error will not converge to origin point. Based on the characteristic of the exponential form friction, the sliding mode control law including the estimation of friction parameter is obtained through terminal sliding mode idea, the on-line parameter update laws are obtained from Lyapunov stability theorem, simulation results show the validity of the compensation method.3. The system parameters uncertainties are considered, a novel radial basis function (RBF) neural network sliding mode control method is proposed. A single input double output RBF neural network estimator is used to estimate this uncertain dynamics online, the weights of RBF neural networks are derived in the sense of Lyapunov stability theorem. Experiment results show the superiority of the proposed RBF neural network sliding mode control method.4. An adaptive fuzzy sliding mode control method based on genetic algorithm (GA) is proposed. An integral type sliding mode function is adopted, the ideal control law is obtained from sliding mode theory. Since this ideal control law is difficult to obtain in advance due to parameter uncertainties and external disturbance, we use adaptive fuzzy system to approximate the ideal control law, the controller proposed includes adaptive fuzzy sliding mode control part and compensation control part, in which the adaptive fuzzy sliding mode control part is designed to approximate the ideal control law and the compensation control part is adopted to compensate the approximation error between ideal control law and adaptive fuzzy sliding mode control part, Lyapunov stability theorem is adopted to derive the online parameter update laws of fuzzy rules and the upper bound of approximation error, the GA is used to find the optimal parameter update laws. The experiment results show the efficiency of the proposed method.