Modeling And Compensation For The Hysteresis In Mechanical Motion System

Hysteresis characteristic widely exists in mechanical and electrical servo systems, suchas the motor shaft side of the joint of motor and load, movement in the sliding area in advancewhen static friction force exists only, friction works together with backlash, hysteresischaracteristics of piezoelectric materials and so on. Hysteresis characteristic is a hardnonlinear characteristic. In the micro mechanical precision machining, the existing hysteresisseriously influenced the machining precision and stability, even lead to a limit circle.Therefore, we need to compensate hysteresis characteristic in the process of actual production.However, on different occasions, hysteresis has many forms different models. And for thehysteresis characteristic, there have variety of compensation control strategies. So, key of thework is to compensate hysteresis characteristic, is to design the controller and parameteradaptive laws, according to the characteristics of the system and model. Then according to thecontroller, we design the simulation and experiment system, through the simulation andexperimental validation, if we have a good tracking performance and convergenceperformance, then the system reached a good compensation result.In this article, we commit our work to the mechanical motion system which containhysteresis characteristic. In the hysteresis modeling and compensation control research, wecomplete several aspects of work as following:Firstly, this paper summarized and analyzed the current research status of hysteresis. Fordomestic and international research status of hysteresis, combined with the relationshipbetween hysteresis and the friction or backlash, and proposed the purpose and significance ofhysteresis compensation.Secondly, we introduced some common hysteresis models here, and discussed thehysteresis compensation method. Also we introduced the SRV02-ET mechanical rotarymotion experiment platform. Then we make mathematical modeling for the platform.Thirdly, in view of the backlash-like hysteresis model, we adopted adaptive sliding modecontrol method. By choosing candidate Lyapunov function, we designed the adaptivecontroller and the adaptive parameters update laws. And then we proved the convergenceperformance of the system. Through the simulation and experiment results of the system, we obtained a perfect stability and tracking performance results, so the controller can satisfy thedesired performance of the system.Fourthly, considering the problems such as parameter uncertainties, model error andexternal disturbance of backlash-like hysteresis model, we adopted the neural networkcombined with the backstepping adaptive compensation control strategy. Through selectedcandidate Lyapunov function and recursived final state feedback controller. Then based on thesystem equations and the design of the Lyapunov equation, we derived the backsteppingadaptive controller and parameter adaptive update laws. Then, through driven the systemsimulation and experimental validation, the curve we obtained showed that system has adesired performance, simulation that the designed controller to meet the compensationrequirements.Fifthly, for Prandtl-Ishlinskii hysteresis model, similarly to the third step, we use therobust adaptive control strategy. It the beginning, we chose candidate Lyapunov function, andthen we derived the controller and parameter updating laws again. In the end, we takesimulation verification. And the simulation results revealed that the effectiveness of thecontrol strategy.Finally, we summarized the hysteresis model and compensation control strategymentioned in the article, and analyzed the advantages and disadvantages of various modelsand compensation strategies. On the basis of previous conclusions, we put forward theproblems to be solved in the continuous research, and ideas to solve them.