Sliding Mode Control And Parameters Tuning Research Of Two Wheeled Self-balancing Vehicle

Two-wheeled self-balancing vehicle is a typical nonlinear and under-actuated system, to control this systems effectively has always been a difficult problem. The study of self-balancing vehicle control is of great theoretical and practical significance, the research results can be applied to control other under-actuated systems. This paper selects self-balancing vehicle as the research object, further research and innovation has been done on the sliding mode control method, new control strategy is explored, a novel sliding mode controller is designed.The research focus on the balance control of self-balancing vehicle, the self-balancing vehicle controller adopts SMC method, and innovates from two aspects of boundary layer and higher order SMC. Hierarchical SMC and the adaptive higher order SMC were designed respectively, and lots of simulation experiments are done to validate. Hierarchical SMC and the higher order SMC is designed to reduce the chattering of the system and simultaneously to improve the transient characteristics of the controller, the corresponding results have been achieved. In order to overcome the subjectivity and randomness of controller parameters selection, this paper combine intelligent particle swarm optimization (PSO) algorithm to design the controller parameter setting method. Using the artificial intelligence method to select the parameters of the controller can avoid the tedious parameter debugging process, and can guarantee to achieve the best control effect.The paper’s main work and innovation points are as follows:This paper uses Lagrange method to establish the mathematical model of self-balancing vehicle, this method can avoid the complex process of mechanics analysis; using generalized coordinates at the same time can reduce the difficulty of solving system equations.The hierarchical SMC method is introduced to solve the coupling control of the self-balancing vehicle. An overall control law is designed to ensure asymptotic stability of two sub-sliding surfaces including displacement and angle at the same time, theoretical proof and simulation verification are carried out. By using nonlinear power function, the chattering phenomenon of hierarchical sliding mode is suppressed.To solve the problem of chattering better, this paper analysis the inherent chattering of sliding mode method, a new control strategy is explored. Second-order adaptive SMC puts discontinuous control law u on the system, while the controller input u is continuous; this method guarantees to eliminate chattering theoretically.To solve the difficult problem of controller parameters selection, this thesis utilizes artificial intelligence method to set the controller parameters. In order to solve the controller parameter setting problem of multiple variables and multiple peaks in this paper, we design a new kind of nested intelligent particle swarm algorithm. The research uses more scientific and reliable method to assist controller design, thus to ensure better performance of the controller.