A Novel Model Predictive Control Strategy for Continuum Robot: Optimization and Application

It is very important for continuous robots to achieve accurate and rapid control. However, the current continuum robot control faces many challenges. First, they often have complex nonlinear dynamics, including kinematics and dynamics equations, which makes it difficult to build accurate models, and conventional control methods do not work well on these complex systems. Secondly, the motion of a continuous robot system is continuous and coherent, requiring real-time control strategies to maintain stability and accuracy. These problems bring great challenges to the control of continuum robots. In this paper, the nonlinear system of continuum robot is modeled, and then the real-time control of continuum robot is realized by the model predictive control method. The real-time control problem of continuum robot is effectively solved and satisfactory control effect is obtained. In other words, for the continuous robot system, nonlinear modeling is first carried out, and then a new linear model of the system is obtained by linearizing and discretizing the nonlinear system model with feedback linearization method. On this basis, model prediction method is applied to the linearized model to achieve effective control of the target Angle. By solving the constraints of the model predictive control method, the control problem of the target Angle is successfully realized. The simulation also verifies the feasibility of the model predictive control method, which can realize the target Angle approaching the target value quickly. Compared with the traditional PD control method, the superiority of the model predictive control method is also proved.