Research On Tracking Control Method Of Cushion Service Robot In Interference Environment

With the aging of the population and frequent traffic accidents,more and more people with disabilities suffer from walking disorders.The cushion service robot has seen rapid development in the field of intelligent medical services,because it can assist patients with walking disorders to complete daily activities.However,in practical applications,robots may seriously deviate from the specified trajectory and collide,or run at a fast speed,affecting the safety of users,etc.Therefore,it is of great research significance to control the position state and speed state of cushion service robot.From the perspective of user safety,aiming at the simultaneous constraint of position state and speed state of cushion service robot,this thesis studies the trajectory tracking control under uncertain disturbance environment.The main contents are as follows:Considering the ground friction and gravity shift of the omnidirectional wheel of the cushion service robot,the force analysis of the omnidirectional wheel of the cushion robot was carried out.Combined with the model parameters after the center of gravity shift,the dynamic model of center of gravity shift of cushion service robot considering omnidirectional wheel friction is established.A sliding mode adaptive control method is proposed to solve the problem that the upper bound of the uncertain disturbance is known and unknown.The parameter adaptive sliding mode controller and disturbance adaptive sliding mode controller are designed respectively to weaken the influence of the change of control parameters and disturbance on the tracking process.Lyapunov stability theorem is used to prove that the system can achieve asymptotic stability under the action of the designed controller.MATLAB simulation experiments show that the proposed control method can accurately track the desired trajectory.Considering the safety problem of cushion service robot in uncertain disturbance environment,a full-state constrained trajectory tracking control method is proposed.Radial Basis Function(RBF)neural network is used to approximate the model uncertainties and external uncertain disturbances in the system.The full-state constrained neural network adaptive controller is designed by combining inversion control and obstacle Lyapunov function properties,which ensures that the robot’s motion state is always within the constraint bounds,and all state signals in the control system are uniformly bounded.Lyapunov stability theorem is used to prove that the system can achieve asymptotic stability under the action of the controller and satisfy all state constraints.The effectiveness of the control method is verified by MATLAB simulation.