Research On Forward Solution Algorithm Of Stewart Platform For Motion Control

In the control of six degrees of freedom parallel mechanism,the task space control can realize the high-precision control of the platform position and attitude,and the forward solution algorithm is an essential part of the task space control.In order to improve the solution accuracy of the forward solution algorithm of six degrees of freedom parallel mechanism,reduce the iteration time,and realize the high-precision control of six degrees of freedom parallel mechanism,this paper takes the six degrees of freedom platform as the research object to study the improvement and optimization of the forward solution algorithm and the design of the task space controller.First,kinematics and dynamics modeling analysis.The coordinate transformation matrix is obtained based on the six degrees of freedom parallel mechanism coordinate system analysis,and the kinematics inverse model of the platform is established.Its effectiveness and uniqueness are verified through numerical simulation;Analyze the iterative process,advantages and disadvantages of four common forward solution algorithms,and verify their effectiveness;The dynamic model of six degrees of freedom parallel mechanism is built by Lagrange method.Secondly,the improvement and optimization of the forward solution algorithm.Propose an optimal hybrid algorithm based on traditional numerical methods and artificial neural networks,considering the impact of hidden layers on iteration accuracy and time.Select the optimal neural network structure and combine it with numerical methods to obtain the optimal hybrid algorithm through numerical analysis;Propose the intelligent step Newton descent method,add the steepest descent formula in the iterative process of the Newton method,and select the optimal step size through simulated annealing algorithm.Verify the effectiveness and superiority of the algorithm through numerical analysis.Then,improve and optimize the platform control strategy.Aiming at the problem of load uncertainty and external disturbance,RBF neural network adaptive Sliding mode control based on disturbance observer in mission space is proposed;Comparative analysis was conducted on PID control in joint space,computed torque control in task space,and the proposed controller to verify their superiority;Apply different disturbances to the controller and verify its good anti-interference ability through numerical simulation.Finally,the effectiveness and superiority of the proposed forward solution algorithm were experimentally verified.By designing the pose motion trajectory of the experimental platform and comparing the trajectory tracking and error curves before and after the experiment,the effectiveness of the proposed algorithm in practical applications is verified;Set the motion trajectory of the rotation attitude,compare the algorithm with the sensor rotation attitude error,and verify the superiority of the forward solution algorithm in practical applications.