Radial Basis Functional Link Network and Hamilton Jacobi Issacs for Force/Position Control in Robotic Manipulation

Measurement and Control

et al. 2022

The load side and the motor connected by flexible joints in the manipulators’ joint servo system. During the motion of manipulators driven by tendon-sheath, the status change of the end-effector will result in the change of the load side rotational inertia. The perturbation of the inertia of the load side will result in the modeling mismatch of the servo system. So the modeling uncertainty and the system robustness will decrease. An adaptive sliding mode robust control based on HJI (Hamilton-Jacobi-Issacs) theory is proposed in this paper to improve the robustness of the system. Firstly, according to D-H coordinate method, kinematics and dynamics models of the manipulator are established. Then, the basic strategy of adaptive sliding mode robust control is proposed. The variation of control parameters of a single joint of the manipulator is adjusted to reduce the control cost. Next, the sliding mode control law was established through the design of the Lyapunov function based on the HJI theory. The manipulator dynamics model was taken as the research object. The simulation analysis was conducted in uncertain parameters. Finally, a series of manipulator prototype experiments were carried out to proof our control theory. The experiment results show that our method can better solve the model uncertainty caused by the servo system. The adaptive sliding mode robust control strategy based on HJI theory has lower dependence on accurately modeling and stronger robustness.

Section: Introductionmentioning

confidence: 99%

Adaptive sliding mode robust control of manipulator driven by tendon-sheath based on HJI theory

Zhang

Measurement and Control

et al. 2022

International Journal of Advanced Robotic Systems

“…Position control ensures accuracy of the position and attitude of the moving platform, while the force control of redundant actuators is used to adjust the distribution of the driving torque of all actuators. 4,5 For redundant parallel robot, the precision requirements of mechanism in manufacturing process is very high. The control problem of redundant actuated parallel robot has been the key technology and difficulty problem of parallel robot.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fractional-order internal model control algorithm based on the force/position control structure of redundant actuation parallel robot

Wen

Zhang

et al. 2020

Self Cite

This article proposes a new control structure for the complex redundant actuation parallel robot based on force/position hybrid control structure. The traditional proportional–integral–derivative control method, integer-order internal model control method and fractional-filter internal model control–proportional–derivative control method are used in the position structure of force/position hybrid control. The fractional-filter internal model control–proportional–derivative control method is used in the position loop of the permanent magnet synchronous motor to reduce the position error. A fractional-order theory with the internal model control method is used in redundant actuation force control structure, which can improve the control precision of the driving force of the parallel robot. The Admas/Matlab simulation results show that the proposed method outperforms other methods and can obtain good robustness and tracking performance.

“…The simulation results showed the efficiency of the proposed T2FNNP controller. Wen et al [8] proposed an improved radial basis functional (RBF) neural network by means of a hybrid force/position control for robotic manipulation; good stability and transient performance of the system were achieved. Zhao et al [9,10] proposed an adaptive output-feedback control for a class of nonsmooth nonlinear systems and an adaptive fuzzy hierarchical sliding-mode control method for a class of multi-input multi-output unknown nonlinear time-delay systems with input saturation.…”

Section: Introductionmentioning

confidence: 99%

Adaptive PD Control Based on RBF Neural Network for a Wire‐Driven Parallel Robot and Prototype Experiments

Lin

Mathematical Problems in Engineering

et al. 2019

An adaptive PD control scheme is proposed for the support system of a wire-driven parallel robot (WDPR) used in a wind tunnel test. The control scheme combines a PD control and an adaptive control based on a radial basis function (RBF) neural network. The PD control is used to track the trajectory of the end effector of the WDPR. The experimental environment, the external disturbances, and other factors result in uncertainties of some parameters for the WDPR; therefore, the RBF neural network control method is used to approximate the parameters. An adaptive control algorithm is developed to reduce the approximation error and improve the robustness and control precision of the WDPR. It is demonstrated that the closed-loop system is stable based on the Lyapunov stability theory. The simulation results show that the proposed control scheme results in a good performance of the WDPR. The experimental results of the prototype experiments show that the WDPR operates on the desired trajectory; the proposed control method is correct and effective, and the experimental error is small and meets the requirements.