Abstract
The application of quadratic optimization and sliding-mode approach is considered for hybrid position and force control of a robot manipulator. The dynamic model of the manipulator is transformed into a state-space model to contain two sets of state variables, where one describes the constrained motion and the other describes the unconstrained motion. The optimal feedback control law is derived solving matrix differential Riccati equation, which is obtained using Hamilton Jacobi Bellman optimization. The optimal feedback control law is shown to be globally exponentially stable using Lyapunov function approach. The dynamic model uncertainties are compensated with a feedforward neural network. The neural network requires no preliminary offline training and is trained with online weight tuning algorithms that guarantee small errors and bounded control signals. The application of the derived control law is demonstrated through simulation with a 4-DOF robot manipulator to track an elliptical planar constrained surface while applying the desired force on the surface.
Highlights
In many robotic applications such as assembly, fine polishing, grasping, grinding, and deburring, the robot comes in extensive contact with its environment
The application of quadratic optimization and sliding-mode approach is considered for hybrid position and force control of a robot manipulator
The dynamic model uncertainties are compensated with a feedforward neural network
Summary
In many robotic applications such as assembly, fine polishing, grasping, grinding, and deburring, the robot comes in extensive contact with its environment. These tasks are better dealt with by directly controlling the forces of interaction between the robot and its environment. The task is to exert a desired profile of force in the constrained degrees of freedom while following the reference trajectory in the unconstrained degrees of freedom. This problem is generally referred to as compliant motion control or impedance control or hybrid position/force control problem
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