Tracking Control of Fully-Constrained Cable-Driven Parallel Robots using Adaptive Dynamic Programming

In this paper, a new adaptive tracking controller with learning ability is proposed for fully-constrained cable-driven parallel robots (CDPRs). For these systems, the necessity of maintaining positive and bounded tensions in all cables while coping with disturbances represents a critical control requirement. To achieve this goal, we propose a control law based on adaptive dynamic programming (ADP), with an actorcritic structure. In the critic part, an artificial neural network (NN) approximates the value function which is to evaluate the system performance; in the action part, the controller’s parameters are tuned online to achieve optimal control performance. Additionally, the anti-windup (AW) technique is combined with the adaptive controller to cope with the input saturation problem. The stability of the closed-loop system with the proposed control algorithm is proved using the Lyapunov method. Numerical simulations show the effectiveness of the proposed controller.

Authors

• Shuai Li
• Damiano Zanotto

Keywords

• Parallel robots
• Trajectory tracking
• System performance
• Optimal control
• Artificial neural networks
• Numerical simulation
• Stability analysis
• Dynamic programming
• Numerical stability
• Cables
• Tracking Control
• Adaptive Programming
• Adaptive Dynamic Programming
• Parallel Robot
• Cable-driven Parallel Robots
• Neural Network
• Numerical Simulations
• Value Function
• Artificial Neural Network
• Stability Of System
• Adaptive Control
• Critical Part
• Input Saturation
• Adaptive Tracking Control
• Degrees Of Freedom
• Cost Function
• Diagonal Matrix
• Linear System
• Control Structure
• Adaptive Law
• Adaptive Control Law
• Positive Definite Matrix
• Tracking Error
• Disturbance Vector
• Rigid Linker
• Definite Matrix
• Reward Function
• Extended State Observer
• Chain Rule
• neural networks
• anti-windup
• tracking control.