Engineering Commons^™

Reinforcement-Learning-Based Adaptive Tracking Control For A Space Continuum Robot Based On Reinforcement Learning, Da Jiang, Zhiqin Cai, Zhongzhen Liu, Haijun Peng, Zhigang Wu

Journal of System Simulation

Abstract: Aiming at the tracking control for three-arm space continuum robot in space active debris removal manipulation, an adaptive sliding mode control algorithm based on deep reinforcement learning is proposed. Through BP network, a data-driven dynamic model is developed as the predictive model to guide the reinforcement learning to adjust the sliding mode controller's parameters online, and finally realize a real-time tracking control. Simulation results show that the proposed data-driven predictive model can accurately predict the robot's dynamic characteristics with the relative error within ±1% to random trajectories. Compared with the fixed-parameter sliding mode controller, the proposed adaptive controller …

Networked Digital Predictive Control For Modular Dc-Dc Converters, Castulo Aaron De La O Pérez

Theses and Dissertations

The concept of power electronics building blocks (PEBB) has driven advancements in highly modularized converter systems with many identical subsystems. PEBBs are distributed subsets of converter systems and thus require communication with a control system for their coordination. For this type of system, the communication latency with hard deterministic deadlines is the driving attribute of communication system requirements. However, inherent communication requirements for PEBB-based converter systems also provide opportunities for coordination of energy flow.

Leveraging developments in Gigabit serial communication channels, a control and communication platform architecture for distributed control schemes based on the 2D-Torus communication network topology was developed …