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Full-Text Articles in Engineering
Robust Predictive Control Of Nonplanar Fully-Actuated Uavs, Yun Ma, Yuan Wang, Meng Li, Peng Wang, Yanling Tang
Robust Predictive Control Of Nonplanar Fully-Actuated Uavs, Yun Ma, Yuan Wang, Meng Li, Peng Wang, Yanling Tang
Journal of System Simulation
Abstract: Targeting the problem that nonplanar fully-actuated unmanned aerial vehicles (UAVs) are susceptible to external winds and unmodeled dynamics, the predictive control system with good robustness is designed. A nonlinear motion model with six degrees of freedom is established through the Newton-Euler approach. A linear extended state observer is designed to estimate the state variables by transforming the system affected by matched and unmatched disturbances into an equivalent system only affected by the matched disturbances. A predictive controller is designed for the equivalent system to reduce the output oscillation and input surging and a disturbance compensator is also designed to …
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
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 …
Data Driven Pre Tuning Adaptive Subspace Model Predictive Control, Han Pu, Liu Miao, Jia Hao
Data Driven Pre Tuning Adaptive Subspace Model Predictive Control, Han Pu, Liu Miao, Jia Hao
Journal of System Simulation
Abstract: The problem of predictive control is investigated for power plant superheated steam temperature system with the characteristics of large delay, large inertia and time-varying. The data driven pre tuning adaptive subspace model predictive control (PTA-MPC) method, which combines the advantages of subspace identification and state space predictive control, is proposed. The state space models of multiple conditions are obtained by subspace identification with the input signal in persistent excitation. The predictive control law is derived with the state space models, and the controller parameters are optimized by using particle swarm optimization (PSO) algorithm. Based on the least square parameter …