Research and Experiment on Dynamic Weight Pseudo-Inverse Control Allocation for Spacecraft Attitude Control System

IEEE Trans. Contr. Syst. Technol.

Zhao

Yang

2021

Self Cite

Section: B Hardware-in-loop Experimental Resultsmentioning

confidence: 99%

Reinforcement Learning-Based Approximate Optimal Control for Attitude Reorientation Under State Constraints

IEEE Trans. Contr. Syst. Technol.

Zhao

Yang

2021

Self Cite

“…Accordingly, the cost function ( 14) is redefined by (26), and HJB equation ( 17) can be rewritten as ( 27)…”

Section: Augmented Term and Learning Law Designmentioning

confidence: 99%

“…5) Other hardware and software including power modules and monitor interface. Other detailed information can be found in [26]. The proposed controller of the spacecraft attitude reorientation can be tested and illustrated by this HIL platform.…”

Section: A Experimental Platformmentioning

confidence: 99%

ADP-Based Spacecraft Attitude Control Under Actuator Misalignment and Pointing Constraints

Yang

IEEE Trans. Ind. Electron.

et al. 2022

Self Cite

“…Besides, this initial controller can be easily described by a set of polynomial basis functions: (32).…”

Section: Construction Of the Initial Control Policymentioning

confidence: 99%

“…2) A reliable real-time simulation computer. Particularly, the weighted pseudo-inverse algorithm designed in [32] is employed for control allocation. 3) An ARM-based underlying control PCB.…”

Section: B Hardware-in-loop Experimentsmentioning

confidence: 99%

Learning-Based Attitude Tracking Control With High-Performance Parameter Estimation

IEEE Trans. Aerosp. Electron. Syst.

Zhao

et al. 2022

Self Cite

This paper aims to handle the optimal attitude tracking control tasks for rigid bodies via a reinforcement learning-based control scheme, in which a constrained parameter estimator is designed to compensate system uncertainties accurately. This estimator guarantees the exponential convergence of estimation errors and can strictly keep all instant estimates always within pre-determined bounds. Based on it, a criticonly adaptive dynamic programming (ADP) control strategy is proposed to learn the optimal control policy with respect to a user-defined cost function. The matching condition on reference control signals, which is commonly employed in relevant ADP design, is not required in the proposed control scheme. We prove the uniform ultimate boundedness of the tracking errors and critic weight's estimation errors under finite excitation conditions by Lyapunov-based analysis. Moreover, an easy-to-implement initial control policy is designed to trigger the real-time learning process. The effectiveness and advantages of the proposed method are verified by both numerical simulations and hardware-in-loop experimental tests.