TY - GEN
T1 - Nonlinear robust optimal control via adaptive dynamic programming of permanent-magnet linear synchronous motor drive for uncertain two-axis motion control system
AU - El-Sousy, Fayez F.M.
AU - Abuhasel, Khaled A.
N1 - Publisher Copyright:
© 2018 IEEE
PY - 2018/11/26
Y1 - 2018/11/26
N2 - In this paper, a nonlinear robust optimal control (NROC) for uncertain two-axis motion control system via adaptive dynamic programming (ADP) and neural networks (NNs) is proposed to improve the robustness against parameter variations and compounded disturbances. The two-axis motion control system is an X-Y table driven by two permanent-magnet linear synchronous motors (PMLSMs) servo drives. The tracking control problem of the nonlinear X-Y table with uncertainties is transformed to a regulation problem. Then, it is solved by an infinite horizon optimal control scheme using a critic NN. Consequently, the NN is developed via ADP learning algorithm to facilitate the online solution of the modified Hamilton-Jacobi-Bellman (HJB) equation corresponding to the nominal system for approximating the optimal control law. The uniform ultimate boundedness of the closed-loop system is proved using the Lyapunov approach and the tracking error asymptotically converges to a residual set. The validity and robustness of the proposed control system are verified by experimental analysis. The control algorithms have been developed in a control computer based on a dSPACE DS1104 DSP control computer. From the experimental results, the dynamic behaviors of the two-axis motion control system using the proposed NROC can achieve robust optimal tracking control performance against parameter uncertainties and compounded disturbances.
AB - In this paper, a nonlinear robust optimal control (NROC) for uncertain two-axis motion control system via adaptive dynamic programming (ADP) and neural networks (NNs) is proposed to improve the robustness against parameter variations and compounded disturbances. The two-axis motion control system is an X-Y table driven by two permanent-magnet linear synchronous motors (PMLSMs) servo drives. The tracking control problem of the nonlinear X-Y table with uncertainties is transformed to a regulation problem. Then, it is solved by an infinite horizon optimal control scheme using a critic NN. Consequently, the NN is developed via ADP learning algorithm to facilitate the online solution of the modified Hamilton-Jacobi-Bellman (HJB) equation corresponding to the nominal system for approximating the optimal control law. The uniform ultimate boundedness of the closed-loop system is proved using the Lyapunov approach and the tracking error asymptotically converges to a residual set. The validity and robustness of the proposed control system are verified by experimental analysis. The control algorithms have been developed in a control computer based on a dSPACE DS1104 DSP control computer. From the experimental results, the dynamic behaviors of the two-axis motion control system using the proposed NROC can achieve robust optimal tracking control performance against parameter uncertainties and compounded disturbances.
KW - Adaptive dynamic programming (ADP)
KW - Hamilton-Jacobi-Bellman (HJB)
KW - Lyapunov satiability
KW - Neural networks
KW - Nonlinear optimal control
KW - PMLSM
KW - X-Y table
UR - http://www.scopus.com/inward/record.url?scp=85059960182&partnerID=8YFLogxK
U2 - 10.1109/IAS.2018.8544612
DO - 10.1109/IAS.2018.8544612
M3 - Conference contribution
AN - SCOPUS:85059960182
T3 - 2018 IEEE Industry Applications Society Annual Meeting, IAS 2018
BT - 2018 IEEE Industry Applications Society Annual Meeting, IAS 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE Industry Applications Society Annual Meeting, IAS 2018
Y2 - 23 September 2018 through 27 September 2018
ER -