首页|All state constrained decentralized adaptive implicit inversion control for a class of large scale nonlinear hysteretic systems with time-delays
All state constrained decentralized adaptive implicit inversion control for a class of large scale nonlinear hysteretic systems with time-delays
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NSTL
Elsevier
This paper proposes an all state constrained decentralized adaptive implicit inversion control scheme for a class of large scale nonlinear systems with unknown time delays and asymmetric saturated hysteresis. First, to address the states constrained problem, the asymmetric barrier Lyapunov function is introduced to keep the error surface within an appropriate range from the view of engineering practice to ensure the performance and safety, such as for attitude tracking of rigid spacecraft, for spacecraft approach and intersection. Second, the transmission delays between different subsystems are considered and approximated through the incorporation of the neural-network approximators and the finite coverage lemma. Third, a new hysteresis implicit inverse algorithm is designed to effectively mitigate asymmetric and saturated hysteresis nonlinearities. It should be noted that the implicit inverse implies that the analytical inverse of the asymmetric and saturated hysteresis is not required. Instead, the decoupling algorithms are designed to extract the actual control signal from the temporarily hysteretic control signal , which reduces the preliminary work of the control algorithm. Finally, all of the signals in the closed-loop system are proved to be semi-globally ultimately uniformly bounded and the tracking errors converge to an arbitrarily small residual set. The experimental results on two-machine excitation power systems in the hardware-in-loop system are presented to illustrate the effectiveness of the proposed scheme. (c) 2021 Elsevier Inc. All rights reserved.
Adaptive dynamic surface controlAdaptive modified implicit inverse controlAsymmetric and saturated hysteresisAll state constrainedOUTPUT-FEEDBACK CONTROLTRACKING CONTROLIMPROVED RAZUMIKHINPERFORMANCE
NSTL
Elsevier
