Aug 2026· IEEE Transactions on Cybernetics· Vol PP· 0 citations
Medicine
Abstract
A novel mode-free reinforcement learning (RL) algorithm is proposed for the optimal control of unknown nonlinear system presented by the interval type-2 fuzzy (IT2F) model. The optimal control is converted into a zero-sum game, where the control input and the external disturbance are the opposing competitors. Based on the RL method, a model-based policy iteration (PI) algorithm is constructed to solve the fuzzy stochastic coupled algebraic Riccati equations of nonlinear system. Considering that the dynamic parameter is difficult to obtain completely in real systems, a model-free fuzzy control algorithm is designed under the framework of the RL algorithm. An optimal control of IT2F system is realized without parameter information, only requiring the state and input information. Furthermore, the asymptotic stability with $H_{\infty } $ performance index is ensured by the Lyapunov function. Finally, the effectiveness is tested by the semi-car active suspension model (SCASM).
This paper investigates the safe optimal control for input-constrained unknown stochastic systems via adaptive dynamic programming (ADP) and generalized fuzzy hyperbolic model (GFHM) and the Lyapunov method is utilized to guarantee the uniform ultimate boundedness of the system states and the weight estimation errors o...
Yu-Ling Liang, Feng-Lin Qin, Lei Liu et al.· Neural Networks· 0 citations
The proposed scheme is proven to ensure that all closed-loop signals are practically predefined-time stable with a preassignable settling-time upper bound and the effectiveness of the approach is validated through simulation results on multiple two-link manipulator systems.
Zhou Yang, Chao-Guang Li, Hai-Tao Liu· International Journal of Con...· 0 citations
A hyperbolic function is adopted to convert the unconstrained control command into a bounded signal so that the actuator constraints are strictly respected, and a Lyapunov-based design is carried out to derive the finite-time adaptive control protocol.
A cost function construction method and a critic learning law are proposed, which together guarantee the practical fixed-time stability of the system while overcoming the limitations of existing fixed-time reinforcement learning formulations.
A novel adaptive optimal controller design scheme for continuous-time nonlinear systems through neural network-based policy iteration through Carleman linearization, which allows the optimal feedback control to be derived from a state-dependent Riccati (SDR) equation rather than the Hamilton-Jacobi-Bellman equation.
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