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• A new policy iterative algorithm is studied to design the online H ∞ optimization problems for a class of nonlinear systems. • Without considering knowledge regarding the system dynamics, the PI algorithm is derived to design the relevant H ∞ optimal control law by means of policy evaluation and policy improvement related to the corresponding algebraic Riccati equations. • It also proves the convergence of the novel PI algorithm and two simulation results are given to demonstrate the feasibility and the applicability of the designed algorithms. A novel policy iterative scheme for the design of online H ∞ optimal laws for a class of nonlinear systems is presented. First, neural network-based linear differential inclusion techniques with two multi-layered perceptions are applied to linearize the nonlinear terms. Then, an online partially model-free policy iterative scheme is applied to the linearized system to obtain the design the H ∞ optimal control law. The iterative scheme for the linear H ∞ control problem consists of policy evaluation and policy improvement by means of algebraic Riccati equations. We establish the convergence of the novel policy iterative scheme to the optimal control law. Numerical simulations demonstrating the feasibility and applicability of our design algorithm are provided. [ABSTRACT FROM AUTHOR]

A resources-aware self-triggered finite-time H ∞ controller collaborative design scheme for Markov jump systems (MJSs) is introduced to achieve the purpose of suppressing interference and saving computation resources in the given time interval. To reduce the redundant samplings, transmission and updates, the self-triggered paradigm is presented for MJSs, which determines the next triggered instant, based only on the latest samples and plants information. Considering the transient performance of the system, sufficient conditions to guarantee the finite-time boundedness and the expected interference suppression level of MJSs are also given for the developed resources-aware triggering technique. Moreover, gains of the finite-time H ∞ controller and parameters of the proposed self-triggered paradigm are co-designed. Finally, the proposed theoretical algorithm is applied to the RLC circuit to investigate the validity and superiority of the proposed scheme, over the conventional schemes. [ABSTRACT FROM AUTHOR]

In this paper, a novel complete model-free integral reinforcement learning (CMFIRL) algorithm based fault tolerant control scheme is proposed to solve the tracking problem of steer-by-wire (SBW) system. We begin with the recognition that the reference errors can eventually converge to zero based on the command generator model. Then an augmented tracking system is constructed with a corresponding performance index which is considered as a type of actuator failure. By using the reinforcement learning (RL) technique, three novel online update strategies are respectively developed to cope with the following three cases, i.e., model-based, partially model-free, and completely model-free. Especially, the RL algorithm for the complete model-free case eliminates the constraints of requiring the known system dynamics in fault-tolerant tracking controlling. The system stability and the convergence of the CMFIRL iteration algorithm are also rigorously proved. Finally, a simulation example is given to illustrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]