This article is devoted to data-driven event-triggered adaptive dynamic programming (ADP) control for nonlinear systems under input saturation. A global optimal data-driven control law is established by the ADP method with a modified index. Compared with the existing constant penalty factor, a dynamic version is constructed to accelerate error convergence. A new triggering mechanism covering existing results as special cases is set up to reduce redundant triggering events caused by emergent factors. The uniformly ultimate boundedness of error system is established by the Lyapunov method. The validity of the presented scheme is verified by two examples.

This article is dedicated to data-driven control of networked nonlinear systems with event-triggered output. An improved extended state observer is constructed to estimate unknown disturbances. An output estimator is built on the triggered output and the estimated output. Consequently, triggering conditions for single-input single-output and multiple-inputs and multiple-outputs systems are individually proposed by integrating the estimated disturbances, the true and the estimated tracking errors. Sufficient conditions are established to guarantee that the resultant tracking error systems are uniformly ultimately bounded. The proposed strategies are verified by illustrative numerical examples.

This paper is devoted to the control of Markov jump nonlinear systems with general transition probabilities (TPs) allowed to be known, uncertain, and unknown. With the help of the S-procedure to dispose the system nonlinearities and the TP property to eliminate the coupling between unknown TP and Lyapunov variable, an extended bounded real lemma for the considered system to be stochastically stable with the prescribed H _∞ performance is established in the framework of linear matrix inequalities. To handle the nonlinearity incurred by uncertain TP for controller synthesis, a separated method is proposed to decouple the interconnection between Lyapunov variables and controller gains. A numerical example is given to show the effectiveness of the proposed method.

This paper is concerned with the mismatched quantized $H_{\infty }$ output-feedback control of fuzzy Markov jump systems via a dynamic guaranteed cost triggering scheme. An event generator and a quantizer are set up at the sensor-to-controller side and the controller-to-actuator side, respectively. The quantization scheme is presented in terms of a multi-channel configuration with different decoder/encoder parameters. A guaranteed cost dynamic event-triggered mechanism is built on instantaneous and averaged triggering errors, output cost, and preset bounds. A composite controller consisting of a static output-feedback and a nonlinear compensation is constructed to meet the desired system performance. Based on the Lyapunov stability theory, sufficient conditions are obtained such that the closed-loop system is stochastically stable with the prescribed $H_{\infty }$ performance. A structural vertex separation technique and Finsler's Lemma are employed to decouple the control gain, the quantizer parameters and the Lyapunov variable. Finally, the validity of proposed scheme is verified by a circuit example.

This article studies the state-feedback resilient control problem for lateral motion regulation of an intelligent vehicle in the presence of randomly occurring uncertainties. First, the uncertain disturbance in an external yaw moment is compensated by the randomly occurring uncertainty when modeling the vehicle system, and a resilient controller by considering the randomly occurring uncertainty is developed, which is a more general case, but increases complexity of regulation design. Then, the resilient control condition is proposed to guarantee the sideslip angle and yaw rate satisfying the prescribed H _∞ and L ₂ - L _∞ performance indexes. Moreover, H _∞ and L ₂ - L _∞ performance indexes of control outputs are merged into one linear matrix inequality. Finally, an illustrative simulation is given to show the effectiveness of the proposed resilient control design.

This paper is devoted to the fuzzy fault-tolerant tracking control of Markov jump systems with unknown mismatched faults. To reconstruct the faults and the system states, a sequence of proportional-integral observers are established via the system outputs. With the help of a structure separation technique, the proportional-integral gains and the observer gains are solved by a unified linear matrix inequality framework. Resorting to the rebuilt faults and states from an iterative estimation algorithm, a backstepping based fuzzy fault-tolerant tracking control scheme against the mismatched faults is established to make the resultant closed-loop system be uniform ultimate boundedness. Simulations are provided to verify the effectiveness of the proposed methods.