This paper concentrates on the problem of control of a hybrid energy storage system (HESS) for an improved and optimized operation of load-frequency control applications. The HESS consists of a supercapacitor serving as the main power source and a fuel cell serving as the auxiliary power source. First, a Hammerstein-type neural network is proposed to identify the HESS, which formulates the Hammerstein model with a nonlinear static gain in cascade with a linear dynamic block. It provides the model information for the controller to achieve the adaptive performance. Second, a feedforward neural network based on a back-propagation training algorithm is designed to formulate the proportional-integral-derivative (PID)-type neural network, which is used for the adaptive control of the HESS. Meanwhile, a dynamic antiwindup signal is designed to solve the operational constraint of the HESS. Then, an appropriate power reference signal for the HESS can be generated. Third, the stability and the convergence of the whole system are proved based on the Lyapunov stability theory. Finally, simulation experiments are followed through on a four-area interconnected power system to demonstrate the effectiveness of the proposed control scheme.

In this paper, a novel adaptive fault-tolerant sliding-mode control scheme is proposed for high-speed trains, where the longitudinal dynamical model is focused, and the disturbances and actuator faults are considered. Considering the disturbances in traction force generated by the traction system, a dynamic model with actuator uncertainties modeled as input distribution matrix uncertainty is established. Then, a new sliding-mode controller with design conditions is proposed for the healthy train system, which can drive the tracking error dynamical system to a predesigned sliding surface in finite time and maintain the sliding motion on it thereafter. In order to deal with the actuator uncertainties and unknown faults simultaneously, the adaptive technique is combined with the fault-tolerant sliding-mode control design together to guarantee that the asymptotical convergence of the tracking errors is achieved. Furthermore, the proposed adaptive fault-tolerant sliding-mode control scheme is extended to the cases of the actuator uncertainties with unknown bounds and the unparameterized actuator faults. Finally, the case studies on a real train dynamic model are presented to explain the developed fault-tolerant control scheme. The simulation results show the effectiveness and feasibility of the proposed method.

This paper considers sliding mode control design for singular stochastic Markovian jump systems with uncertainties. A suitable integral sliding function is proposed and the resulting sliding mode dynamics is an uncertain singular stochastic Markovian jump system. A set of new sufficient conditions is developed which not only guarantees the stochastic admissibility of the sliding mode dynamics, but also determines all the parameter matrices in the integral sliding function. Then, a sliding mode control law is synthesized such that reachability of the specified sliding surface can be ensured. Finally, three examples are given to demonstrate the effectiveness of the results.

In this paper, a class of nonlinear systems with uncertain parameters is considered. A novel adaptive law is designed to identify unknown parameters under the assumption that the time derivative of some of the outputs is measurable. Then, a sliding-mode observer is proposed to estimate the system state variables. By using the inherent features of sliding-mode observers, a fault-reconstruction scheme is proposed which can be implemented online. The proposed reconstruction signal can approximate the fault signal to any required accuracy even in the presence of uncertain parameters. A simulation example for a magnetic-levitation system is given to illustrate the feasibility and effectiveness of the proposed scheme.