Yaozhen Han

In this paper, the adaptive robust simultaneous stabilization problem of two ships is studied. Firstly, the water surface three-degree-of-freedom ship models are transformed into port-controlled Hamiltonian (PCH) models. Using a single output feedback controller, the two PCH systems are combined to generate an enhanced PCH system based on Hamiltonian structural attributes. Then, considering the situation with both external interference and structural parameter perturbation in the systems, an adaptive robust output feedback controller is designed to stabilize the two systems simultaneously. Finally, the effectiveness of the controller proposed in this paper is illustrated by a simulation example.

In a wind turbine system, a doubly-fed induction generator (DFIG), with nonlinear and high-dimensional dynamics, is generally subjected to unbalanced grid voltage and unknown uncertainty. This paper proposes a novel adaptive-gain second-order sliding mode direct power control (AGSOSM-DPC) strategy for a wind-turbine-driven DFIG, valid for both balanced and unbalanced grid voltage. The AGSOSM-DPC control scheme is presented in detail to restrain rotor voltage chattering and deal with the scenario of unknown uncertainty upper bound. Stator current harmonics and electromagnetic torque ripples can be simultaneously restrained without phase-locked loop (PLL) and phase sequence decomposition using new active power expression. Adaptive control gains are deduced based on the Lyapunov stability method. Comparative simulations under three DPC schemes are executed on a 2-MW DFIG under both balanced and unbalanced grid voltage. The proposed strategy achieved active and reactive power regulation under a two-phase stationary reference frame for both balanced and unbalanced grid voltage. An uncertainty upper bound is not needed in advance, and the sliding mode control chattering is greatly restrained. The simulation results verify the effectiveness, robustness, and superiority of the AGSOSM-DPC strategy.

Grid-connected and islanding operations of a microgrid are often influenced by system uncertainties, such as load parameter variations and unmodeled dynamics. This paper proposes a novel adaptive higher-order sliding mode (AHOSM) control strategy to enhance system robustness and handle an unknown uncertainty upper bounds problem. Firstly, microgrid models with uncertainties are established under islanding and grid-connected modes. Then, adaptive third-order sliding mode and adaptive second-order sliding mode control schemes are respectively designed for the two modes. Microgrid models’ descriptions are divided into nominal part and uncertain part, and higher-order sliding mode (HOSM) control problems are transformed into finite time stability problems. Again, a scheduled law is proposed to increase or decrease sliding mode control gain adaptively. Real higher-order sliding modes are established, and finite time stability is proven based on the Lyapunov method. In order to achieve smooth mode transformation, an islanding mode detection algorithm is also adopted. The proposed control strategy accomplishes voltage control and current control of islanding mode and grid-connected mode. Control voltages are continuous, and uncertainty upper bounds are not required. Furthermore, adjustable control gain can further whittle control chattering. Simulation experiments verify the validity and robustness of the proposed control scheme.

Power optimisation is quite important for the doubly‐fed induction generator (DFIG)‐based variable speed wind turbine (VSWT) in the modern renewable power generation system. However, the VSWTs are generally non‐linear and uncertain systems. This study proposes a super‐twisting second‐order sliding mode (SOSM) control scheme to maximise the wind energy capture of a DFIG‐based VSWT system, and regulate the stator reactive power to follow the grid requirements. By regulating the generator rotor voltage, the designed SOSM controller makes the wind turbine rotor speed track the optimal speed to maximise the power generation, and controls the rotor current to follow the external reference to regulate the stator reactive power. A quadratic form Lyapunov function is adopted to determine the range of controller parameters and guarantee the finite time stability. Simulation results on a 1.5 MW DFIG‐based VSWT demonstrate the effectiveness of the proposed control strategy.