Search

Space vector pulsewidth modulation (SVPWM) strategies for active neutral-point-clamped five-level (ANPC-5L) inverters have multiple control degrees of freedom and control objectives. However, traditional SVPWM strategies are hard to realize optimal control of multiple objectives as the objectives are coupled with each other. To address this problem, an SVPWM strategy based on model predictive control (MPC) is proposed. In the pulse generation process of the proposed SVPWM, all control degrees of freedom are utilized to achieve optimal control of multiple objectives through MPC. First, voltage vector sequences that meet the requirements of common-mode voltage (CMV) are selected. Then, a cost function is constructed to realize optimal control of the neutral point potential (NPP) and three floating capacitor (FC) voltages. The proposed strategy has advantages such as high control accuracy and producing low CMV. Finally, the effectiveness of the proposed strategy is validated by a 150-kW three-phase ANPC-5L inverter prototype.

In the past decade, adaptive critic designs (ACDs), including heuristic dynamic programming (HDP), dual heuristic programming (DHP), and their action-dependent ones, have been widely studied to realize online learning control of dynamical systems. However, because neural networks with manually designed features are commonly used to deal with continuous state and action spaces, the generalization capability and learning efficiency of previous ACDs still need to be improved. In this paper, a novel framework of ACDs with sparse kernel machines is presented by integrating kernel methods into the critic of ACDs. To improve the generalization capability as well as the computational efficiency of kernel machines, a sparsification method based on the approximately linear dependence analysis is used. Using the sparse kernel machines, two kernel-based ACD algorithms, that is, kernel HDP (KHDP) and kernel DHP (KDHP), are proposed and their performance is analyzed both theoretically and empirically. Because of the representation learning and generalization capability of sparse kernel machines, KHDP and KDHP can obtain much better performance than previous HDP and DHP with manually designed neural networks. Simulation and experimental results of two nonlinear control problems, that is, a continuous-action inverted pendulum problem and a ball and plate control problem, demonstrate the effectiveness of the proposed kernel ACD methods.