Adaptive Neural Sliding Mode Control of Active Power Filter

Hindawi Publishing Corporation Journal of Applied Mathematics Volume 2013, Article ID 341831, 8 pages http://dx.doi.org/10.1155/2013/341831 Research Article Adaptive Neural Sliding Mode Control of Active Power Filter Juntao Fei and Zhe Wang Jiangsu Key Laboratory of Power Transmission and Distribution Equipment Technology, College of Computer and Information, Hohai University, Changzhou 213022, China Correspondence should be addressed to Juntao Fei; Received 10 January 2013; Revised 2 April 2013; Accepted 11 April 2013 Academic Editor: Michel Fliess Copyright © 2013 J. Fei and Z. Wang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. A radial basis function (RBF) neural network adaptive sliding mode control system is developed for the current compensation control of three-phase active power filter (APF). The advantages of the adaptive control, neural network control, and sliding mode control are combined together to achieve the control task; that is, the harmonic current of nonlinear load can be eliminated and the quality of power system can be well improved. Sliding surface coordinate function and sliding mode controller are used as input and output of the RBF neural network, respectively. The neural network control parameters are online adjusted through gradient method and Lyapunov theory. Simulation results demonstrate that the adaptive RBF sliding mode control can compensate harmonic current effectively and has strong robustness to disturbance signals. 1. Introduction Active power filters are commonly used to deal with the increasing harmonic current in electrical system nowadays, which can degrade the quality of power system. Since APF is a complicated nonlinear system, advanced controller can be utilized to control the APF. In order to improve the performance of APF, adaptive control, neural network control, fuzzy control, and sliding mode control have been proposed to control the APF. Kömürcügil and Kükrer [1] derived a new control strategy for single-phase shunt APF using a Lyapunov function. Rahmani et al. [2] presented an experimental design of a nonlinear control technique for three-phase shunt APF. Shyu et al. [3] proposed a model reference adaptive control analysis for a shunt APF system. Chang and Shee [4] investigated novel reference compensation current strategy for shunt APF control. Matas et al. [5] developed a feedback linearization way of a single-phase APF via sliding mode control. Valdez et al. [6] designed an adaptive controller for shunt active filter in the presence of a dynamic load and the line impedance. Marconi et al. [7] developed robust nonlinear controller to compensate harmonic current for shunt APF. Since neural network has the capability to approximate any nonlinear function, the tracking control using neural network for nonlinear dynamic system has become a promising research topic. Man et al. [8] derived an adaptive back propagation (BP) neural network controller. Phooi and Ang [9] proposed adaptive RBF neural network training algorithm for nonlinear signal. Lewis et al. [10] designed neural network approaches for robot manipulator. Horng [11] proposed a neural adaptive tracking control of a direct current motor with unknown system nonlinearities where neural network approximation errors are compensated by using the sliding mode scheme. Huang et al. [12] developed a novel RBF sliding mode controller for a dynamic absorber by combining the advantages of the adaptive control, neural network and sliding mode control strategies; this method is well implemented on dynamic absorber, but it has not been implemented on three-phase active power filter before. Neural sliding mode control approaches have been developed for robot manipulators [13, 14]. In [13], self-recurrent wavelet neural networks are used instead of RBF neural networks. And in [14], sliding mode control method is not combined with RBF neural network. Bhattacharya and Chakraborty [15] 2 Journal of Applied Mathematics 𝑖𝐿 𝑖𝑠 Nonlinear load 𝑈𝑠 𝑖ℎ 𝑖𝑐 Main circuit Drive circuit PWM signal Tracking control circuit 𝑖𝑐∗ Command current operation circuit Figure 1: Schematic structure of shunt APF. proposed an ANN-based predictive and adaptive controller for shunt active power filter. Abdeslam et al. [16] designed a unified artificial neural network architecture for active power filters. Kandil et al. [17] developed a novel three-phase active filter based on neural networks and sliding mode control, but RBF neural network and Lyapunov stability analysis are not used in the paper. In our paper, we will design an adaptive controller for shunt active power filter by combining the advantages of adaptive control, RBF neural network, and sliding mode control strategies. Neural network does not depend on mathematical models; sliding mode control has strong robustness. The motivation of this paper is to investigate the combination of adaptive control, neural network control and sliding mode control applied to APF based on Lyapunov analytical method. So it is necessary to combine the advantages of adaptive control, neural network control, and sliding mode control to improve the control performance of APF. In this paper, a Lyapunov adaptive sliding mode control method based on RBF neural network is presented to overcome the shortcomings of traditional methods. The key property of this method is that the weights of neural network can be online adjusted, and the asymptotical stability of the system can be guaranteed by Lyapunov stability theory. The contribution of this paper can be emphasized as follows. (1) The sliding mode technique has been combined with the adaptive control and neural network control to achieve the desired elimination of harmonic current in APF system. The performance of current tracking and total harmonic distortion (THD) can be improved effectively. (2) The adaptive RBF sliding mode controller does not rely on accurate mathematical model since it has the ability to approximate the nonlinear function of APF. The adaptive neural controller is used to model the relationship between the sliding surface and the control law. (3) The adaptive neural network sliding mode control is proposed to deal with nonlinear load in APF system and to improve the performance of current tracking. This is a successful example of using adaptive control, RBF neural network control, and sliding mode control with application to three-phase APF. 2. Dynamics of Active Power Filter The schematic diagram of the three-phase three-wire shunt active power filter is shown in Figure 1. In Figure 1, 𝑖𝑠 is line current, 𝑖𝐿 is nonlinear load current, 𝑖ℎ is harmonic current, and 𝑖𝑐 is compensate current, 𝑖𝑐∗ is command current as the basis of compensate current. The principle of shunt APF is as follows. First, the harmonic cu (...truncated)