Explicit nonlinear predictive control algorithms for Laguerre filter and sparse least square support vector machine-based Wiener model

In this paper, three computationally proficient model predictive control (MPC) algorithms for least square support vector machine (LSSVM)-based Wiener model are described. A Wiener model with Laguerre filter as dynamic linear part and LSSVM approximator as nonlinear static part is considered. Even though having excellent approximation abilities, LSSVM suffers from lack of sparseness. A pruning algorithm for LSSVM model is proposed and its comparison is made with classical pruning algorithm. The proposed pruning algorithm is able to remove 99% of support vectors with no remarkable drop in modelling accuracy. Using pruned Wiener model, three computationally efficient MPC algorithms are described. In the first algorithm, linearization of Wiener model is performed at every sampling interval and therefore control vector is determined by carrying out a quadratic optimization task. In the second algorithm, control signal is determined by an explicit control law and parameters of this control law are computed by performing lower-upper (LU) factorization of a matrix and solving linear equations without any online optimization. In the third algorithm, the parameters of explicit control law are calculated directly by another LSSVM approximator, which is trained offline. The advantages and effectiveness of proposed methods are demonstrated on the benchmark pH neutralization reactor. The control performance and computational efficiency of proposed algorithms are compared with computationally complex nonlinear MPC, which repeats a nonlinear optimization task at every sampling interval. The impact of pruning on model accuracy, computational efficiency and control accuracy is also discussed.

Model Predictive Individual Pitch Control using Laguerre Filter for Load Mitigation in Wind Turbine

This paper focuses on variable speed variable pitch wind- turbine control when operating in region 3. Designing a pitch controller is important while operating in region 3 to regulate the rated generator power and to reduce the flap-wise moment on the turbine blades. The regulation of the generator power and speed is achievable by using the collective pitch control (CPC); while reduction of the flap-wise moment is the objective of individual pitch control (IPC). The main challenge of designing this pitch controller is the pitch-angle constraints. Model predictive control (MPC) using Laguerre network is designed to produce the optimal individual pitch control action that satisfies the system constraints. A typical 5-MW benchmark wind turbine simulator is used to test the performance of the proposed controller. Comparisons between the proposed controller and the standard PI controller, which has been employed generously for wind-turbine control in industry, are performed. The results show the superiority of the proposed pitch controller over the standard controller.

Adaptive Equalizer for DS-CDMA Multi-User Communication System Using Sign-Algorithm Based on Laguerre Filter Structure

This paper proposes a sign-algorithm based on adaptive Laguerre filter structure for improving the performance of the direct sequence-code division multiple access (DS-CDMA) communication system. The proposed adaptive equalizer employs a combination of sign and least mean square (LMS) algorithms to minimizing the effect of inter-symbol interference (ISI) that arises due to multipath propagation, and the computational complexity of equalizer. The performance of proposed equalizer was compared with traditional equalizers such as LMS and RLS algorithms in terms of varying path. The function detail and performance results of the proposed equalizer are described by computer simulations in terms of the bit error rate (BER).