The multivariable optimal control of a wind turbine by an approach based on incremental state model is proposed. The advantages of incremental state model in comparison with the non incremental one are that the control action cancels steady state errors and incremental state solves the problem of computing the target state, choosing zero as an objective. Linear Quadratic Regulator (LQR) and optimal state observer are applied. The effectiveness of the proposed control method, over the non incremental one, is examined by applying the linear controllers to the nonlinear wind turbine model. The results show that incremental LQR control presents good transient response and zero steady state errors, even in presence of disturbances, nonlinearities and modelling errors.

In this work, a new method for Takagi-Sugeno (T-S) fuzzy modelling based on multidimensional membership functions (MDMFs) is proposed. It is verified that the fuzzy inference method of one-dimensional membership functions (lDMFs) may place the fuzzy rules in inappropriate locations for modelling of nonlinear multivariable systems, while the application of MDMFs allows a better identification through a smaller number of fuzzy rules. The proposed method uses a genetic algorithm (GA) for the adjustment of the MDMFs and the T-S method for modelling and identification of the nonlinear system. As a validation example, a nonlinear multivariable system, a coupled tanks system, is chosen. The results show that the proposed method presents less identification error than the T-S method, with less number of fuzzy rules.

In this work, a new multi-strategy fuzzy control (MSFC) based on fuzzy fusion techniques for the control of multivariable nonlinear systems is proposed. This new nonlinear control method is a mixture of different control techniques by fuzzy interpolation using the fuzzy Takagi-Sugeno model. It combines various control strategies to achieve smooth transient responses and zero steady state error. The following techniques are merged within the proposed MSFC structure: an optimal state feedback control that yields an optimal transient response, optimal control through an incremental state model that returns a zero steady state error, and a constant input control to maintain the system behavior within predefined boundaries whenever the MSFC method is applied.