Fuzzy–neural Model Predictive Control of Multivariable Processes

Abstract

Predictive control is a model-based strategy used to calculate the optimal control action, by solving an optimization problem at each sampling interval, in order to maintain the output of the controlled plant close to the desired reference. Model predictive control (MPC) based on linear models is an advanced control technique with many applications in the process industry (Rossiter, 2003). The next natural step is to extend the MPC concept to work with nonlinear models. The use of controllers that take into account the nonlinearities of the plant implies an improvement in the performance of the plant by reducing the impact of the disturbances and improving the tracking capabilities of the control system. In this chapter, Nonlinear Model Predictive Control (NMPC) is studied as a more applicable approach for optimal control of multivariable processes. In general, a wide range of industrial processes are inherently nonlinear. For such nonlinear systems it is necessary to apply NMPC. Recently, several researchers have developed NMPC algorithms (Martinsen et al., 2004) that work with different types of nonlinear models. Some of these models use empirical data, such as artificial neural networks and fuzzy logic models. The model accuracy is very important in order to provide an efficient and adequate control action. Accurate nonlinear models based on soft computing (fuzzy and neural) techniques, are increasingly being used in model-based control (Mollov et al., 2004). On the other hand, the mathematical model type, which the modelling algorithm relies on, should be selected. State-space models are usually preferred to transfer functions, because the number of coefficients is substantially reduced, which simplifies the computation; systems instability can be handled; there is no truncation error. Multi-input multi-output (MIMO) systems are modelled easily (Camacho et al., 2004) and numerical conditioning is less important. A state-space representation of a Takagi-Sugeno type fuzzy-neural model (Ahmed et al., 2010; Petrov et al., 2008) is proposed in the Section 2. This type of models ensures easier description and direct computation of the gradient control vector during the optimization procedure. Identification procedure of the proposed model relies on a training algorithm, which is well-known in the field of artificial neural networks. Obtaining an accurate model is the first stage of the of the NMPC predictive control strategy. The second stage involves the computation of a future control actions sequence. In order to obtain the control actions, a previously defined optimization problem has to be solved. Different types of objective and optimization algorithms (Fletcher, 2000) can be used