A new approach for modeling and control of MIMO nonlinear systems

Modeling of nonlinear systems is implemented using a partial linear modeling (PLM) technique, which separates the linear and nonlinear part of the model. The linear part of the model is in the form of an ARX model, while the nonlinear part is in the form of NNARX model using an RBF neural network. For the RBF neural network, the centers of the network are chosen using an orthogonal least squares (OLS) method. The linear part of the model is constructed to fit and absorb as much as possible the dynamic of the system, while its residuals are fitted using the nonlinear part of the model. The model is tested on experimental data of a MIMO spark ignition (SI) engine system. The plant (SI engine) is handled as a two-inputs, two-outputs process, the two inputs are the ignition timing and the throttle angle, the two outputs are the engine speed and manifold pressure. Different order and nonlinear terms of model are tested on the input-output data to obtain a valid model. Finally, second order with three nonlinear terms of model is found as a fairly accurate model. Model validation is treated using different sets of input-output data which indicates that the resulting model is fairly valid. A feedback linearization method is used for controlling the nonlinear system where the model is constructed using the PLM technique. The smooth transition of the controller output shows that the combination of the modeling and control technique has real potential for real-time implementation