This paper describes the development of a novel state-space model predictive controller. The proposed modelling structure used to capture and predict the nonlinear process dynamics lies on the concept for a neo-fuzzy neuron, deployed in state-space. The introduced approach represents a set of simple fuzzy inferences along the temporal behaviour of each input node, whose dynamics is expressed as a singleton function. The learning algorithm for the proposed modelling structure is realized as a gradient descent procedure. On the basis of the obtained neo-fuzzy state-space model, a fuzzy predictor for the purpose of predictive control is developed. The achieved predictions are used to optimize the future system response by implementing a quadratic programming optimization procedure along the stated controller horizons. The potentials of the proposed approach are studied by simulation experiments to modelling and control of a nonlinear drying plant.
|Number of pages||6|
|Publication status||Published - 1 Jan 2015|
|MoE publication type||A4 Article in a conference publication|
- neo-fuzzy neuron
- predictive control