Scalable non-dimensional model predictive control of liquid level in generally shaped tanks using RBF neural network

Abstract

This paper focuses on developing and analyzing a concept of a fully scalable control method applicable to highly nonlinear systems with dynamics varying over the whole working area. The approach is demonstrated on control of liquid level in non-trivial shaped tanks. Non-dimensionalised quantities were used for the development of general geometric model systems of the liquid accumulation in the tanks. Then, training sets were obtained from simulations of the model systems and used for training radial basis function neural network (RBFNN) models. These RBFNNs were implemented in controllers using model predictive control (MPC) method. Both the models and controllers are scalable and applicable in industry or nature. A tentative set of conditions and rules was defined to transfer the solution to practical situations.