Disturbance rejection in information-poor systems using an adaptive model-free fuzzy controller

Designing controllers to reject disturbances, which cannot be accurately measured or estimated, is a challenging problem in real applications where the behaviour of the system is both non-linear and uncertain. Control schemes based on internal model control (IMC) can only be used to reject disturbances in non-linear systems if they do not affect the relationship between the controlled variable and the control signal i.e. they are additive input disturbances or output disturbances. An accurate model of the relationship between the disturbance and the controlled variable is required in feedforward control schemes. In this paper, an adaptive model-free fuzzy controller is designed to offer good control performance for non-linear uncertain plants in the face of input disturbances which affect the relationship between the controlled variable and the control signal. The controller is a T-S fuzzy model whose output parameters are updated on-line using feedback error learning. Three ways of incorporating the inaccurate measurements of the disturbance are considered :(1) The measured disturbance is one of the inputs of the fuzzy controller (2) The fuzzy controller is used in an IMC scheme, in which the measured disturbance is an input of the internal model. (3) The measured disturbance is fed into both the fuzzy controller and the internal model of the IMC scheme. A computer simulation, which is based on a modified Hammerstein model of the behaviour of the cooling coil in an air-conditioning system, is used to test the performance of each version of the control scheme. Results are presented, which show that feedforward of the disturbance provides the best control.