Experimental study: Neural network based model predictive control of a distributed parameter system

An experimental set up has been designed for the purpose of spatial property control of a distributed parameter system. The setup consists of a thin metal chip with four temperature sensors along the length of the chip and four electric heaters to regulate the spatial temperature profile of the metal chip. A sequence of random pulse input signals with varying sampling times for the four manipulated inputs were used for live data generation of the transient temperature values at four locations along the metal chip length. Two data-driven models, namely linear state space model and neural network based model are identified and applied for online control. Model predictive control was used to control the spatial temperature profile for set-point tracking and disturbance rejection.