Adaptive temperature tracking control of microwave heating high titanium slag based on ε-ADP

A data-driven model-based microwave heating adaptive dynamic planning temperature tracking algorithm is proposed for the microwave heating process with time-varying nonlinearity and strong coupling. The microwave heating process is modeled using a single hidden-layer feedforward network and the discrimination capability of an extreme learning machine. Based on this, the performance index functions and control laws are approximated using the approximation capability of the single hidden layer feedforward network and the extreme learning machine. Experimental and simulation results show that the proposed algorithm successfully tracks the temperature of high titanium slag during the microwave heating process.