No abstract

No abstract

No abstract

No abstract

Microwave heating has been widely used for its advantage of volumetric heating. For the material exposed by two input sources, due to microwave superposition property, different phase between the input sources can generate different power distribution. To achieve a uniform temperature rising process, a sliding mode neural network combined with Cuckoo Search algorithm is constructed. By regarding the power dissipation at each sampling point as an independent actuator, a sliding mode neural network control method is used to get the suitable inputs for those sampling points under uncertain process parameters. As the constraint that only three inputs variables, which are two input powers and the phase difference between the two sources, can be controlled, Cuckoo Search is used to get the best fit values to approximate the calculated inputs at those sampling points. Tap water and White Pudding are chosen as the heating material to show the correctness and the feasibility of this algorithm, and a uniform temperature rising process can be obtained. Compared with genetic algorithm, Cuckoo Search uses less time and has a better tracking result. The algorithm is also verified in the test of microwave source output powers randomly jumping around 100 ± 40% of the calculated values, sampling temperature with the error of -0.3 ∼ 0.3 K, long sampling period of 4 seconds, and uniform temperature rising processes can be also achieved. The average temperature of those sampling points can well follow the reference trajectory.