High-efficiency adaptive temperature control for thermoelectric system based on the OBPPID strategy

Thermoelectric cooler (TEC) is widely used for temperature control in optoelectronics, machinery, biomedicine, and other fields. However, the unstable temperature control over wide ranges and different gradients restricts the efficiency of TEC. To highlight the neural network’s effectiveness in controlling TEC systems, the optimized BPPID (OBPPID) strategy is proposed. In the OBPPID strategy, the factors s̃ and G are decided through formula derivation and the particle swarm optimization (PSO) algorithm, respectively contributing to the backward adjustment and forward propagation of BPPID. The OBPPID solves three issues that the BPPID faces: gradient vanishing of network weights, over-reliance of initial network weights and range constraint of control parameters. The OBPPID and BPPID have been simulated 1000 times to evaluate control performance. The results show that, with random initial network weights, the OBPPID strategy reduces the cost variability of the BPPID strategy from 92 to 17, achieving an 81.52% reduction. Physical tests have confirmed that in the temperature control over wide range and the temperature gradients of 5°C, 20°C, and 80°C, the OBPPID strategy can adaptively adjust the control parameters and provide higher efficiency than PID, BPPID and fuzzy PID strategies for TEC system.