This research focuses on developing an adaptive fuzzy-neural control system to manage and maintain temperature in brick tunnel kilns. The problem is how to optimize performance and energy consumption in the brick production process. A control algorithm using a combination of a fuzzy logic system and neural network is proposed to automatically adjust temperature parameters, optimize production efficiency, and reduce energy consumption. Simulation and experimental results demonstrate the outstanding performance of the presented system, with significant improvements in energy efficiency and product quality compared to traditional control methods. Moreover, the obtained results exhibit the potential for wide application of the adaptive neuro-fuzzy control method in academic study or industrial production processes.

Ensuring the stability and accuracy of the output voltage in DC/DC buck converters (DBCs) is critical for reliable operation. This paper investigates an observer-based adaptive command-filtered controller designed for DBC systems subject to lumped disturbances. First, a mathematical model of the system is developed on the basis of switching modes. Then, a simplified extended state observer (SESO) is elaborated to mitigate the effects of lumped disturbances. A command filter technique with an integrated adaptive law is subsequently synthesized to enhance output voltage regulation. The stability of the observer and DBC control system is rigorously certified using the Lyapunov principle. Finally, simulation and experimental approaches are exploited to confirm the validity of the proposed method. Compared to state-of-the-art approaches, the proposed observer-based adaptive command-filtered controller improves tracking performance by 96.1% and 77.8% in simulations and 84.4% and 49.1% in experiments under a sinusoidal reference trajectory.