Abstract: An algorithm for a fuzzy neural network controller based on a phase plane objective function and its corresponding parameters is proposed. Based on the frequency conversion regulation of a three-phase AC motor speed, the system structure for computer monitoring of motor speed is discussed, thus achieving intelligent control of the motor speed. The new intelligent control method introduces a new objective function, has a simple structure, and a fast learning speed. Experimental studies show that the method has a fast convergence speed, strong learning ability, and improves the robustness of the system, confirming its effectiveness in motor speed control. Keywords: phase plane; fuzzy neural network; intelligent control The experiment demonstrates that this object function converges rapidly, improving both the learning speed and robustness of such a speed salvo control system. Keywords: phase space; fuzzy neural Netzoork; intelligent control. Engineering application systems, exemplified by electromechanical systems, are becoming increasingly complex. This complexity manifests not only in the difficulty of describing the system itself with precise mathematical models but also in the complexity of the operating environment and the goals the system aims to achieve. According to the incompatibility principle, as the complexity of a system increases, the accuracy of describing its dynamic behavior decreases. Therefore, traditional methods of description, control, and diagnosis are no longer adequate for this complexity. Artificial intelligence (AI) has opened a new avenue and provided new methods for solving the problem of understanding complex systems. However, due to the relatively low overall level of AI at present, the solution to this problem is not yet satisfactory. Therefore, it is necessary to explore new AI methods to better apply them to complex electromechanical systems. Click here to download the original paper.