A new self-tuning neuro-PID control architecture is proposed and applied to the stabilization of double inverted pendulum. The gain parameters of the PID controller are tuned using a neural network. The effectiveness of the proposed method is shown through simulation and experiment.

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In recent years, the sizes of Energy Management Systems (EMS) and Supervisory Control and Data Acquisition (SCADA) systems have grown to huge proportions for two reasons: The power systems to which they are applied have grown in size and complexity; and Their own functions have become more diverse and sophisticated. This raises the following problems: degradation of response time; decreased reliability; limited expandability; less maintainability; and increased costs. A functionally distributed system that is characterized by parallel processing and independent subsystems (parallel‐independent architecture) will solve these problems. The system is comprised of a group of functional units, each of which runs in parallel, independently and asynchronously. Copies of some programs and power system status data are stored in the relevant functional units, and data‐driven architecture is adopted, which eliminates the need for a centralized control mechanism. The feasibility of a functionally distributed system was tested through construction of a prototype. The results were satisfactory.

This paper is concerned with a new architecture of a self-tuning neuro-PID control system and its application to stabilization of an inverted pendulum. A single-input multi-output system is considered to control the inverted pendulum by using the PID controller. The PID gains are tuned by using two kinds of neural networks. The simulation results show effectiveness of the proposed approach. 0-7803-4316-6/98/$10.00 01998 IEEE

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