Real-Time Implementation of Intelligent Modeling and Control Techniques on a PLC Platform
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Abstract
Programmable logic controllers (PLCs) have been used for many decades for standard control in industrial and factory environments. Over the years, PLCs have become computational efficient and powerful, and a robust platform with applications beyond the standard control and factory automation. Due to the new advanced PLC's features and computational power, they are ideal platforms for exploring advanced modeling and control methods, including computational intelligence based techniques such as neural networks, particle swarm optimization (PSO) and many others. Some of these techniques require fast floating-point calculations that are now possible in real-time on the PLC. This paper focuses on the Allen-Bradley ControlLogix brand of PLCs, due to their high performance and extensive use in industry. The design and implementation of a neurocontroller consisting of two neural networks, one for modeling and the other for control, and the training of these neural networks with particle swarm optimization is presented in this paper on a single PLC. The neurocontroller in this study is a power system stabilizer (PSS) that is used for power system oscillation damping. The PLC is interfaced to a power system simulated on the real time digital simulator. Real time results are presented showing that the PLC is a suitable hardware platform for implementing advanced modeling and control techniques for industrial applications.
