Neural Network -Based Control Systems

The rapid evolution of industrial automation and smart grid technology has exposed the inherent limitations of classical control theories. Standard Proportional-Integral-Derivative (PID) and Linear Quadratic Regulator (LQR) controllers rely heavily on precise mathematical modelling and the assumption of linearity. However, most electrical "plants"—including Induction Motors (IM), Permanent Magnet Synchronous Motors (PMSM), and Power Electronic Converters—are characterized by high-degree nonlinearities, magnetic saturation, and time-varying parameters such as temperature-dependent resistance shifts. This seminar explores the integration of Neurological Network-Based Control Systems (NNBCS) as a robust, data-driven alternative to conventional modelling. By leveraging the universal approximation capabilities of Artificial Neural Networks (ANNs), these systems can identify and compensate for unmodeled dynamics without an explicit transfer function $G(s)$. The report focuses on three primary architectural implementations: Neural System Identification, where the network creates a "digital twin" of the plant; Neuro-Adaptive PID Control, where the NN dynamically tunes controller gains to eliminate overshoot; and Direct Inverse Control, where the network learns the inverse physics of the electrical drive to provide high-precision trajectory tracking. Specific attention is given to the application of these networks in Power Electronics and Machine Drives. Case studies presented include Sensor less Speed Control of asynchronous machines, where NNs estimate rotor position from stator current/voltage, and Maximum Power Point Tracking (MPPT) in Photovoltaic (PV) systems, where neural algorithms significantly reduce settling time compared to traditional "Perturb and Observe" methods. Furthermore, the abstract addresses the role of NNBCS in improving Power Quality through the real-time estimation and cancellation of Total Harmonic Distortion (THD) using Active Power Filters. Despite the superior performance in handling nonlinearities, the transition to NN-based control faces challenges regarding computational latency and formal stability verification (the "Black Box" problem). The report concludes by evaluating implementation strategies using high-speed hardware such as Digital Signal Processors (DSPs) and Field Programmable Gate Arrays (FPGAs), highlighting the future shift toward Edge AI in self-optimizing electrical infrastructure and resilient smart grids.