Adaptive Fault-Tolerant MCU-Based Motor Control System for Smart Grids

Thesis Abstract

The rapid integration of motor-driven actuation within smart grid infrastructures introduces stringent reliability requirements, particularly for critical loads and distributed energy resources where motor control failure can propagate across the grid. This study addresses the vulnerability of conventional motor control systems to faults in microcontroller units (MCUs) and power electronics, which can lead to degraded performance, efficiency losses, and stability challenges in grid-supporting applications. The aim is to develop an adaptive fault-tolerant MCU-based motor control framework that maintains nominal performance under component faults and cyber-physical disturbances. Specific objectives include (1) designing a fault-tolerant control architecture that integrates real-time fault detection, isolation, and reconfiguration; (2) implementing adaptive parameter estimation and model predictive control (MPC) for robust torque and speed regulation under degraded hardware conditions; (3) evaluating the framework on representative motor topologies (PM, BLDC, and induction motors) with grid-like dynamics and demand variability; (4) quantifying improvements in reliability, efficiency, and grid support metrics; and (5) providing a comprehensive assessment of scalability and deployment considerations in smart grid environments. The research adopts a design-science and engineering-validated methodology combining simulation, hardware-in-the-loop (HIL) testing, and empirical evaluation. The population comprises motor control software and hardware platforms, including an ARM Cortex-M7 MCU-based controller, inverter units, and three motor types (permanent magnet synchronous, brushless DC, and induction). A sample of 30 real-time control scenarios is constructed, spanning fault modes such as sensor failures, actuator saturation, inverter short circuits, and timing jitter. Data collection employs synchronized oscilloscope traces, inverter current/voltage and motor speed sensors, and telemetry from the MCU’s fault manager. The instrument set includes a fault-injection framework, a performance metrics dashboard (torque ripple, efficiency, latency, and settling time), and grid-support indicators (reactive power support, voltage regulation margin, and frequency response). Validity and reliability are ensured through cross-validation between hardware-in-the-loop experiments and high-fidelity simulations in MATLAB/Simulink. The analysis employs a suite of established techniques fault detection uses data-driven thresholding and Kalman-based anomaly detection; adaptive parameter estimation leverages recursive least squares with forgetting factor; control performance is evaluated via model predictive control with linearization around operating points, and robustness is assessed with Monte Carlo simulations. Statistical analysis includes repeated-measures ANOVA to compare performance under fault vs. no-fault conditions, with post-hoc pairwise comparisons where appropriate. Regression analysis identifies the relationship between fault severity and control accuracy. Theoretical underpinnings draw on classical fault-tolerant control theory, HIL validation frameworks, and resilience concepts from smart grid literature, with references to the Internal Model Principle and robust MPC formulations to justify control reconfiguration strategies. Expected findings indicate that the adaptive fault-tolerant controller maintains motor torque and speed within 2% of nominal under single-point MCU faults, inverter anomalies, and sensor degradations, while improving overall system availability by 25–40% and reducing energy losses by 1–3% through sustained efficiency under fault conditions. The framework is anticipated to demonstrate improved transient response during grid-induced perturbations, with reactive power support maintained within ±5% of target and voltage regulation stability preserved during distributed generation swings. The study also elucidates the trade-offs between fault-detection latency and control reconfiguration overhead, providing guidelines for selecting detection thresholds and MPC horizon lengths in resource-constrained MCU environments. The contribution to knowledge encompasses (1) a holistic design and empirical validation of an adaptive fault-tolerant MCU-based motor control system tailored for smart grids; (2) an integrated methodology for fault detection, isolation, and reconfiguration that preserves performance across multiple motor technologies; (3) quantitative evidence of reliability and efficiency gains in grid-supporting motor drives; and (4) practical deployment recommendations, including hardware sizing, software architectures, and cyber-physical security considerations for scalable smart grid implementations. The study concludes that embedding adaptive fault-tolerant control in MCU-based motor drives yields measurable improvements in reliability and grid-support capabilities, with actionable recommendations for standardizing fault-tolerant motor control in future smart grid deployments. Recommendations for future work include extending the framework to collaborative multi-motor coordination, exploring machine learning-enhanced fault prognosis, and validating performance under cyber-attack scenarios.