On Hyper-Stability Theory Based Multivariable Nonlinear Adaptive Control: Experimental Validation on Induction Motors

This paper presents a novel hyperstability-based adaptive control strategy for induction motors, distinguishing itself from conventional model reference adaptive control (MRAC) approaches by integrating enhanced robustness against parametric uncertainties and external disturbances. Unlike traditional adaptive controllers, the proposed Hyper-stable adaptive controller (H-MRAC) ensures improved transient performance and faster convergence rates, validated through both theoretical analysis and experimental verification. Key innovations include the integration of hyper-stability theory into adaptive control design and a comprehensive evaluation of parameter uncertainties, which significantly improves motor performance in variable conditions. Experimental results demonstrate a 42% reduction in integral squared error (ISE), a 37% improvement in integral absolute error (IAE), a 28.7% improvement in integral time absolute error (ITAE) and a 25% faster convergence compared to standard MRAC. A detailed comparison with passivity-based control strategies shows a 26.5% improvement in steady-state performance and 30% faster transient response. Despite these successes, the paper discusses limitations related to computational complexity, real-time implementation challenges, and the impact of sensor noise on control performance. The potential need for DSPs or FPGA-based solutions is also addressed. Finally, the generalisability of the proposed control method across different motor types and power ratings is considered with future directions for broader validation in diverse industrial scenarios.