Hierarchical adaptive control of self-stabilizing electromechanical systems using artificial-immune self-tuning mechanism for state weighting-factors

This article presents a novel self-adaptive linear-quadratic-regulator (LQR) architecture to improve the robustness of self-stabilizing electromechanical systems against exogenous disturbances. The main contribution of this article is to formulate a nonlinear-type artificial-immune adaptation mechanism that dynamically adjusts the state-weighting-factors of LQR’s quadratic-performance-index online. The Riccati-equation solver uses these updated state-weighting-factors to yield time-varying state-feedback gains. This hierarchical control procedure uses immunological computations to indirectly alter the LQR gains, which helps in flexibly reconfiguring the control trajectory under disturbances. The performance of the proposed immune-adaptive LQR is benchmarked against a conventional adaptive LQR and a fixed-gain LQR by conducting software simulations on the nominal model of the QNET rotary pendulum system. Credible real-time experiments are also conducted on the QNET rotary pendulum’s hardware setup to analyze each controller’s efficacy in the physical environment. The simulation and experimental results validate the superior disturbance-rejection capability of the proposed controller under every testing scenario.