Feasibility-Robust MPC for Self-Powered Control of Nonlinear Systems

Abstract

Self-powered control systems harvest disturbance energy to power the controller, eliminating reliance on external power sources. These systems are of interest in applications requiring autonomous operation without access to grid power or periodic recharging. Control inputs are constrained by finite energy storage and the requirement to prevent energy depletion during operation. This paper proposes a Model Predictive Control (MPC) approach that enforces these constraints while minimizing a quadratic performance objective. Nonconvex energy bounds are addressed using convex–concave approximations to enable efficient real-time optimization. However, nonlinear state evolution can render these approximations infeasible between MPC iterations. To address this, a feasibility restoration algorithm is introduced that minimally adjusts the convexification point while preserving real-time computational efficiency. The approach is demonstrated through simulation of a stochastically excited mechanical linkage system, showing that convex-concave MPC with feasibility restoration enables effective control of self-powered systems with guaranteed recursive feasibility.