The article addresses the problem of applying potentially unsafe and non-stabilizing desired inputs to safety-critical systems, such as those found in automated driving, smart factories, or surgical robotics. It proposes a stability-enhanced predictive safety filter that extends an existing model predictive safety filter formulation to provide stability guarantees in addition to constraint satisfaction.
The key highlights and insights are:
The proposed framework extends well-known stability results from model predictive control (MPC) theory while supporting commonly used design techniques. It can provide different levels of stability, ranging from bounded convergence to uniform asymptotic stability.
The formulation is easily adaptable to a variety of different MPC settings, resulting in a modular framework that can be tailored to robust or dynamic tracking settings. This provides an online adaptable closed-loop performance bound.
The design and implementation of the scheme are illustrated using an advanced driver assistance system example, requiring safety and stability with respect to a dynamic reference trajectory.
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