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Receding horizon control (RHC) is a popular procedure to deal with optimalcontrol problems. Due to the existence of state constraints, optimization-basedRHC often suffers the notorious issue of infeasibility, which strongly shrinksthe region of controllable state. This paper proposes a generalized controlbarrier function (CBF) to enlarge the feasible region of constrained RHC withonly a one-step constraint on the prediction horizon. This design can reducethe constrained steps by penalizing the tendency to move towards the constraintboundary. Additionally, generalized CBF is able to handle high-order equalityor inequality constraints through extending the constrained step to nonadjacentnodes. We apply this technique on an automated vehicle control task. Theresults show that compared to multi-step pointwise constraints, generalized CBFcan effectively avoid the infeasibility issue in a larger partition of thestate space, and the computing efficiency is also improved by 14%-23%.

Feasibility Enhancement of Constrained Receding Horizon Control Using Generalized Control Barrier Function