  Model predictive control (MPC) schemes are commonly designed with fixed, i.e., time-invariant, horizon length and cost functions. If no stabilizing terminal ingredients are used, stability can be guaranteed via a sufficiently long horizon. A suboptimality index can be derived that gives bounds on the performance of the MPC law over an infinite-horizon (IH). While for time-invariant schemes such index can be computed offline, less attention has been paid to time-varying strategies with adapting cost function which can be found, e.g., in learning-based optimal control. This work addresses the performance bounds of nonlinear MPC with stabilizing horizon and time-varying terminal cost. A scheme is proposed that uses the decay of the optimal finite-horizon cost and convolutes a history stack to predict the bounds on the IH performance. Based on online information on the decay rate, the performance bound estimate is improved while the terminal cost is adapted using methods from adaptive dynamic programming. The adaptation of the terminal cost leads to performance improvement over a time-invariant scheme with the same horizon length. The approach is demonstrated in a case study. 
On performance bound estimation in NMPC with time-varying terminal cost