This paper proposes an optimal control strategy for a discrete-time linear system constrained to satisfy a temporal logic specification over a set of linear predicates in its state variables. The cost is a quadratic function that penalizes the distance from desired state and control trajectories. The specification is a formula of syntactically co-safe Linear Temporal Logic (scLTL), which can be...

We exploit symmetry to reduce the memory storage requirements of linear explicit model predictive controllers. In the first part of the paper we define controller symmetry. We describe how symmetry can be used to compress the explicit controller and discuss the implementation of the resulting compressed controller. In the second part we develop a method for computing the reduced-memory controll...

Adaptive Horizon Model Predictive Control (AHMPC) is a scheme for varying as needed the horizon length of Model Predictive Control (MPC). Its goal is to achieve stabilization with horizons as small as possible so that MPC can be used on faster or more complicated dynamic processes. Beside the standard requirements of MPC including a terminal cost that is a control Lyapunov function, AHMPC requi...

This paper aims to improve the reliability of optimal control using models constructed by machine learning methods. Optimal problems based on such are generally non-convex and difficult solve online. In this paper, we propose a model that combines Hammerstein-Wiener with input convex neural networks, which have recently been proposed in field learning. An important feature is resulting effectiv...