In this work a continuous-time MPC scheme is presented for linear systems under Signal Temporal Logic (STL) specifications and input constraints. The satisfaction of the STL specifications is encoded by time-varying barrier functions and a least-violating control law is designed for cases when the satisfaction of the task with a given robustness value is not achieved, e.g., due to actuation limitations. The recursive feasibility of the proposed scheme is guaranteed when a time-varying terminal constraint is introduced. This constraint ensures a desired behavior for the system that guarantees the satisfaction of the task with pre-determined robustness.

In this paper we focus on the problem of decomposing a global Signal Temporal Logic formula (STL) assigned to a multi-agent system to local STL tasks when the team of agents is a-priori decomposed to disjoint sub-teams. The predicate functions associated to the local tasks are parameterized as hypercubes depending on the states of the agents in a given sub-team. The parameters of the functions are, then, found as part of the solution of a convex program that aims implicitly at maximizing the volume of the zero level-set of the corresponding predicate function. Two alternative definitions of the local STL tasks are proposed and the satisfaction of the global STL formula is proven when the conjunction of the local STL tasks is satisfied.

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Coordination of multiple platoons involves the design of vehicles' trajectories ensuring splitting and merging of different platoons in a safe and energy-optimal manner. In this work, we consider the split-merge-maintain problem between any pair of platoons in which a number of vehicles temporally split from the rest, allowing the vehicles of another platoon to merge, and move with the preceding and following vehicles as a single, large platoon. Here, the splitting, merging and distancemaintaining tasks are expressed in Signal Temporal Logic (STL) and a control barrier function (CBF) is introduced to encode the STL constraints. The control inputs of the vehicles are, then, found as a solution to a computationally efficient, convex, quadratic program. The effectiveness of the proposed method is verified in simulation.