Robust temporal logic model predictive control

2017 American Control Conference (ACC)

2017

Abstract-Motion planning classically concerns the problem of accomplishing a goal configuration while avoiding obstacles. However, the need for more sophisticated motion planning methodologies, taking temporal aspects into account, has emerged. To address this issue, temporal logics have recently been used to formulate such advanced specifications. This paper will consider Signal Temporal Logic in combination with Model Predictive Control. A robustness metric, called Discrete Average Space Robustness, is introduced and used to maximize the satisfaction of specifications which results in a natural robustness against noise. The comprised optimization problem is convex and formulated as a Linear Program.

Section: Average Space Robustnessmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Robust motion planning employing signal temporal logic

Lindemann

2017 American Control Conference (ACC)

2017

“…STL task specifications have lately been studied from a control perspective in the sense of how to ensure their satisfaction. Proposed approaches for controller synthesis include model predictive control (MPC) [6], [7], barrier function- [8], and prescribed performance control (PPC)based methods [9]. These methods rely heavily on knowledge of system dynamics and exhibit a trade-off between their computational complexity and the range of system dynamics and STL task fragments they can handle.…”

Section: Introductionmentioning

confidence: 99%

Prescribed Performance Control Guided Policy Improvement for Satisfying Signal Temporal Logic Tasks

Várnai

2019 American Control Conference (ACC)

2019

Signal temporal logic (STL) provides a userfriendly interface for defining complex tasks for robotic systems. Recent efforts aim at designing control laws or using reinforcement learning methods to find policies which guarantee satisfaction of these tasks. While the former suffer from the trade-off between task specification and computational complexity, the latter encounter difficulties in exploration as the tasks become more complex and challenging to satisfy. This paper proposes to combine the benefits of the two approaches and use an efficient prescribed performance control (PPC) base law to guide exploration within the reinforcement learning algorithm. The potential of the method is demonstrated in a simulated environment through two sample navigational tasks.

“…Control of systems subject to STL is a difficult task due to the nonlinear, nonconvex, noncausal, and nonsmooth semantics. Previous work on STL control synthesis has been done in [12], [13], [14] by using model predictive control (MPC), while [15] explicitly extends the method derived in [13] to multi-agent systems. In this paper, we consider a nonlinear system subject to a subset of STL.…”

Section: Introductionmentioning

confidence: 99%

Prescribed performance control for signal temporal logic specifications

Lindemann

Verginis

2017 IEEE 56th Annual Conference on Decision and Control (CDC)

2017

Motivated by the recent interest in formal methods-based control for dynamic robots, we discuss the applicability of prescribed performance control to nonlinear systems subject to signal temporal logic specifications. Prescribed performance control imposes a desired transient behavior on the system trajectories that is leveraged to satisfy atomic signal temporal logic specifications. A hybrid control strategy is then used to satisfy a finite set of these atomic specifications. Simulations of a multi-agent system, using consensus dynamics, show that a wide range of specifications, i.e., formation, sequencing, and dispersion, can be robustly satisfied.y := H(y, t) with y(0) := y 0 ∈ Ω y ,