Robust Temporal Logic Model Predictive Control

Control synthesis from temporal logic specifications has gained popularity in recent years. In this paper, we use a model predictive approach to control discrete time linear systems with additive bounded disturbances subject to constraints given as formulas of signal temporal logic (STL). We introduce a (conservative) computationally efficient framework to synthesize control strategies based on mixed integer programs. The designed controllers satisfy the temporal logic requirements, are robust to all possible realizations of the disturbances, and optimal with respect to a cost function. In case the temporal logic constraint is infeasible, the controller satisfies a relaxed, minimally violating constraint. An illustrative case study is included.Comment: This work has been accepted to appear in the proceedings of 53rd Annual Allerton Conference on Communication, Control and Computing, Urbana-Champaign, IL (2015

Robust Model Predictive Control for Signal Temporal Logic Synthesis

Most automated systems operate in uncertain or adversarial conditions, and have to be capable of reliably reacting to changes in the environment. The focus of this paper is on automatically synthesizing reactive controllers for cyber-physical systems subject to signal temporal logic (STL) specifications. We build on recent work that encodes STL specifications as mixed integer linear constraints on the variables of a discrete-time model of the system and environment dynamics. To obtain a reactive controller, we present solutions to the worst-case model predictive control (MPC) problem using a suite of mixed integer linear programming techniques. We demonstrate the comparative effectiveness of several existing worst-case MPC techniques, when applied to the problem of control subject to temporal logic specifications; our empirical results emphasize the need to develop specialized solutions for this domain

A Reuse-based framework for the design of analog and mixed-signal ICs

Despite the spectacular breakthroughs of the semiconductor industry, the ability to design integrated circuits (ICs) under stringent time-to-market (TTM) requirements is lagging behind integration capacity, so far keeping pace with still valid Moore's Law. The resulting gap is threatening with slowing down such a phenomenal growth. The design community believes that it is only by means of powerful CAD tools and design methodologies -and, possibly, a design paradigm shift-that this design gap can be bridged. In this sense, reuse-based design is seen as a promising solution, and concepts such as IP Block, Virtual Component, and Design Reuse have become commonplace thanks to the significant advances in the digital arena. Unfortunately, the very nature of analog and mixed-signal (AMS) design has hindered a similar level of consensus and development. This paper presents a framework for the reuse-based design of AMS circuits. The framework is founded on three key elements: (1) a CAD-supported hierarchical design flow that facilitates the incorporation of AMS reusable blocks, reduces the overall design time, and expedites the management of increasing AMS design complexity; (2) a complete, clear definition of the AMS reusable block, structured into three separate facets or views: the behavioral, structural, and layout facets, the two first for top-down electrical synthesis and bottom-up verification, the latter used during bottom-up physical synthesis; (3) the design for reusability set of tools, methods, and guidelines that, relying on intensive parameterization as well as on design knowledge capture and encapsulation, allows to produce fully reusable AMS blocks. A case study and a functional silicon prototype demonstrate the validity of the paper's proposals.Ministerio de Educación y Ciencia TEC2004-0175

From Uncertainty Data to Robust Policies for Temporal Logic Planning

We consider the problem of synthesizing robust disturbance feedback policies for systems performing complex tasks. We formulate the tasks as linear temporal logic specifications and encode them into an optimization framework via mixed-integer constraints. Both the system dynamics and the specifications are known but affected by uncertainty. The distribution of the uncertainty is unknown, however realizations can be obtained. We introduce a data-driven approach where the constraints are fulfilled for a set of realizations and provide probabilistic generalization guarantees as a function of the number of considered realizations. We use separate chance constraints for the satisfaction of the specification and operational constraints. This allows us to quantify their violation probabilities independently. We compute disturbance feedback policies as solutions of mixed-integer linear or quadratic optimization problems. By using feedback we can exploit information of past realizations and provide feasibility for a wider range of situations compared to static input sequences. We demonstrate the proposed method on two robust motion-planning case studies for autonomous driving

Model Predictive Control for Signal Temporal Logic Specification

We present a mathematical programming-based method for model predictive control of cyber-physical systems subject to signal temporal logic (STL) specifications. We describe the use of STL to specify a wide range of properties of these systems, including safety, response and bounded liveness. For synthesis, we encode STL specifications as mixed integer-linear constraints on the system variables in the optimization problem at each step of a receding horizon control framework. We prove correctness of our algorithms, and present experimental results for controller synthesis for building energy and climate control