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

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

Diagnosis and Repair for Synthesis from Signal Temporal Logic Specifications

We address the problem of diagnosing and repairing specifications for hybrid systems formalized in signal temporal logic (STL). Our focus is on the setting of automatic synthesis of controllers in a model predictive control (MPC) framework. We build on recent approaches that reduce the controller synthesis problem to solving one or more mixed integer linear programs (MILPs), where infeasibility of a MILP usually indicates unrealizability of the controller synthesis problem. Given an infeasible STL synthesis problem, we present algorithms that provide feedback on the reasons for unrealizability, and suggestions for making it realizable. Our algorithms are sound and complete, i.e., they provide a correct diagnosis, and always terminate with a non-trivial specification that is feasible using the chosen synthesis method, when such a solution exists. We demonstrate the effectiveness of our approach on the synthesis of controllers for various cyber-physical systems, including an autonomous driving application and an aircraft electric power system

Formal Synthesis of Control Strategies for Positive Monotone Systems

We design controllers from formal specifications for positive discrete-time monotone systems that are subject to bounded disturbances. Such systems are widely used to model the dynamics of transportation and biological networks. The specifications are described using signal temporal logic (STL), which can express a broad range of temporal properties. We formulate the problem as a mixed-integer linear program (MILP) and show that under the assumptions made in this paper, which are not restrictive for traffic applications, the existence of open-loop control policies is sufficient and almost necessary to ensure the satisfaction of STL formulas. We establish a relation between satisfaction of STL formulas in infinite time and set-invariance theories and provide an efficient method to compute robust control invariant sets in high dimensions. We also develop a robust model predictive framework to plan controls optimally while ensuring the satisfaction of the specification. Illustrative examples and a traffic management case study are included.Comment: To appear in IEEE Transactions on Automatic Control (TAC) (2018), 16 pages, double colum

Oracle-Guided Design and Analysis of Learning-Based Cyber-Physical Systems

We are in world where autonomous systems, such as self-driving cars, surgical robots, robotic manipulators are becoming a reality. Such systems are considered \textit{safety-critical} since they interact with humans on a regular basis. Hence, before such systems can be integrated into our day to day life, we need to guarantee their safety. Recent success in machine learning (ML) and artificial intelligence (AI) has led to an increase in their use in real world robotic systems. For example, complex perception modules in self-driving cars and deep reinforcement learning controllers in robotic manipulators. Although powerful, they introduce an additional level of complexity when it comes to the formal analysis of autonomous systems. In this thesis, such systems are designated as Learning-Based Cyber-Physical Systems~(LB-CPS). In this thesis, we take inspiration from the Oracle-Guided Inductive Synthesis~(OGIS) paradigm to develop frameworks which can aid in achieving formal guarantees in different stages of an autonomous system design and analysis pipeline. Furthermore, we show that to guarantee the safety of LB-CPS, the design (synthesis) and analysis (verification) must consider feedback from the other. We consider five important parts of the design and analysis process and show a strong coupling among them, namely (i) Robust Control Synthesis from High Level Safety Specifications; (ii) Diagnosis and Repair of Safety Requirements for Control Synthesis; (iii) Counter-example Guided Data Augmentation for training high-accuracy ML models; (iv) Simulation-Guided Falsification and Verification against Adversarial Environments; and (v) Bridging Model and Real-World Gap. Finally, we introduce a software toolkit \verifai{} for the design and analysis of AI based systems, which was developed to provide a common formal platform to implement design and analysis frameworks for LB-CPS