Property:Abstract
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O
We present a mathematical programming-based method for optimal con- trol of nonlinear systems subject to temporal logic task specifications. We specify tasks using a fragment of linear temporal logic (LTL) that allows both finite- and infinite-horizon properties to be specified, including tasks such as surveillance, periodic motion, repeated assembly, and environmental monitoring. Our method di- rectly encodes an LTL formula as mixed-integer linear constraints on the system variables, avoiding the computationally expensive process of creating a finite ab- straction. Our approach is efficient; for common tasks our formulation uses significantly fewer binary variables than related approaches and gives the tightest possible convex relaxation. We apply our method on piecewise affine systems and certain classes of differentially flat systems. In numerical experiments, we solve temporal logic motion planning tasks for high-dimensional (10+) continuous systems. +
M
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. +
We present a mathematical programming-based method for model predictive control of discrete-time cyber- physical systems subject to signal temporal logic (STL) speci- fications. 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 model predictive control. We present experimental results for controller synthesis on simplified models of a smart micro-grid and HVAC system. +
O
We present a mathematical programming-based method for optimal control of discrete-time nonlinear systems subject to temporal logic task specifications. We use linear temporal logic (LTL) to specify a wide range of properties and tasks, such as safety, progress, response, surveillance, repeated assembly, and environmental monitoring. Our method directly encodes an LTL formula as mixed-integer linear constraints on the continuous system variables, avoiding the computationally expensive processes of creating a finite abstraction of the system and a Bu Ìchi automaton for the specification. In numerical experiments, we solve temporal logic motion planning tasks for high-dimensional (more than 10 continuous states) dynamical systems. +
A
We present a mathematical reconstruction
of the kinematics and dynamics of flight initiation as observed in high-speed video recordings of the insect Drosophila melanogaster. The behavioral dichotomy observed in the fruit flies' flight initiation sequences, as a response to different stimuli, was reflected in two contrasting sets of dynamics once the flies had become airborne. By reconstructing the dynamics of unconstrained motion during flight initiations, we assess the fly's responses (generation of forces and moments) amidst these two dynamic patterns. Moreover, we introduce a 3D visual tracking algorithm as a tool to analyze the wing kinematics applied by the insect, and investigate their relation(s) to the production of these aerodynamic forces. Using this framework we formulate different hypotheses about the modulation of flight forces and moments during flight initiation as a way torefining our understanding of insect flight control., +
R
We present a method for designing robust con- trollers for dynamical systems with linear temporal logic specifications. We abstract the original system by a finite Markov Decision Process (MDP) that has transition probabilities in a specified uncertainty set. A robust control policy for the MDP is generated that maximizes the worst-case probability of satisfying the specification over all transition probabilities in the uncertainty set. To do this, we use a procedure from probabilistic model checking to combine the system model with an automaton representing the specification. This new MDP is then transformed into an equivalent form that satisfies assumptions for stochastic shortest path dynamic programming. A robust version of dynamic programming allows us to solve for a eps-suboptimal robust control policy with time complexity O(log1/eps) times that for the non-robust case. We then implement this control policy on the original dynamical system. +
P
We present a method for mending strategies for GR(1) specifications. Given the addition or removal of edges from the game graph describing a problem (essentially transition rules in a GR(1) specification), we apply a μ-calculus formula to a neighborhood of states to obtain a âlocal strategyâ that navigates around the invalidated parts of an original synthesized strategy. Our method may thus avoid global resynthesis while recovering correctness with respect to the new specification. We illustrate the results both in simulation and on physical hardware for a planar robot surveillance task. +
R
We present a methodology for automatic synthesis of embedded control software that incorporates a class of linear temporal logic (LTL) specifications sufficient to describe a wide range of properties including safety, stability, progress, obligation, response and guarantee. To alleviate the associated computational complexity of LTL synthesis, we propose a receding horizon framework that effectively reduces the synthesis problem into a set of smaller problems. The proposed control architecture consists of a goal generator, a trajectory planner, and a continuous controller. The goal generator reduces the trajectory generation problem into a sequence of smaller problems of short horizon while preserving the desired system-level temporal properties. Subsequently, in each iteration, the trajectory planner solves the corresponding short-horizon problem with the currently observed state as the initial state and generates a feasible trajectory to be implemented by the continuous controller. Based on the simulation property, we show that the composition of the goal generator, trajectory planner and continuous controller and the corresponding receding horizon framework guarantee the correctness of the system with respect to its specification regardless of the environment in which the system operates. In addition, we present a response mechanism to handle failures that may occur due to a mismatch between the actual system and its model. The effectiveness of the proposed technique is demonstrated through an example of an autonomous vehicle navigating an urban environment. This example also illustrates that the system is not only robust with respect to exogenous disturbances but is also capable of properly handling violation of the environment assumption that is explicitly stated as part of the system specification . +
C
We present a set of primitive operations which forms the core of a robot system
description and control language. The actions of the individual primitives are derived
from the mathematical structure of the equations of motion for constrained mechanical
systems. The recursive nature of the primitives allows composite robots to be constructed
from more elementary daughter robots. We review a few pertinent results of classical
mechanics, describe the functionality of our primitive operations, and present several
different hierarchical strategies for the description and control of a two-fingered hand
holding a box. +
R
We present a simple geometric analysis of wireless
connectivity in vehicle networks. We introduce a localized
notion of connectedness, and construct a function that measures
the robustness of this local connectedness to variations
in position. Under a mild feasibility hypothesis, this function
provides a sufficient condition for global connectedness of
the network. Further, it is distributed, in the sense that
both the function and its gradients can be calculated using
only neighbor-to-neighbor communications. It can thus form
the basis for distributed motion-control algorithms which
respect connectivity constraints. We conclude with two simple
examples of target applications. +
S
We present a synthesis method for communication protocols for active safety applications that satisfy certain formal specifications on quality of service requirements. The protocols are developed to provide reliable communication services for automobile active safety applications. The synthesis method transforms a specification into a distributed implementation of senders and receivers that together satisfy the quality of service requirements by transmitting messages over an unreliable medium. We develop a specification language and an execution model for the implementations, and demonstrate the viability of our method by developing a protocol for a traffic scenario in which a car runs a red light at a busy intersection. +
C
We present a theory of contracts that is centered around reacting to failures and explore it from a general assume-guarantee perspective as well as from a concrete context of automated synthesis from linear temporal logic (LTL) specifications, all of which are compliant with a contract metatheory introduced by Benveniste et al. We also show how to obtain an automated procedure for synthesizing reactive assume-guarantee contracts and implementations that capture ideas like optimality and robustness based on assume-guarantee lattices computed from antitone Galois connection fixpoints. Lastly, we provide an example of a “reactive GR(1)” contract and a simulation of its implementation. +
D
We present an approach that allows mission and contingency management to be achieved in a distributed and dynamic manner without any central control over multiple software modules. This approach comprises two key elements---a mission management subsystem and a Canonical Software Architecture (CSA) for a planning subsystem. The mission management subsystem works in conjunction with the planning subsystem to dynamically replan in reaction to contingencies. The CSA ensures the consistency of the states of all the software modules in the planning subsystem. System faults are identified and replanning strategies are performed distributedly in the planning and the mission management subsystems through the CSA. The approach has been implemented and tested on Alice, an autonomous vehicle developed by the California Institute of Technology for the 2007 DARPA Urban Challenge. +
A
An automated model reduction tool to guide the design and analysis of synthetic biological circuits +
We present an automated model reduction algorithm that uses quasi-steady state approximation based reduction to minimize the error between the desired outputs. Additionally, the algorithm minimizes the sensitivity of the error with respect to parameters to ensure robust performance of the reduced model in the presence of parametric uncertainties. We develop the theory for this model reduction algorithm and present the implementation of the algorithm that can be used to perform model reduction of given SBML models. To demonstrate the utility of this algorithm, we consider the design of a synthetic biological circuit to control the population density and composition of a consortium consisting of two different cell strains. We show how the model reduction algorithm can be used to guide the design and analysis of this circuit. +
We present an identification framework for biochemical systems that allows multiple candidate models to be compared. This framework is designed to select a model that fits the data while maintaining model simplicity. The model identification task is divided into a parameter estimation stage and a model comparison stage. Model selection is based on calculating Akaike's Information Criterion, which is a systematic method for determining the model that best represents a set of experimental data. Two case studies are presented: a simulated transcriptional control circuit and a system of oscillators that has been built and characterized in vitro. In both examples the multi-model framework is able to discriminate between model candidates to select the one that best describes the data. +
U
We present the results from a flight experiment demonstrating two significant advances in software enabled
control: optimization-based control using real-time trajectory generation and logical programming environments for
formal analysis of control software. Our demonstration platform consisted of a human-piloted F-15 jet flying together
with an autonomous T-33 jet. We describe the behavior of the system in two scenarios. In the first, nominal state
communications were present and the autonomous aircraft maintained formation as the human pilot flew maneuvers. In
the second, we imposed the loss of high-rate communications and demonstrated an autonomous safe âlost wingmanâ
procedure to increase separation and re-acquire contact. The flight demonstration included both a nominal formation
flight component and an execution of the lost wingman scenario. +
C
We propose a compositional stability analysis methodology for verifying properties of systems that are interconnections of multiple subsystems. The proposed method assembles stability certificates for the interconnected system based on the certificates for the input-output properties of the subsystems. The hierarchy in the analysis is achieved by utilizing dual decomposition ideas in optimization. Decoupled subproblems establish subsystem level input-output properties whereas the ``master'' problem imposes and updates the conditions on the subproblems toward ensuring interconnected system level stability properties. Both global stabilityanalysis and region-of-attraction analysis are discussed. +
R
Reverse Engineering Combination Therapies for Evolutionary Dynamics of Disease: An Hinfty Approach +
We propose a general algorithm for the systematic design of feedback strategies to stabilize the evolutionary dynamics of a generic disease model using an H1 approach. We show that designing antibody concentrations can be cast as an H1 state feedback synthesis problem, where the feedback gain is constrained to not only be strictly diagonal, but also that its diagonal elements satisfy an overdetermined set of linear equations. Leveraging recent results in positive systems, we additionally show that our algorithm always converges to a stabilizing controller. +
H
We propose a method for eliminating variables from component specifications during the decomposition of GR(1) properties into contracts. The variables that can be eliminated are identified by parameterizing the communication architecture to investigate the dependence of realizability on the availability of information. We prove that the selected variables can be hidden from other components, while still expressing the resulting specification as a game with full information with respect to the remaining variables. The values of other variables need not be known all the time, so we hide them for part of the time, thus reducing the amount of information that needs to be communicated between components. We improve on our previous results on algorithmic decomposition of GR(1) properties, and prove existence of decompositions in the full information case. We use semantic methods of computation based on binary decision diagrams. To recover the constructed specifications so that humans can read them, we implement exact symbolic minimal covering over the lattice of integer orthotopes, thus deriving minimal formulae in disjunctive normal form over integer variable intervals. +
S
Strategy-Driven Partitioning into Switching Modes for Piecewise-Affine Systems with Continuous Environments +
We propose a methodology for abstracting discrete-time piecewise-affine systems influenced by additive continuous environment variables, to synthesize correct-by-construction controllers from linear temporal logic specifications. The proposed algorithm partitions the environment domain into polytopes, considered as modes controlled by the environment. In each mode the environment variable is treated as a bounded disturbance for the system. Mode polytopes are iteratively enlarged while a strategy isomorphism between successive system partitions can be constructed. Isomorphisms are obtained by solving the stable marriage problem after proving that our case admits a unique solution. This leads to mapping high-level symbolic variables to equivalence classes of polytopes, instead of single polytopes as in other works. We thus avoid the need to merge partitions, which can create sliver polytopes, causing numerical problems during reachability computations. The approach enables using the same strategy over partitions that have an isomorphic subgraph relevant to the strategy. Reachability checks between neighboring partitions are used to reduce non-determinism introduced by switching and allow continuous restoration of discrete state. If bifurca- tions occur that prevent strategy reuse, then switched system game synthesis is performed, and a logic modeling formalism proposed that avoids trivial cyclic counterexamples. +
