Optimization of human generated trajectories for safety controller synthesis

Winn, Andrew K.; Julius, A. Agung

doi:10.1109/acc.2013.6580513

Cited by 13 publications

(11 citation statements)

References 32 publications

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“…In addition, it is possible to formulate an optimal control problem in which a performance objective needs to be optimized while maintaining the safety/reachability property. For further discussion on the trajectorybased approach to this problem, the reader is referred to [65].…”

Section: Remark 2011mentioning

confidence: 99%

Mathematical Control Theory I

Camlibel

Julius

Pasumarthy³

et al. 2015

Lecture Notes in Control and Information Sciences

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About this SeriesThis series aims to report new developments in the fields of control and information sciences-quickly, informally and at a high level. The type of material considered for publication includes:1. Preliminary drafts of monographs and advanced textbooks 2. Lectures on a new field, or presenting a new angle on a classical field 3. Research reports 4. Reports of meetings, provided they are (a) of exceptional interest and (b) devoted to a specific topic. The timeliness of subject material is very important.More information about this series at

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Section: Remark 2011mentioning

confidence: 99%

Mathematical Control Theory I

Camlibel

Julius

Pasumarthy³

et al. 2015

Lecture Notes in Control and Information Sciences

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“…The letter w will denote an element w = (x 0 , u) of X 0 × L 2 . Standard assumptions apply -see [9], [10]. Assumption 2.1: We formally capture specifications regarding the correct system behavior using Metric Temporal Logic (MTL) formulae [6].…”

Section: Problem Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

“…Our solution combines the previous works in [9], [10]. In brief, the work in [10] deals with the problem of optimizing a differentiable integral cost function over the output trajectories of the system starting from a given an input signal. The method is based on the calculus of variations, but it uses a gradient descent based approach to solve the optimization problem without formulating the optimality conditions given by the Minimum Principle.…”

Section: Introductionmentioning

confidence: 98%

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Functional gradient descent method for Metric Temporal Logic specifications

Abbas

Winn

Fainekos

et al. 2014

2014 American Control Conference

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Metric Temporal Logic (MTL) specifications can capture complex state and timing requirements. Given a nonlinear dynamical system and an MTL specification for that system, our goal is to find a trajectory that violates or satisfies the specification. This trajectory can be used as a concrete feedback to the system designer in the case of violation or as a trajectory to be tracked in the case of satisfaction. The search for such a trajectory is conducted over the space of initial conditions, system parameters and input signals. We convert the trajectory search problem into an optimization problem through MTL robust semantics. Robustness quantifies how close the trajectory is to violating or satisfying a specification. Starting from some arbitrary initial condition and parameter and given an input signal, we compute a descent direction in the search space, which leads to a trajectory that optimizes the MTL robustness. This process can be iterated to reach local optima (min or max). We demonstrate the method on examples from the literature.

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“…Following our earlier results [1], [2], [3], we pursue a synthesis strategy that exploits human generated trajectories.…”

Section: Introductionmentioning

confidence: 99%

Feedback control law generation for safety controller synthesis

Winn

Julius

2013

52nd IEEE Conference on Decision and Control

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In recent papers we have focused on the task of safety controller synthesis, that is, designing a controller that will take the system from any point within a compact set of initial states to a point inside a set of acceptable goal states, while never entering any state that is deemed unsafe. This method first finds a feedback controller that causes the system to be imbued with trajectory robustness, then finds open-loop reference signals that each safely drive the system from a subset of initial states to the goal state. In this paper we use piecewise affine system identification techniques to generate a feedback control law to replace the open-loop signals. This provides additional robustness to unexpected disturbances, in addition to reducing the memory required in the resulting controller, from storing many signals to a set of piecewise affine control laws.

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Optimization of human generated trajectories for safety controller synthesis

Cited by 13 publications

References 32 publications

Mathematical Control Theory I

Mathematical Control Theory I

Functional gradient descent method for Metric Temporal Logic specifications

Feedback control law generation for safety controller synthesis

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