Co-design of Anytime Computation and Robust Control

Pant, Yash Vardhan; Abbas, Houssam; Mohta, Kartik; Nghiem, Truong X.; Devietti, Joseph; Mangharam, Rahul

doi:10.1109/rtss.2015.12

Cited by 25 publications

(16 citation statements)

References 18 publications

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“…In simulation, d min is set to 0.2 m. The quad-rotor dynamics are obtained via linearization around hover, and discretization at 5-Hz. Similar models have been used for control of real quad-rotors with success ( [25]). For simulation, we set the mass of either quad-rotor to be 0.5 kg.…”

Section: B Autonomous Atc For Quad-rotorsmentioning

confidence: 99%

Smooth operator: Control using the smooth robustness of temporal logic

Pant

Abbas

Mangharam

2017

2017 IEEE Conference on Control Technology and Applications (CCTA)

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112

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Modern control systems, like controllers for swarms of quadrotors, must satisfy complex control objectives while withstanding a wide range of disturbances, from bugs in their software to attacks on their sensors and changes in their environments. These requirements go beyond stability and tracking, and involve temporal and sequencing constraints on system response to various events. This work formalizes the requirements as formulas in Metric Temporal Logic (MTL), and designs a controller that maximizes the robustness of the MTL formula. Formally, if the system satisfies the formula with robustness r, then any disturbance of size less than r cannot cause it to violate the formula. Because robustness is not differentiable, this work provides arbitrarily precise, infinitely differentiable, approximations of it, thus enabling the use of powerful gradient descent optimizers. Experiments on a temperature control example and a two-quadrotor system demonstrate that this approach to controller design outper-forms existing approaches to robustness maximization based on Mixed Integer Linear Programming and stochastic heuristics. Moreover, it is not constrained to linear systems. Abstract-Modern control systems, like controllers for swarms of quadrotors, must satisfy complex control objectives while withstanding a wide range of disturbances, from bugs in their software to attacks on their sensors and changes in their environments. These requirements go beyond stability and tracking, and involve temporal and sequencing constraints on system response to various events. This work formalizes the requirements as formulas in Metric Temporal Logic (MTL), and designs a controller that maximizes the robustness of the MTL formula. Formally, if the system satisfies the formula with robustness r, then any disturbance of size less than r cannot cause it to violate the formula. Because robustness is not differentiable, this work provides arbitrarily precise, infinitely differentiable, approximations of it, thus enabling the use of powerful gradient descent optimizers. Experiments on a temperature control example and a two-quadrotor system demonstrate that this approach to controller design outperforms existing approaches to robustness maximization based on Mixed Integer Linear Programming and stochastic heuristics. Moreover, it is not constrained to linear systems. Keywords

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Section: B Autonomous Atc For Quad-rotorsmentioning

confidence: 99%

Smooth operator: Control using the smooth robustness of temporal logic

Pant

Abbas

Mangharam

2017

2017 IEEE Conference on Control Technology and Applications (CCTA)

Self Cite

112

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“…For the sake of simplicity, in this paper we assume they are all hyper-rectangles that contain the origin III. ROBUST MPC FOR THE FEEDBACK LINEARIZED SYSTEM Following [4], [10], we formulate a Robust MPC (RMPC) controller of (4) via constraint restriction. We outline the idea before providing the technical details.…”

Section: Problem Formulationmentioning

confidence: 99%

“…(10).Ẽ max represents the worst case bound on the estimation errorẽ k , and is computed similar to Eq. (16), but over the entire set X.…”

Section: Approximating the Bounding Sets For The Disturbancesmentioning

confidence: 99%

Robust model predictive control for non-linear systems with input and state constraints via feedback linearization

Pant

Abbas

Mangharam

2016

2016 IEEE 55th Conference on Decision and Control (CDC)

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Robust predictive control of non-linear systems under state estimation errors and input and state constraints is a challenging problem, and solutions to it have generally involved solving computationally hard non-linear optimizations. Feedback linearization has reduced the computational burden, but has not yet been solved for robust model predictive control under estimation errors and constraints. In this paper, we solve this problem of robust control of a non-linear system under bounded state estimation errors and input and state constraints using feedback linearization. We do so by developing robust constraints on the feedback linearized system such that the non-linear system respects its constraints. These constraints are computed at run-time using online reachability, and are linear in the optimization variables, resulting in a Quadratic Program with linear constraints. We also provide robust feasibility, recursive feasibility and stability results for our control algorithm. We evaluate our approach on two systems to show its applicability and performance. Abstract-Robust predictive control of non-linear systems under state estimation errors and input and state constraints is a challenging problem, and solutions to it have generally involved solving computationally hard non-linear optimizations. Feedback linearization has reduced the computational burden, but has not yet been solved for robust model predictive control under estimation errors and constraints. In this paper, we solve this problem of robust control of a non-linear system under bounded state estimation errors and input and state constraints using feedback linearization. We do so by developing robust constraints on the feedback linearized system such that the non-linear system respects its constraints. These constraints are computed at run-time using online reachability, and are linear in the optimization variables, resulting in a Quadratic Program with linear constraints. We also provide robust feasibility, recursive feasibility and stability results for our control algorithm. We evaluate our approach on two systems to show its applicability and performance.

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“…In [49], different controllers with different floating point operations were designed. Notable later works include [10,[51][52][53].…”

Section: Event-triggered Sequence-based Anytime Controlmentioning

confidence: 99%

“…In [10], named as sequence-based anytime control (SAC) as aformentioned, a buffer is used to store the tentative future inputs. In [53], a method for co-design of estimator and controller is proposed where the controller requests a time-varying criterion for the estimator. When sensor measurements are transmitted through a communication network, the measurements may be unavailable due to packet dropouts, or network congestion.…”

Section: Event-triggered Sequence-based Anytime Controlmentioning

confidence: 99%

Efficient algorithms for embedded optimisation-based control

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Co-design of Anytime Computation and Robust Control

Cited by 25 publications

References 18 publications

Smooth operator: Control using the smooth robustness of temporal logic

Smooth operator: Control using the smooth robustness of temporal logic

Robust model predictive control for non-linear systems with input and state constraints via feedback linearization

Efficient algorithms for embedded optimisation-based control

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