Towards a Framework for Realizable Safety Critical Control through Active Set Invariance

Gurriet, Thomas; Singletary, Andrew; Reher, Jacob; Ciarletta, Laurent; Féron, Éric; Ames, Aaron D.

doi:10.1109/iccps.2018.00018

Cited by 110 publications

(117 citation statements)

References 16 publications

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“…To demonstrate the application of control barrier functions as "safety filters," we will consider their experimental realization on a Segway type robot, i.e., a two-wheeled inverted pendulum. In particular, this subsection summarizes the results of [32] which provided the first experimental evaluation of CBFs on a robotic system that is not statically stable. To realize these results, a Ninebot Segway was rebuilt, with only the original chassis and motors remaining-all of the electronics were customized to allow for the realtime control of the system via optimization based controllers.…”

Section: Dynamic Balancing On Segwaysmentioning

confidence: 99%

Control Barrier Functions: Theory and Applications

Ames

Coogan

Egerstedt

et al. 2019

2019 18th European Control Conference (ECC)

1,116

801

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Section: Dynamic Balancing On Segwaysmentioning

confidence: 99%

Control Barrier Functions: Theory and Applications

Ames

Coogan

Egerstedt

et al. 2019

2019 18th European Control Conference (ECC)

1,116

801

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“…The idea here is to decouple performance from enforcing hard safety constraints in such a way that prioritizes the latter over the former. Given a nominal controller that processes commands and focuses on performing the desired task, a safety filter can be used to preempt these desired inputs in a way that ensures safety of the system when necessary (see [9]- [11] for application examples). Ideally, the filter is minimally invasive to the desired input, i.e.…”

Section: Safety Filteringmentioning

confidence: 99%

“…Unfortunately, these algorithms take substantial time to run and can only handle high dimensional systems at the expense of conservative results, leading to small operational regions and degraded performances for the system. Nonetheless, given a control invariant set, safety of the system can then be easily guaranteed by continuously filtering the control signal in a minimally invasive way as proposed in [8] (see [9]- [11] for applications).…”

Section: Introductionmentioning

confidence: 99%

A Scalable Safety Critical Control Framework for Nonlinear Systems

et al. 2020

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There are two main approaches to safety-critical control. The first one relies on computation of control invariant sets and is presented in the first part of this work. The second approach draws from the topic of optimal control and relies on the ability to realize Model-Predictive-Controllers online to guarantee the safety of a system. In the second approach, safety is ensured at a planning stage by solving the control problem subject for some explicitly defined constraints on the state and control input. Both approaches have distinct advantages but also major drawbacks that hinder their practical effectiveness, namely scalability for the first one and computational complexity for the second. We therefore present an approach that draws from the advantages of both approaches to deliver efficient and scalable methods of ensuring safety for nonlinear dynamical systems. In particular, we show that identifying a backup control law that stabilizes the system is in fact sufficient to exploit some of the set-invariance conditions presented in the first part of this work. Indeed, one only needs to be able to numerically integrate the closed-loop dynamics of the system over a finite horizon under this backup law to compute all the information necessary for evaluating the regulation map and enforcing safety. The effect of relaxing the stabilization requirements of the backup law is also studied, and weaker but more practical safety guarantees are brought forward. We then explore the relationship between the optimality of the backup law and how conservative the resulting safety filter is. Finally, methods of selecting a safe input with varying levels of trade-off between conservatism and computational complexity are proposed and illustrated on multiple robotic systems, namely: a two-wheeled inverted pendulum (Segway), an industrial manipulator, a quadrotor, and a lower body exoskeleton.

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“…A resulting discrete-time state history is obtained, which is then transformed with Lyapunov function V and finally differentiated numerically to estimateV throughout the experiment. This yields a data set comprised of input-output pairs: (18) and let H be the class of all such estimators mapping R 2k × Q × R n × U to R. Defining a loss function L : R × R → R + , the supervised regression task is then to find a function in H via empirical risk minimization (ERM):…”

Section: B Motivating a Data-driven Learning Approachmentioning

confidence: 99%

“…In this section we apply the episodic learning algorithm constructed in Section IV to the Segway platform. In particular, we consider a 4-dimensional planar Segway model based on the simulation model in [18]. The system states consist of horizontal position and velocity, pitch angle, and pitch angle rate.…”

Section: Application On Segway Platformmentioning

confidence: 99%

Episodic Learning with Control Lyapunov Functions for Uncertain Robotic Systems

Taylor

Dorobantu

et al. 2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Many modern nonlinear control methods aim to endow systems with guaranteed properties, such as stability or safety, and have been successfully applied to the domain of robotics. However, model uncertainty remains a persistent challenge, weakening theoretical guarantees and causing implementation failures on physical systems. This paper develops a machine learning framework centered around Control Lyapunov Functions (CLFs) to adapt to parametric uncertainty and unmodeled dynamics in general robotic systems. Our proposed method proceeds by iteratively updating estimates of Lyapunov function derivatives and improving controllers, ultimately yielding a stabilizing quadratic program modelbased controller. We validate our approach on a planar Segway simulation, demonstrating substantial performance improvements by iteratively refining on a base model-free controller.

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Towards a Framework for Realizable Safety Critical Control through Active Set Invariance

Cited by 110 publications

References 16 publications

Control Barrier Functions: Theory and Applications

Control Barrier Functions: Theory and Applications

A Scalable Safety Critical Control Framework for Nonlinear Systems

Episodic Learning with Control Lyapunov Functions for Uncertain Robotic Systems

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