Stabilization with guaranteed safety using Control Lyapunov–Barrier Function

Romdlony, Muhammad Zakiyullah; Jayawardhana, Bayu

doi:10.1016/j.automatica.2015.12.011

Cited by 255 publications

(203 citation statements)

References 37 publications

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“…To name a few, we refer interested readers to [1], [20], [15] and [16]. In [1] and [20], the authors proposed an optimization problem, in the form of a quadratic programming, where both control Lyapunov and control Barrier inequalities are formulated in the constraints.…”

Section: Introductionmentioning

confidence: 99%

“…It has been successfully implemented in the cruise control of autonomous vehicle as reported in [10]. Another direct approach is pursued in [14], [16] which is based on the direct merging of control Lyapunov function and control Barrier function. The merging process results in a control Lyapunov-Barrier function which can be used to stabilize the system with guaranteed safety by using the Sontag's universal control law.…”

Section: Introductionmentioning

confidence: 99%

“…Following definition in [16], the (autonomous) system (2) with u = 0 is called safe if for all x 0 ∈ X 0 and for all t ∈ R + , x(t) / ∈ D. Additionally, (2) with u = 0 is called (asymptotically) stable with guaranteed safety if it is both (asymptotically) stable and safe. Based on these notions, the problem of stabilization with guaranteed safety has been investigated in [16] where the control problem is to design a feedback law u = k(x) such that the closed loop system is safe and asymptotically stable, i.e.…”

Section: Introductionmentioning

confidence: 99%

“…Based on these notions, the problem of stabilization with guaranteed safety has been investigated in [16] where the control problem is to design a feedback law u = k(x) such that the closed loop system is safe and asymptotically stable, i.e. for all x 0 ∈ X 0 , we have that x(t) / ∈ D for all t and lim t→∞ x(t) = 0.…”

Section: Introductionmentioning

confidence: 99%

“…In [16], [21], the use of such barrier function B for control design that guarantees safety has been presented. It is shown in these works that the standard Lyapunov-based control design can directly be extended to solving the safety problem by replacing the Lyapunov function with the barrier one.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

On the new notion of input-to-state safety

Romdlony

Jayawardhana

2016

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

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Abstract-In this paper, we study robustness analysis of systems' safety with respect to external input (or disturbance) signals. To this end, we introduce a new notion of input-to-state safety (ISSf) which allows us to quantify the systems' safety robustness, in the same way as the application of input-tostate stability (ISS) notion for analyzing robustness of systems' stability. In particular, ISSf prescribes the relationship between the evolution of state distance to the unsafe set with the initial conditions and the bounded external input signals. Finally, we discuss how to combine this notion with ISS for analyzing the robustness of both systems' stability and safety.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

On the new notion of input-to-state safety

Romdlony

Jayawardhana

2016

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

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Improving autonomous vehicle in‐traffic safety using learning‐based action governor

Han

Tseng

et al. 2022

Adv Control Appl

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The Action Governor (AG) is a supervisory scheme augmenting a nominal control system in order to enhance the system's safety and performance. It acts as an action filter, monitoring the action commands generated by the nominal control policy and adjusting the ones that might lead to undesirable system behavior. In this article, we present an approach based on learning to developing an AG for autonomous vehicle (AV) decision policies to improve their safety for operating in mixed‐autonomy traffic (i.e., traffic involving both AVs and human‐operated vehicles (HVs)). To achieve this, we demonstrate that it is possible to train the AG in a traffic simulator that is capable of representing in‐traffic interactions among AVs and HVs. We illustrate the effectiveness of this learning‐based AG approach to improving AV in‐traffic safety through simulation case studies.

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Machine‐learning‐based construction of barrier functions and models for safe model predictive control

Chen

Christofides

2021

AIChE Journal

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In this paper, we propose a control Lyapunov‐barrier function‐based model predictive control method utilizing a feed‐forward neural network specified control barrier function (CBF) and a recurrent neural network (RNN) predictive model to stabilize nonlinear processes with input constraints, and to guarantee that safety requirements are met for all times. The nonlinear system is first modeled using RNN techniques, and a CBF is characterized by constructing a feed‐forward neural network (FNN) model with unique structures and properties. The FNN model for the CBF is trained based on data samples collected from safe and unsafe operating regions, and the resulting FNN model is verified to demonstrate that the safety properties of the CBF are satisfied. Given sufficiently small bounded modeling errors for both the FNN and the RNN models, the proposed control system is able to guarantee closed‐loop stability while preventing the closed‐loop states from entering unsafe regions in state‐space under sample‐and‐hold control action implementation. We provide the theoretical analysis for bounded unsafe sets in state‐space, and demonstrate the effectiveness of the proposed control strategy using a nonlinear chemical process example with a bounded unsafe region.

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Stabilization with guaranteed safety using Control Lyapunov–Barrier Function

Cited by 255 publications

References 37 publications

On the new notion of input-to-state safety

On the new notion of input-to-state safety

Improving autonomous vehicle in‐traffic safety using learning‐based action governor

Machine‐learning‐based construction of barrier functions and models for safe model predictive control

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