Achieving Performance and Safety in Large Scale Systems with Saturation using a Nonlinear System Level Synthesis Approach

Yu, Jing; Ho, Dimitar

doi:10.23919/acc45564.2020.9147577

Cited by 6 publications

(3 citation statements)

References 11 publications

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“…Before the development of the solution to Problem (P0), we first review the System Level Synthesis framework [12] that has seen much success in distributed [15], nonlinear [17], MPC [18], and adaptive [19] control design.…”

Section: Preliminaries On System Level Synthesismentioning

confidence: 99%

Localized and Distributed $\mathcal{H}_{2}$ State Feedback Control

Wang²,

Anderson

2021

2021 American Control Conference (ACC)

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Distributed linear control design is crucial for large-scale cyber-physical systems. It is generally desirable to both impose information exchange (communication) constraints on the distributed controller, and to limit the propagation of disturbances to a local region without cascading to the global network (localization). Recently proposed System Level Synthesis (SLS) theory provides a framework where such communication and localization requirements can be tractably incorporated in controller design and implementation. In this work, we derive a solution to the localized and distributed H2 state feedback control problem without resorting to Finite Impulse Response (FIR) approximation. Our proposed synthesis algorithm allows a column-wise decomposition of the resulting convex program, and is therefore scalable to arbitrary largescale networks. We demonstrate superior cost performance and computation time of the proposed procedure over previous methods via numerical simulation.

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Section: Preliminaries On System Level Synthesismentioning

confidence: 99%

Localized and Distributed $\mathcal{H}_{2}$ State Feedback Control

Wang²,

Anderson

2021

2021 American Control Conference (ACC)

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“…Nonlinear SLS can be applied to saturating linear systems [3] to provide distributed anti-windup controllers that accommodate state and input saturation constraints. An alternative approach to handling saturation is [17].…”

Section: B Sls For Nonlinearities and Saturationsmentioning

confidence: 99%

“…Since its inception, many extensions of the SLS framework have been developed, including works on nonlinear plants [2], [3], model predictive control (MPC) [4], [5], adaptive control [6], [7], and learning [8]- [10]; the core SLS ideas are useful and applicable to a variety of settings. In this paper, we hope to familiarize more researchers and practitioners with this powerful and scalable framework.…”

Section: Introductionmentioning

confidence: 99%

Frontiers in Scalable Distributed Control: SLS, MPC, and Beyond

Alonso

Doyle

2021

2021 American Control Conference (ACC)

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The System Level Synthesis (SLS) approach facilitates distributed control of large cyberphysical networks in an easy-to-understand, computationally scalable way. We present an overview of the SLS approach and its associated extensions in nonlinear control, MPC, adaptive control, and learning for control. To illustrate the effectiveness of SLS-based methods, we present a case study motivated by the power grid, with communication constraints, actuator saturation, disturbances, and changing setpoints. This simple but challenging case study necessitates the use of model predictive control (MPC); however, standard MPC techniques often scales poorly to large systems and incurs heavy computational burden. To address this challenge, we combine two SLS-based controllers to form a layered MPC-like controller. Our controller has constant computational complexity with respect to the system size, gives a 20-fold reduction in online computation requirements, and still achieves performance that is within 3% of the centralized MPC controller.

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Reactive Anticipatory Robot Skills with Memory

Girgin

Jankowski

Calinon

2023

Springer Proceedings in Advanced Robotics

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Robot programming tools ranging from inverse kinematics (IK) to model predictive control (MPC) are most often described as constrained optimization problems. Even though there are currently many commercially-available second-order solvers, robotics literature recently focused on efficient implementations and improvements over these solvers for real-time robotic applications. However, most often, these implementations stay problem-specific and are not easy to access or implement, or do not exploit the geometric aspect of the robotics problems. In this work, we propose to solve these problems using a fast, easy-to-implement first-order method that fully exploits the geometric constraints via Euclidean projections, called Augmented Lagrangian Spectral Projected Gradient Descent (ALSPG). We show that 1. using projections instead of full constraints and gradients improves the performance of the solver and 2. ALSPG stays competitive to the standard second-order methods such as iLQR in the unconstrained case. We showcase these results with IK and motion planning problems on simulated examples and with an MPC problem on a 7-axis manipulator experiment.

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Achieving Performance and Safety in Large Scale Systems with Saturation using a Nonlinear System Level Synthesis Approach

Cited by 6 publications

References 11 publications

Localized and Distributed $\mathcal{H}_{2}$ State Feedback Control

Localized and Distributed $\mathcal{H}_{2}$ State Feedback Control

Frontiers in Scalable Distributed Control: SLS, MPC, and Beyond

Reactive Anticipatory Robot Skills with Memory

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