Distributed Feedback Optimisation for Robotic Coordination

Feedback optimisation is an emerging technique aiming at steering a system to an optimal steady state for a given objective function. We show that it is possible to employ this control strategy in a distributed manner. Moreover, we prove asymptotic convergence to the set of optimal configurations. To this scope, we show that exponential stability is needed only for the portion of the state that affects the objective function. This is showcased by driving a swarm of agents towards a target location while maintaining a target formation. Finally, we provide a sufficient condition on the topological structure of the specified formation to guarantee convergence of the swarm in formation around the target location.

[1]  Marcello Colombino,et al.  Towards robustness guarantees for feedback-based optimization , 2019, 2019 IEEE 58th Conference on Decision and Control (CDC).

[2]  Charles R. Johnson,et al.  Matrix analysis , 1985, Statistical Inference for Engineers and Data Scientists.

[3]  Adrian Hauswirth,et al.  Anti-Windup Approximations of Oblique Projected Dynamics for Feedback-based Optimization , 2020, 2003.00478.

[4]  Jing Wang,et al.  A control perspective for centralized and distributed convex optimization , 2011, IEEE Conference on Decision and Control and European Control Conference.

[5]  Gabriela Hug,et al.  Time-varying Projected Dynamical Systems with Applications to Feedback Optimization of Power Systems , 2018, 2018 IEEE Conference on Decision and Control (CDC).

[6]  Florian Dörfler,et al.  Non-Convex Feedback Optimization With Input and Output Constraints , 2020, IEEE Control Systems Letters.

[7]  P. Olver Nonlinear Systems , 2013 .

[8]  Morris W. Hirsch 7 – Nonlinear Systems , 2013 .

[9]  Chin-Yao Chang,et al.  Saddle-Flow Dynamics for Distributed Feedback-Based Optimization , 2019, IEEE Control Systems Letters.

[10]  John Lygeros,et al.  Sampled-Data Online Feedback Equilibrium Seeking: Stability and Tracking , 2021, 2021 60th IEEE Conference on Decision and Control (CDC).

[11]  Henrik Sandberg,et al.  A Survey of Distributed Optimization and Control Algorithms for Electric Power Systems , 2017, IEEE Transactions on Smart Grid.

[12]  Craig W. Reynolds Flocks, herds, and schools: a distributed behavioral model , 1987, SIGGRAPH.

[13]  Andrey Bernstein,et al.  Online Primal-Dual Methods With Measurement Feedback for Time-Varying Convex Optimization , 2018, IEEE Transactions on Signal Processing.

[14]  Georgios B. Giannakis,et al.  Time-Varying Convex Optimization: Time-Structured Algorithms and Applications , 2020, Proceedings of the IEEE.

[15]  F. Ramponi,et al.  Lecture Notes on Linear System Theory , 2015 .

[16]  Liam S. P. Lawrence The Optimal Steady-State Control Problem , 2019 .

[17]  Florian Dörfler,et al.  On the Differentiability of Projected Trajectories and the Robust Convergence of Non-Convex Anti-Windup Gradient Flows , 2020, IEEE Control Systems Letters.

[18]  Jorge Cortes,et al.  Time-Varying Optimization of LTI Systems Via Projected Primal-Dual Gradient Flows , 2021, IEEE Transactions on Control of Network Systems.

[19]  Gabriela Hug,et al.  Optimization Algorithms as Robust Feedback Controllers , 2021, Annu. Rev. Control..

[20]  Florian Dörfler,et al.  Experimental Validation of Feedback Optimization in Power Distribution Grids , 2019, ArXiv.

[21]  Steven H. Low,et al.  An Online Gradient Algorithm for Optimal Power Flow on Radial Networks , 2016, IEEE Journal on Selected Areas in Communications.

[22]  Sergio Grammatico,et al.  Learning generalized Nash equilibria in multi-agent dynamical systems via extremum seeking control , 2020, Autom..

[23]  Andrey Bernstein,et al.  A Feedback-Based Regularized Primal-Dual Gradient Method for Time-Varying Nonconvex Optimization , 2018, 2018 IEEE Conference on Decision and Control (CDC).

[24]  Reza Olfati-Saber,et al.  Flocking for multi-agent dynamic systems: algorithms and theory , 2006, IEEE Transactions on Automatic Control.

[25]  Marcello Colombino,et al.  Online Optimization as a Feedback Controller: Stability and Tracking , 2018, IEEE Transactions on Control of Network Systems.

[26]  Gabriela Hug,et al.  Timescale Separation in Autonomous Optimization , 2019, IEEE Transactions on Automatic Control.

[27]  Gabriela Hug,et al.  Stability of Dynamic Feedback optimization with Applications to Power Systems , 2018, 2018 56th Annual Allerton Conference on Communication, Control, and Computing (Allerton).

[28]  Emiliano Dall'Anese,et al.  Optimal power flow pursuit , 2016, 2016 American Control Conference (ACC).