检索结果-内蒙古大学图书馆

Learning Robust Data-based LQG controllers from Noisy Data

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Liu, Wenjie Sun, Jian Wang, Gang Bullo, Francesco Chen, Jie The National Key Lab of Autonomous Intelligent Unmanned Systems School of Automation Beijing Institute of Technology Beijing100081 China The Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China The Mechanical Engineering Department The Center of Control Dynamical Systems and Computation UC Santa Barbara CA93106-5070 United States The Department of Control Science and Engineering Tongji University Shanghai201804 China The National Key Lab of Autonomous Intelligent Unmanned Systems Beijing Institute of Technology Beijing100081 China

This paper addresses the joint state estimation and control problems for unknown linear time-invariant systems subject to both process and measurement noise. The aim is to redesign the linear quadratic Gaussian (LQG) controller based solely on data. The LQG controller comprises a linear quadratic regulator (LQR) and a steady-state Kalman observer;while the data-based LQR design problem has been previously studied, constructing the Kalman gain and the LQG controller from noisy data presents a novel challenge. In this work, a data-based formulation for computing the steady-state Kalman gain is proposed based on semi-definite programming (SDP) using some noise-free input-state-output data. Additionally, a data-based LQG controller is developed, which is shown to be equivalent to the model-based LQG controller. For cases where offline data are corrupted by noise, a robust data-based observer gain is constructed by tackling a relaxed SDP. The proposed controllers are proven to achieve robust global exponential stability (RGES) for state estimation and input-to-state practical stability (ISpS) under standard conditions. Finally, numerical tests are conducted to validate the proposed controllers' correctness and effectiveness. © 2023, CC BY.

关键词： Linear time-invariant system

Contraction Analysis of Hopfield Neural Networks with Hebbian Learning

学校读者我要写书评

暂无评论

Contraction Analysis of Hopfield Neural Networks with Hebbia...

IEEE Conference on Decision and control

作者： Veronica Centorrino Francesco Bullo Giovanni Russo Scuola Superiore Meridionale University of Naples Federico II Italy Department of Mechanical Engineering Center for Control Dynamical Systems and Computation University of California Santa Barbara USA Department of Information and Electric Engineering and Applied Mathematics University of Salerno Italy

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

Motivated by advances in neuroscience and machine learning, this paper is concerned with the modeling and analysis of Hopfield neural networks with dynamic recurrent connections undergoing Hebbian learning. To capture the synaptic sparsity of neural circuits, we propose a low dimensional formulation for the model and then characterize its key dynamical properties. First, we give a biologically-inspired forward invariance result. Then, we give sufficient conditions for the non-Euclidean contractivity of the model. Our contraction analysis leads to stability and robustness of time-varying trajectories – for networks with both excitatory and inhibitory synapses governed by both Hebbian and anti-Hebbian rules. Our proposed contractivity test is based upon biologically meaningful quantities, e.g., neural and synaptic decay rate, maximum out-degree, and the maximum synaptic strength. Finally, we show that the model satisfies Dale’s principle. The effectiveness of our results is illustrated via a numerical example.

关键词： Sufficient conditions Biological system modeling Hopfield neural networks Stability analysis Robustness Trajectory Integrated circuit modeling

Non-Euclidean Monotone Operator Theory with Applications to Recurrent Neural Networks

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Davydov, Alexander Jafarpour, Saber Proskurnikov, Anton V. Bullo, Francesco The Center for Control Dynamical Systems and Computation University of California Santa Barbara93106-5070 United States The School of Electrical and Computer Engineering Georgia Institute of Technology United States The Department of Electronics and Telecommunications Politecnico di Torino Turin Italy

We provide a novel transcription of monotone operator theory to the non-Euclidean finite-dimensional spaces l1 and l∞. We first establish properties of mappings which are monotone with respect to the non-Euclidean norms l1 or l∞. In analogy with their Euclidean counterparts, mappings which are monotone with respect to a non-Euclidean norm are amenable to numerous algorithms for computing their zeros. We demonstrate that several classic iterative methods for computing zeros of monotone operators are directly applicable in the non-Euclidean framework. We present a case-study in the equilibrium computation of recurrent neural networks and demonstrate that casting the computation as a suitable operator splitting problem improves convergence rates. Copyright © 2022, The Authors. All rights reserved.

关键词： Recurrent neural networks

Biologically plausible robust control with Neural Network weight reset for unmanned aircraft systems under impulsive disturbances

学校读者我要写书评

暂无评论

Biologically plausible robust control with Neural Network we...

American control Conference (ACC)

作者： Ignacio Rubio Scola Luis Rodolfo Garcia Carrillo Andrew T. Sornborger João P. Hespanha Department of Industrial Products Engineering INTI CONICET and UNR Rosario Argentina Klipsch School of Electrical and Computer Engineering New Mexico State University Las Cruces NM USA Los Alamos National Laboratory Los Alamos NM USA Center for Control Dynamical Systems and Computation University of California Santa Barbara USA

Self-learning control techniques mimicking the functionality of the limbic system in the mammalian brain have shown advantages in terms of superior learning ability and low computational cost. However, accompanying stability analyses and mathematical proofs rely on unrealistic assumptions which limit not only the performance, but also the implementation of such controllers in real-world scenarios. In this work the limbic system inspired control (LISIC) framework is revisited, introducing three contributions that facilitate the implementation of this type of controller in real-time. First, an extension enabling the implementation of LISIC to the domain of SISO affine systems is proposed. Second, a strategy for resetting the controller’s Neural Network (NN) weights is developed, in such a way that now it is possible to deal with piece-wise smooth references and impulsive perturbations. And third, for the case when a nominal model of the system is available, a technique is proposed to compute a set of optimal NN reset weight values by solving a convex constrained optimization problem. Numerical simulations addressing the stabilization of an unmanned aircraft system via the robust LISIC demonstrate the advantages obtained when adopting the extension to SISO systems and the two NN weight reset strategies.

关键词：

Multiplayer Homicidal Chauffeur Reach-Avoid Games: A Pursuit Enclosure Function Approach

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Yan, Rui Duan, Xiaoming Zou, Rui He, Xin Shi, Zongying Bullo, Francesco Department of Computer Science University of Oxford OxfordOX1 3QD United Kingdom Department of Automation Tsinghua University Beijing100084 China Department of Automation Shanghai Jiao Tong University Shanghai200240 China Center of Control Dynamical Systems and Computation University of California Santa Barbara United States

This paper presents a multiplayer Homicidal Chauffeur reach-avoid differential game, which involves Dubins-car pursuers and simple-motion evaders. The goal of the pursuers is to cooperatively protect a planar convex region from the evaders, who strive to reach the region. We propose a cooperative strategy for the pursuers based on subgames for multiple pursuers against one evader and optimal task allocation. We introduce pursuit enclosure functions (PEFs) and propose a new enclosure region pursuit (ERP) winning approach that supports the forward analysis for the strategy synthesis in the subgames. We show that if a pursuit coalition is able to defend the region against an evader under the ERP winning, then no more than two pursuers in the coalition are necessarily needed. We also propose a steer-to-ERP approach to certify the ERP winning and synthesize the ERP winning strategy. To implement the strategy, we introduce a positional PEF and provide the necessary parameters, states, and strategies that ensure the ERP winning for both one pursuer and two pursuers against one evader. Additionally, we formulate a binary integer program using the subgame outcomes to maximize the captured evaders in the ERP winning for the pursuit task allocation. Finally, we propose a multiplayer receding-horizon strategy where the ERP winnings are checked in each horizon, the task is allocated, and the strategies of the pursuers are determined. Numerical examples are provided to illustrate the results. © 2023, CC BY.

关键词： Enclosures

COMBINING PHYSICS AND MACHINE LEARNING FOR NETWORK FLOW ESTIMATION 9

学校读者我要写书评

暂无评论

COMBINING PHYSICS AND MACHINE LEARNING FOR NETWORK FLOW ESTI...

9th International Conference on Learning Representations, ICLR 2021

作者： Silva, Arlei Kocayusufoglu, Furkan Jafarpour, Saber Bullo, Francesco Swami, Ananthram Singh, Ambuj Computer Science Department UC Santa BarbaraCA93106-5110 United States Mechanical Engineering Department The Center of Control Dynamical Systems and Computation UC Santa BarbaraCA93106-5070 United States U.S. Army Research Lab AdelphiMD20783 United States

The flow estimation problem consists of predicting missing edge flows in a network (e.g., traffic, power, and water) based on partial observations. These missing flows depend both on the underlying physics (edge features and a flow conservation law) as well as the observed edge flows. This paper introduces an optimization framework for computing missing edge flows and solves the problem using bilevel optimization and deep learning. More specifically, we learn regularizers that depend on edge features (e.g., number of lanes in a road, resistance of a power line) using neural networks. Empirical results show that our method accurately predicts missing flows, outperforming the best baseline, and is able to capture relevant physical properties in traffic and power networks. © 2021 ICLR 2021 - 9th International Conference on Learning Representations. All rights reserved.

关键词： Deep learning

Convergence, Consensus and Dissensus in the Weighted-Median Opinion Dynamics

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Mei, Wenjun Hendrickx, Julien M. Chen, Ge Bullo, Francesco Dörfler, Florian The Department of Mechanics and Engineering Science Peking University China The Institute of Information and Communication Technologies Electronics and Applied Mathematics UCLouvain Belgium The Academy of Mathematics and Systems Science Chinese Academy of Sciences China The Center of Control Dynamical-Systems and Computation University of California Santa Barbara United States Automatic Control Laboratory ETH Zurich Switzerland

Mechanistic and tractable mathematical models play a key role in understanding how social influence shapes public opinions. Recently, a weighted-median mechanism has been proposed as a new micro-foundation of opinion dynamics and validated via experimental data. Numerical studies also indicate that this new mechanism recreates some non-trivial real-world features of opinion evolution. In this paper, we conduct a thorough theoretical analysis of the weighted-median opinion dynamics. We fully characterize the set of all equilibria, and we establish the almost-sure finite-time convergence for any initial condition. Moreover, we prove a necessary and sufficient graph-theoretic condition for the almost-sure convergence to consensus, as well as a sufficient graph-theoretic condition for almost-sure persistent dissensus. It turns out that the weighted-median opinion dynamics, despite its simplicity in form, exhibit rich dynamical behavior that depends on some delicate network structures. To complement our sufficient conditions for almost-sure dissensus, we further prove that, given the influence network, determining whether the system almost surely achieves persistent dissensus is NP-hard, which reflects the complexity the network topology contributes to opinion *** Codes 93E03, 93A30, 91D30 Copyright © 2022, The Authors. All rights reserved.

关键词： Network topology

Optimization Strategy of Bio-Inspired Metaheuristic Algorithms Tuned PID controller for PMBDC Actuated Robotic Manipulator

学校读者我要写书评

暂无评论

Procedia Computer Science 2020年 171卷 2040-2049页

作者： Sudarshan K. Valluru Madhusudan Singh Center for Control of Dynamical Systems and Computation Department of Electrical Engineering Delhi Technological University Delhi-110042 India

The present article makes an attempt to evaluate the computational performance of genetic meta heuristic optimized control algorithms. Here, the multi-objective bio inspired algorithm (MOGA) and adaptive particle swarm optimization (APSO) algorithm are used to tune linear PID (L-PID) and nonlinear PID(NL-PID) controllers to implement performance and execution control of permanent magnet brushed DC (PMBDC) motor actuated robotic manipulator. The MOGA, APSO optimised nonlinear and linear PID controllers have been validated for their response efficacy and compared in respect of the steady-state error, overshoot and settling time of PMBDC driven robotic Manipulator. The efficacy of metaheuristics such as APSO and MOGA tuned NL-PID controller are better as compared to the L- PID controlled objects. Experimental results show that the NL-PID controller mends the controlled objects’ performace with a reduction in both the overshoot as well as the settling time, if tuned either with MOGA or APSO for L-PID controllers while NL-PID controller tuned with APSO gives satisfactory dynamic response of the system.

关键词： L-PID NL-PID MOGA APSO Robotic Manipulator

Experimental validation and control of Laboratory aerial system by IC741-IOPID and IC741-FOP $I^{\lambda}$ controllers: An Analog approach

学校读者我要写书评

暂无评论

Experimental validation and control of Laboratory aerial sys...

International Conference on Computing and Networking Technology (ICCNT)

作者： Sudarshan K. Valluru Gaurav Kumar Rajul Kumar Ankit Lal Meena Center for Control of Dynamical Systems and Computation Delhi Technological University Delhi India

ISBN: (数字)9781728168517

ISBN: (纸本)9781728168524

As control and trajectory tracking of hovering aerial vehicles are remarkable objective among researchers and industries. So in this paper, a validated attempt is made to achieve the precise and desired control of the Twin Rotor Aerial System (TRAS) over is motion in horizontal as well as vertical planes. To obtain pitch and yaw angles stabilization and desired trajectory tracking of TRAS the IO-PID and fractional order FOP I λ controllers are designed using IC741 operational amplifiers. The designing and transforming integer order scheme into fractional-order crone1 and continued fraction expansion (CFE) methods are used, which ultimately replace feedback capacitor in I λ control scheme with an RC ladder fractance device. The designed controllers are tested with a defined disturbance provided to pitch and yaw trajectories and the time taken by both the controllers are compared for testing of stabilization control performances. It is observed from results that IC741- I λ controller gives better trajectory tracking and stabilization than IC741-IOPID.

关键词：