Systems and Control
New submissions
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New submissions for Mon, 6 May 24
- [1] arXiv:2405.01681 [pdf, other]
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Title: Accounting for the Effects of Probabilistic Uncertainty During Fast Charging of Lithium-ion BatteriesAuthors: Minsu Kim, Joachim Schaeffer, Marc D. Berliner, Berta Pedret Sagnier, Rolf Findeisen, Richard D. BraatzComments: 6 pages, 5 figures, accepted for ACC 2024Subjects: Systems and Control (eess.SY)
Batteries are nonlinear dynamical systems that can be modeled by Porous Electrode Theory models. The aim of optimal fast charging is to reduce the charging time while keeping battery degradation low. Most past studies assume that model parameters and ambient temperature are a fixed known value and that all PET model parameters are perfectly known. In real battery operation, however, the ambient temperature and the model parameters are uncertain. To ensure that operational constraints are satisfied at all times in the context of model-based optimal control, uncertainty quantification is required. Here, we analyze optimal fast charging for modest uncertainty in the ambient temperature and 23 model parameters. Uncertainty quantification of the battery model is carried out using non-intrusive polynomial chaos expansion and the results are verified with Monte Carlo simulations. The method is investigated for a constant current--constant voltage charging strategy for a battery for which the strategy is known to be standard for fast charging subject to operating below maximum current and charging constraints. Our results demonstrate that uncertainty in ambient temperature results in violations of constraints on the voltage and temperature. Our results identify a subset of key parameters that contribute to fast charging among the overall uncertain parameters. Additionally, it is shown that the constraints represented by voltage, temperature, and lithium-plating overpotential are violated due to uncertainties in the ambient temperature and parameters. The C-rate and charge constraints are then adjusted so that the probability of violating the degradation acceleration condition is below a pre-specified value. This approach demonstrates a computationally efficient approach for determining fast-charging protocols that take probabilistic uncertainties into account.
- [2] arXiv:2405.01753 [pdf, other]
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Title: A Feedback Linearized Model Predictive Control Strategy for Input-Constrained Self-Driving CarsComments: Preprint of a manuscript currently under review for TCTSSubjects: Systems and Control (eess.SY); Optimization and Control (math.OC)
This paper proposes a novel real-time affordable solution to the trajectory tracking control problem for self-driving cars subject to longitudinal and steering angular velocity constraints. To this end, we develop a dual-mode Model Predictive Control (MPC) solution starting from an input-output feedback linearized description of the vehicle kinematics. First, we derive the state-dependent input constraints acting on the linearized model and characterize their worst-case time-invariant inner approximation. Then, a dual-mode MPC is derived to be real-time affordable and ensuring, by design, constraints fulfillment, recursive feasibility, and uniformly ultimate boundedness of the tracking error in an ad-hoc built robust control invariant region. The approach's effectiveness and performance are experimentally validated via laboratory experiments on a Quanser Qcar. The obtained results show that the proposed solution is computationally affordable and with tracking capabilities that outperform two alternative control schemes.
- [3] arXiv:2405.01889 [pdf, ps, other]
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Title: Reinforcement Learning control strategies for Electric Vehicles and Renewable energy sources Virtual Power PlantsComments: DAI-Labor of Technische Universit\"at Berlin Master thesisSubjects: Systems and Control (eess.SY)
The increasing demand for direct electric energy in the grid is also tied to the increase of Electric Vehicle (EV) usage in the cities, which eventually will totally substitute combustion engine Vehicles. Nevertheless, this high amount of energy required, which is stored in the EV batteries, is not always used and it can constitute a virtual power plant on its own. Bidirectional EVs equipped with batteries connected to the grid can therefore charge or discharge energy depending on public needs, producing a smart shift of energy where and when needed. EVs employed as mobile storage devices can add resilience and supply/demand balance benefits to specific loads, in many cases as part of a Microgrid (MG). Depending on the direction of the energy transfer, EVs can provide backup power to households through vehicle-to-house (V2H) charging, or storing unused renewable power through renewable-to-vehicle (RE2V) charging. V2H and RE2V solutions can complement renewable power sources like solar photovoltaic (PV) panels and wind turbines (WT), which fluctuate over time, increasing the self-consumption and autarky. The concept of distributed energy resources (DERs) is becoming more and more present and requires new solutions for the integration of multiple complementary resources with variable supply over time. The development of these ideas is coupled with the growth of new AI techniques that will potentially be the managing core of such systems. Machine learning techniques can model the energy grid environment in such a flexible way that constant optimization is possible. This fascinating working principle introduces the wider concept of an interconnected, shared, decentralized grid of energy. This research on Reinforcement Learning control strategies for Electric Vehicles and Renewable energy sources Virtual Power Plants focuses on providing solutions for such energy supply optimization models.
- [4] arXiv:2405.01916 [pdf, other]
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Title: Multi-objective Optimal Trade-off Between V2G Activities and Battery Degradation in Electric Mobility-as-a-Service SystemsSubjects: Systems and Control (eess.SY); Optimization and Control (math.OC)
This paper presents optimization models for electric Mobility-as-a-Service systems, whereby electric vehicles not only provide on-demand mobility, but also perform charging and Vehicle-to-Grid (V2G) operations to enhance the fleet operator profitability. Specifically, we formulate the optimal fleet operation problem as a mixed-integer linear program, with the objective combining of operational costs and revenues generated from servicing requests and grid electricity sales. Our cost function explicitly captures battery price and degradation, reflecting their impact on the fleet total cost of ownership due to additional charging and discharging activities. Simulation results for Eindhoven, The Netherlands, show that integrating V2G activities does not compromise the number of travel requests being served. Moreover, we emphasize the significance of accounting for battery degradation, as the costs associated with it can potentially outweigh the revenues stemming from V2G operations.
- [5] arXiv:2405.02030 [pdf, other]
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Title: Obstacle Avoidance of Autonomous Vehicles: An LPVMPC with Scheduling Trust RegionSubjects: Systems and Control (eess.SY)
Reference tracking and obstacle avoidance rank among the foremost challenging aspects of autonomous driving. This paper proposes control designs for solving reference tracking problems in autonomous driving tasks while considering static obstacles. We suggest a model predictive control (MPC) strategy that evades the computational burden of nonlinear nonconvex optimization methods after embedding the nonlinear model equivalently to a linear parameter-varying (LPV) formulation using the so-called scheduling parameter. This allows optimal and fast solutions of the underlying convex optimization scheme as a quadratic program (QP) at the expense of losing some performance due to the uncertainty of the future scheduling trajectory over the MPC horizon. Also, to ensure that the modeling error due to the application of the scheduling parameter predictions does not become significant, we propose the concept of scheduling trust region by enforcing further soft constraints on the states and inputs. A consequence of using the new constraints in the MPC is that we construct a region in which the scheduling parameter updates in two consecutive time instants are trusted for computing the system matrices, and therefore, the feasibility of the MPC optimization problem is retained. We test the method in different scenarios and compare the results to standard LPVMPC as well as nonlinear MPC (NMPC) schemes.
- [6] arXiv:2405.02184 [pdf, other]
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Title: Hybrid Lyapunov-based feedback stabilization of bipedal locomotion based on reference spreadingSubjects: Systems and Control (eess.SY); Dynamical Systems (math.DS)
We propose a hybrid formulation of the linear inverted pendulum model for bipedal locomotion, where the foot switches are triggered based on the center of mass position, removing the need for pre-defined footstep timings. Using a concept similar to reference spreading, we define nontrivial tracking error coordinates induced by our hybrid model. These coordinates enjoy desirable linear flow dynamics and rather elegant jump dynamics perturbed by a suitable extended class ${\mathcal K}_\infty$ function of the position error. We stabilize this hybrid error dynamics using a saturated feedback controller, selecting its gains by solving a convex optimization problem. We prove local asymptotic stability of the tracking error and provide a certified estimate of the basin of attraction, comparing it with a numerical estimate obtained from the integration of the closed-loop dynamics. Simulations on a full-body model of a real robot show the practical applicability of the proposed framework and its advantages with respect to a standard model predictive control formulation.
Cross-lists for Mon, 6 May 24
- [7] arXiv:2405.01690 (cross-list from cs.NI) [pdf, other]
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Title: Addressing the Load Estimation Problem: Cell Switching in HAPS-Assisted Sustainable 6G NetworksComments: arXiv admin note: substantial text overlap with arXiv:2402.04386Subjects: Networking and Internet Architecture (cs.NI); Systems and Control (eess.SY)
This study aims to introduce and address the problem of traffic load estimation in the cell switching concept within the evolving landscape of vertical heterogeneous networks (vHetNets). The problem is that the practice of cell switching faces a significant challenge due to the lack of accurate data on the traffic load of sleeping small base stations (SBSs). This problem makes the majority of the studies in the literature, particularly those employing load-dependent approaches, impractical due to their basic assumption of perfect knowledge of the traffic loads of sleeping SBSs for the next time slot. Rather than developing another advanced cell switching algorithm, this study investigates the impacts of estimation errors and explores possible solutions through established methodologies in a novel vHetNet environment that includes the integration of a high altitude platform (HAPS) as a super macro base station (SMBS) into the terrestrial network. In other words, this study adopts a more foundational perspective, focusing on eliminating a significant obstacle for the application of advanced cell switching algorithms. To this end, we explore the potential of three distinct spatial interpolation-based estimation schemes: random neighboring selection, distance-based selection, and clustering-based selection. Utilizing a real dataset for empirical validations, we evaluate the efficacy of our proposed traffic load estimation schemes. Our results demonstrate that the multi-level clustering (MLC) algorithm performs exceptionally well, with an insignificant difference (i.e., 0.8%) observed between its estimated and actual network power consumption, highlighting its potential to significantly improve energy efficiency in vHetNets.
- [8] arXiv:2405.01758 (cross-list from cs.RO) [pdf, other]
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Title: CGD: Constraint-Guided Diffusion Policies for UAV Trajectory PlanningAuthors: Kota Kondo, Andrea Tagliabue, Xiaoyi Cai, Claudius Tewari, Olivia Garcia, Marcos Espitia-Alvarez, Jonathan P. HowComments: 8 pages, 3 figuresSubjects: Robotics (cs.RO); Machine Learning (cs.LG); Systems and Control (eess.SY)
Traditional optimization-based planners, while effective, suffer from high computational costs, resulting in slow trajectory generation. A successful strategy to reduce computation time involves using Imitation Learning (IL) to develop fast neural network (NN) policies from those planners, which are treated as expert demonstrators. Although the resulting NN policies are effective at quickly generating trajectories similar to those from the expert, (1) their output does not explicitly account for dynamic feasibility, and (2) the policies do not accommodate changes in the constraints different from those used during training.
To overcome these limitations, we propose Constraint-Guided Diffusion (CGD), a novel IL-based approach to trajectory planning. CGD leverages a hybrid learning/online optimization scheme that combines diffusion policies with a surrogate efficient optimization problem, enabling the generation of collision-free, dynamically feasible trajectories. The key ideas of CGD include dividing the original challenging optimization problem solved by the expert into two more manageable sub-problems: (a) efficiently finding collision-free paths, and (b) determining a dynamically-feasible time-parametrization for those paths to obtain a trajectory. Compared to conventional neural network architectures, we demonstrate through numerical evaluations significant improvements in performance and dynamic feasibility under scenarios with new constraints never encountered during training. - [9] arXiv:2405.01792 (cross-list from cs.RO) [pdf, other]
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Title: Learning Robust Autonomous Navigation and Locomotion for Wheeled-Legged RobotsAuthors: Joonho Lee, Marko Bjelonic, Alexander Reske, Lorenz Wellhausen, Takahiro Miki, Marco HutterJournal-ref: Science Robotics, 2024, Vol 9, Issue 89Subjects: Robotics (cs.RO); Machine Learning (cs.LG); Systems and Control (eess.SY)
Autonomous wheeled-legged robots have the potential to transform logistics systems, improving operational efficiency and adaptability in urban environments. Navigating urban environments, however, poses unique challenges for robots, necessitating innovative solutions for locomotion and navigation. These challenges include the need for adaptive locomotion across varied terrains and the ability to navigate efficiently around complex dynamic obstacles. This work introduces a fully integrated system comprising adaptive locomotion control, mobility-aware local navigation planning, and large-scale path planning within the city. Using model-free reinforcement learning (RL) techniques and privileged learning, we develop a versatile locomotion controller. This controller achieves efficient and robust locomotion over various rough terrains, facilitated by smooth transitions between walking and driving modes. It is tightly integrated with a learned navigation controller through a hierarchical RL framework, enabling effective navigation through challenging terrain and various obstacles at high speed. Our controllers are integrated into a large-scale urban navigation system and validated by autonomous, kilometer-scale navigation missions conducted in Zurich, Switzerland, and Seville, Spain. These missions demonstrate the system's robustness and adaptability, underscoring the importance of integrated control systems in achieving seamless navigation in complex environments. Our findings support the feasibility of wheeled-legged robots and hierarchical RL for autonomous navigation, with implications for last-mile delivery and beyond.
- [10] arXiv:2405.01794 (cross-list from cs.RO) [pdf, ps, other]
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Title: New design of smooth PSO-IPF navigator with kinematic constraintsSubjects: Robotics (cs.RO); Systems and Control (eess.SY)
Robotic applications across industries demand advanced navigation for safe and smooth movement. Smooth path planning is crucial for mobile robots to ensure stable and efficient navigation, as it minimizes jerky movements and enhances overall performance Achieving this requires smooth collision-free paths. Partial Swarm Optimization (PSO) and Potential Field (PF) are notable path-planning techniques, however, they may struggle to produce smooth paths due to their inherent algorithms, potentially leading to suboptimal robot motion and increased energy consumption. In addition, while PSO efficiently explores solution spaces, it generates long paths and has limited global search. On the contrary, PF methods offer concise paths but struggle with distant targets or obstacles. To address this, we propose Smoothed Partial Swarm Optimization with Improved Potential Field (SPSO-IPF), combining both approaches and it is capable of generating a smooth and safe path. Our research demonstrates SPSO-IPF's superiority, proving its effectiveness in static and dynamic environments compared to a mere PSO or a mere PF approach.
- [11] arXiv:2405.02034 (cross-list from math.OC) [pdf, other]
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Title: Multi-Agent Coverage Control on Surfaces Using Conformal MappingSubjects: Optimization and Control (math.OC); Systems and Control (eess.SY)
Real-time environmental monitoring using a multi-agent system (MAS) has long been a focal point of cooperative control. It is still a challenging task to provide cost-effective services for potential emergencies in surface environments. This paper explores the transformation of a general surface into a two-dimensional (2D) disk through the construction of a conformal mapping. Multiple agents are strategically deployed within the mapped convex disk, followed by mapping back to the original surface environment. This approach circumvents the complexities associated with handling the difficulties and intricacies of path planning. Technical analysis encompasses the design of distributed control laws and the method to eliminate distortions introduced by the mapping. Moreover, the developed coverage algorithm is applied to a scenario of monitoring surface deformation. Finally, the effectiveness of the proposed algorithm is validated through numerical simulations.
- [12] arXiv:2405.02044 (cross-list from cs.LG) [pdf, other]
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Title: Zero-Sum Positional Differential Games as a Framework for Robust Reinforcement Learning: Deep Q-Learning ApproachSubjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Science and Game Theory (cs.GT); Systems and Control (eess.SY); Optimization and Control (math.OC)
Robust Reinforcement Learning (RRL) is a promising Reinforcement Learning (RL) paradigm aimed at training robust to uncertainty or disturbances models, making them more efficient for real-world applications. Following this paradigm, uncertainty or disturbances are interpreted as actions of a second adversarial agent, and thus, the problem is reduced to seeking the agents' policies robust to any opponent's actions. This paper is the first to propose considering the RRL problems within the positional differential game theory, which helps us to obtain theoretically justified intuition to develop a centralized Q-learning approach. Namely, we prove that under Isaacs's condition (sufficiently general for real-world dynamical systems), the same Q-function can be utilized as an approximate solution of both minimax and maximin Bellman equations. Based on these results, we present the Isaacs Deep Q-Network algorithms and demonstrate their superiority compared to other baseline RRL and Multi-Agent RL algorithms in various environments.
- [13] arXiv:2405.02131 (cross-list from eess.SP) [pdf, other]
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Title: Physics-informed generative neural networks for RF propagation prediction with application to indoor body perceptionSubjects: Signal Processing (eess.SP); Artificial Intelligence (cs.AI); Systems and Control (eess.SY)
Electromagnetic (EM) body models designed to predict Radio-Frequency (RF) propagation are time-consuming methods which prevent their adoption in strict real-time computational imaging problems, such as human body localization and sensing. Physics-informed Generative Neural Network (GNN) models have been recently proposed to reproduce EM effects, namely to simulate or reconstruct missing data or samples by incorporating relevant EM principles and constraints. The paper discusses a Variational Auto-Encoder (VAE) model which is trained to reproduce the effects of human motions on the EM field and incorporate EM body diffraction principles. Proposed physics-informed generative neural network models are verified against both classical diffraction-based EM tools and full-wave EM body simulations.
- [14] arXiv:2405.02180 (cross-list from cs.LG) [pdf, other]
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Title: A Flow-Based Model for Conditional and Probabilistic Electricity Consumption Profile Generation and PredictionSubjects: Machine Learning (cs.LG); Systems and Control (eess.SY)
Residential Load Profile (RLP) generation and prediction are critical for the operation and planning of distribution networks, particularly as diverse low-carbon technologies are increasingly integrated. This paper introduces a novel flow-based generative model, termed Full Convolutional Profile Flow (FCPFlow), which is uniquely designed for both conditional and unconditional RLP generation, and for probabilistic load forecasting. By introducing two new layers--the invertible linear layer and the invertible normalization layer--the proposed FCPFlow architecture shows three main advantages compared to traditional statistical and contemporary deep generative models: 1) it is well-suited for RLP generation under continuous conditions, such as varying weather and annual electricity consumption, 2) it shows superior scalability in different datasets compared to traditional statistical, and 3) it also demonstrates better modeling capabilities in capturing the complex correlation of RLPs compared with deep generative models.
- [15] arXiv:2405.02198 (cross-list from cs.RO) [pdf, other]
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Title: The Cambridge RoboMaster: An Agile Multi-Robot Research PlatformSubjects: Robotics (cs.RO); Multiagent Systems (cs.MA); Systems and Control (eess.SY)
Compact robotic platforms with powerful compute and actuation capabilities are key enablers for practical, real-world deployments of multi-agent research. This article introduces a tightly integrated hardware, control, and simulation software stack on a fleet of holonomic ground robot platforms designed with this motivation. Our robots, a fleet of customised DJI Robomaster S1 vehicles, offer a balance between small robots that do not possess sufficient compute or actuation capabilities and larger robots that are unsuitable for indoor multi-robot tests. They run a modular ROS2-based optimal estimation and control stack for full onboard autonomy, contain ad-hoc peer-to-peer communication infrastructure, and can zero-shot run multi-agent reinforcement learning (MARL) policies trained in our vectorized multi-agent simulation framework. We present an in-depth review of other platforms currently available, showcase new experimental validation of our system's capabilities, and introduce case studies that highlight the versatility and reliabilty of our system as a testbed for a wide range of research demonstrations. Our system as well as supplementary material is available online: https://proroklab.github.io/cambridge-robomaster
Replacements for Mon, 6 May 24
- [16] arXiv:2107.12416 (replaced) [pdf, other]
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Title: Asynchronous Distributed Reinforcement Learning for LQR Control via Zeroth-Order Block Coordinate DescentComments: The arxiv version contains proofs of Lemma 3 and Lemma 5, which are missing in the published versionSubjects: Systems and Control (eess.SY); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Optimization and Control (math.OC)
- [17] arXiv:2310.11760 (replaced) [pdf, ps, other]
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Title: Performance Investigation of an Optimal Control Strategy for Zero-Emission Operations of Shipboard MicrogridsComments: Submitted to SPEEDAM 2024Subjects: Systems and Control (eess.SY)
- [18] arXiv:2312.01441 (replaced) [pdf, other]
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Title: Koopman-based feedback design with stability guaranteesSubjects: Systems and Control (eess.SY); Optimization and Control (math.OC)
- [19] arXiv:2312.12267 (replaced) [pdf, other]
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Title: Optimal Power Flow Pursuit via Feedback-based Safe Gradient FlowSubjects: Systems and Control (eess.SY); Optimization and Control (math.OC)
- [20] arXiv:2312.13859 (replaced) [pdf, other]
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Title: Nonlinear Functional Estimation: Functional Detectability and Full Information EstimationComments: 15 pages, 3 figuresSubjects: Systems and Control (eess.SY)
- [21] arXiv:2401.10990 (replaced) [pdf, other]
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Title: A Nonlinear Observer Design for the Discrete-time Systems: Exploiting Matrix-Multiplier-based LMI ApproachAuthors: Shivaraj MohiteSubjects: Systems and Control (eess.SY)
- [22] arXiv:2402.04074 (replaced) [pdf, other]
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Title: Mean-Square Stability and Stabilizability for LTI and Stochastic Systems Connected in FeedbackSubjects: Systems and Control (eess.SY)
- [23] arXiv:2402.08289 (replaced) [pdf, ps, other]
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Title: Why Studying Cut-ins? Comparing Cut-ins and Other Lane Changes Based on Naturalistic Driving DataSubjects: Systems and Control (eess.SY); Signal Processing (eess.SP)
- [24] arXiv:2402.18554 (replaced) [pdf, other]
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Title: Extended Kalman filter -- Koopman operator for tractable stochastic optimal controlComments: 6 pagesSubjects: Systems and Control (eess.SY); Signal Processing (eess.SP)
- [25] arXiv:2402.15942 (replaced) [pdf, other]
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Title: Minimum energy density steering of linear systems with Gromov-Wasserstein terminal costComments: 7 pagesSubjects: Optimization and Control (math.OC); Systems and Control (eess.SY)
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