| 1. |
- Baumann, Dominik, Ph.D. 1991-, et al.
(författare)
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A computationally lightweight safe learning algorithm
- 2023
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Ingår i: 2023 62nd IEEE Conference on Decision and Control, (CDC). - : Institute of Electrical and Electronics Engineers (IEEE). - 9798350301243 - 9798350301250 ; , s. 1022-1027
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Konferensbidrag (refereegranskat)abstract
- Safety is an essential asset when learning control policies for physical systems, as violating safety constraints during training can lead to expensive hardware damage. In response to this need, the field of safe learning has emerged with algorithms that can provide probabilistic safety guarantees without knowledge of the underlying system dynamics. Those algorithms often rely on Gaussian process inference. Unfortunately, Gaussian process inference scales cubically with the number of data points, limiting applicability to high-dimensional and embedded systems. In this paper, we propose a safe learning algorithm that provides probabilistic safety guarantees but leverages the Nadaraya-Watson estimator instead of Gaussian processes. For the Nadaraya-Watson estimator, we can reach logarithmic scaling with the number of data points. We provide theoretical guarantees for the estimates, embed them into a safe learning algorithm, and show numerical experiments on a simulated seven-degrees-of-freedom robot manipulator.
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| 2. |
- Li, Zishuo, et al.
(författare)
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Secure State Estimation with Asynchronous Measurements against Malicious Measurement-Data and Time-Stamp Manipulation
- 2023
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Ingår i: 2023 62nd IEEE Conference on Decision and Control, CDC 2023. - : Institute of Electrical and Electronics Engineers (IEEE). - 9798350301243 - 9798350301250 ; , s. 7073-7080
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Konferensbidrag (refereegranskat)abstract
- This paper proposes a secure state estimation scheme with asynchronous non-periodic measurements for con-tinuous LTI systems under false data attacks on measurement transmission channels. Each sensor transmits the measurement information in a triple comprised of its sensor index, the time-stamp, and the measurement value to the fusion center via unprotected communication channels. A malicious attacker can corrupt a subset of sensors by (i) manipulating the time-stamp and the measurement value, (ii) blocking transmitted measurement triples, or (iii) injecting fake measurement triples. To deal with such attacks, we propose a secure state estimator by designing decentralized local estimators and fusing all the local states by the median operator. The local estimators receive the sampled measurements and update their local state in an asynchronous manner, while the fusion center triggers the fusion and generates a secure estimation in the presence of a local update. We prove that local estimators of benign sensors are unbiased with stable error covariance. Moreover, the fused secure estimation error has bounded expectation and covariance against at most p corrupted sensors as long as the system is 2p-sparse observable. The efficacy of the proposed scheme is demonstrated through a benchmark example of the IEEE 14-bus system.
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| 3. |
- Ramos, Guilherme, et al.
(författare)
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On the trade-offs between accuracy, privacy, and resilience in average consensus algorithms
- 2023
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Ingår i: 2023 62nd IEEE Conference on Decision and Control, (CDC). - : Institute of Electrical and Electronics Engineers (IEEE). - 9798350301243 - 9798350301250 ; , s. 8026-8031
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Konferensbidrag (refereegranskat)abstract
- There can be none. In this paper, we address the problem of a set of discrete-time networked agents reaching average consensus privately and resiliently in the presence of a subset of attacked agents. Existing approaches to the problem rely on trade-offs between accuracy, privacy, and resilience, sacrificing one for the others. We show that a separation-like principle for privacy-preserving and resilient discrete-time average consensus is possible. Specifically, we propose a scheme that combines strategies from resilient average consensus and private average consensus, which yields both desired properties. The proposed scheme has polynomial time-complexity on the number of agents and the maximum number of attacked agents. In other words, each agent that is not under attack is able to detect and discard the values of the attacked agents, reaching the average consensus of non-attacked agents while keeping each agent's initial state private. Finally, we demonstrate the effectiveness of the proposed method with numerical results.
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| 4. |
- Razaq, Muhammad Ahsan, et al.
(författare)
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Propagation of Stubborn Opinions on Signed Graphs
- 2023
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Ingår i: 2023 62ND IEEE CONFERENCE ON DECISION AND CONTROL, CDC. - : IEEE. - 9798350301243 - 9798350301250 ; , s. 491-496
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Konferensbidrag (refereegranskat)abstract
- This paper addresses the problem of propagation of opinions in a Signed Friedkin-Johnsen (SFJ) model, i.e., an opinion dynamics model in which the agents are stubborn and the interaction graph is signed. We provide sufficient conditions for the stability of the SFJ model and for convergence to consensus of a concatenation of such SFJ models.
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| 5. |
- Shoja, Shamisa, et al.
(författare)
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Exact Complexity Certification of Start Heuristics in Branch-and-Bound Methods for Mixed-Integer Linear Programming
- 2023
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Ingår i: 2023 62ND IEEE CONFERENCE ON DECISION AND CONTROL, CDC. - : IEEE. - 9798350301243 - 9798350301250 ; , s. 2292-2299
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Konferensbidrag (refereegranskat)abstract
- Model predictive control (MPC) with linear performance measure for hybrid systems requires the solution of a mixed-integer linear program (MILP) at each time instance. A well-known method to solve MILP problems is branch-and-bound (B&B). To enhance the performance of B&B, start heuristic methods are often used, where they have shown to be useful supplementary tools to find good feasible solutions early in the B&B search tree, hence, reducing the overall effort in B&B to find optimal solutions. In this work, we extend the recently-presented complexity certification framework for B&B-based MILP solvers to also certify computational complexity of the start heuristics that are integrated into B&B. Therefore, the exact worst-case computational complexity of the three considered start heuristics and, consequently, the B&B method when applying each one can be determined offline, which is of significant importance for real-time applications of hybrid MPC. The proposed algorithms are validated by comparing against the corresponding online heuristic-based MILP solvers in numerical experiments.
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| 6. |
- Tosun, Fatih Emre, et al.
(författare)
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Robust Sequential Detection of Non-stealthy Sensor Deception Attacks in an Artificial Pancreas System
- 2023
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Ingår i: 2023 62nd IEEE Conference on Decision and Control (CDC). - : Institute of Electrical and Electronics Engineers (IEEE). - 9798350301243 - 9798350301250 ; , s. 2827-2832
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Konferensbidrag (refereegranskat)abstract
- This paper considers deterministic sensor deception attacks in closed-loop insulin delivery. Since the quality of decision-making in control systems heavily relies on accurate sensor measurements, timely detection of attacks is imperative. To this end, we consider a model-based anomaly detection scheme based on Kalman filtering and sequential change detection. In particular, we derive the minimax robust CUSUM and Shewhart tests that minimizes the worst-case mean detection delay and maximizes the instant detection rate, respectively. As a byproduct of our analysis, we show that the notorious.2 test shares an interesting optimality property with the twosided Shewhart test. Finally, we show that one-sided sequential detectors can significantly improve sensor anomaly detection for preventing overnight hypoglycemia which can be fatal.
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| 7. |
- Wigren, Torbjörn, et al.
(författare)
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Feedback Path Delay Attacks and Detection
- 2023
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Ingår i: Proceedings of the 62nd IEEE Conference on Decision and Control (CDC). - Singapore : Institute of Electrical and Electronics Engineers (IEEE). - 9798350301243 - 9798350301250 ; , s. 3864-3871
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Konferensbidrag (refereegranskat)abstract
- The paper discusses delay injection attacks on regulator loops and suggests joint recursive prediction error identification of delay and dynamics for supervision and attack detection. The control system is assumed to be operated either in open- or closed-loop mode. It is shown why delay insertion in the feedback path before the user switches to closed-loop operation is advantageous to disguise the attack. The detection performance is evaluated numerically for a linearized automotive cruise control feedback loop.
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| 8. |
- Bencherki, Fethi, et al.
(författare)
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Robust Simultaneous Stabilization Via Minimax Adaptive Control
- 2023
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Ingår i: Proceedings of the IEEE Conference on Decision and Control. - 9798350301243 ; , s. 2503-2508
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Konferensbidrag (refereegranskat)abstract
- The paper explores the usage of minimax adaptive controllers to guarantee finite L2 -gain simultaneous stabilization of linear time-invariant (LTI) plants. It is shown that a minimax adaptive controller simultaneously stabilizes any two multiple-input multiple-output (MIMO) P-stabilizable LTI plants when no LTI controller can achieve that, and the worst attained L2 -gain bound for the transient dynamics is readily computable.
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| 9. |
- Cianfanelli, Leonardo, et al.
(författare)
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Information Design in Bayesian Routing Games
- 2023
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Ingår i: Proceedings of the IEEE Conference on Decision and Control. - 9798350301243 ; , s. 3945-3949
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Konferensbidrag (refereegranskat)abstract
- We study optimal information provision in transportation networks when users are strategic and the network state is uncertain. An omniscient planner observes the network state and discloses information to the users with the goal of minimizing the expected travel time at the user equilibrium. Public signal policies, including full-information disclosure, are known to be inefficient in achieving optimality. For this reason, we focus on private signals and restrict without loss of generality the analysis to signals that coincide with path recommendations that satisfy obedience constraints, namely users have no incentive in deviating from the received recommendation according to their posterior belief. We first formulate the general problem and analyze its properties for arbitrary network topologies and delay functions. Then, we consider the case of two parallel links with affine delay functions, and provide sufficient conditions under which optimality can be achieved by information design. Interestingly, we observe that the system benefits from uncer-tainty, namely it is easier for the planner to achieve optimality when the variance of the uncertain parameters is large. We then provide an example where optimality can be achieved even if the sufficient conditions for optimality are not met.
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| 10. |
- Dahlin, Albin, 1992, et al.
(författare)
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Obstacle Avoidance in Dynamic Environments via Tunnel-following MPC with Adaptive Guiding Vector Fields
- 2023
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Ingår i: Proceedings of the IEEE Conference on Decision and Control. - 9798350301243 ; 2023, s. 5784-5789
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Konferensbidrag (refereegranskat)abstract
- This paper proposes a motion control scheme for robots operating in a dynamic environment with concave obstacles. A Model Predictive Controller (MPC) is constructed to drive the robot towards a goal position while ensuring collision avoidance without direct use of obstacle information in the optimization problem. This is achieved by guaranteeing tracking performance of an appropriately designed receding horizon path. The path is computed using a guiding vector field defined in a subspace of the free workspace where each point in the subspace satisfies a criteria for minimum distance to all obstacles. The effectiveness of the control scheme is illustrated by means of simulation.
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| 11. |
- Grönqvist, Johan, et al.
(författare)
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Verification of Low-Dimensional Neural Network Control
- 2023
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Ingår i: Proceedings of the IEEE Conference on Decision and Control. - 9798350301243 ; , s. 4566-4571
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Konferensbidrag (refereegranskat)abstract
- We verify safety of a nonlinear continuous-time system controlled by a neural network controller. The system is decomposed into low-dimensional subsystems connected in a feedback loop. Our application is a rocket landing, and open-loop properties of the two-dimensional altitude subsystem are verified using worst-case simulations. Closed-loop safety properties (crash-avoidance) of the full system are obtained from composition of contracts for open-loop safety properties of subsystems in a fashion analogous to the small-gain theorem.
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| 12. |
- Hansson, Jonas, et al.
(författare)
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A closed-loop design for scalable high-order consensus
- 2023
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Ingår i: Proceedings of the IEEE Conference on Decision and Control. - 9798350301243 ; , s. 7388-7394
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Konferensbidrag (refereegranskat)abstract
- This paper studies the problem of coordinating a group of nth-order integrator systems. As for the well-studied conventional consensus problem, we consider linear and distributed control with only local and relative measurements. We propose a closed-loop dynamic that we call serial consensus and prove it achieves nth order consensus regardless of model order and underlying network graph. This alleviates an important scalability limitation in conventional consensus dynamics of order n≥2, whereby they may lose stability if the underlying network grows. The distributed control law which achieves the desired closed loop dynamics is shown to be localized and obey the limitation to relative state measurements. Furthermore, through use of the small-gain theorem, the serial consensus system is shown to be robust to both model and feedback uncertainties. We illustrate the theoretical results through examples.
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| 13. |
- Hellander, Anja, 1996-, et al.
(författare)
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On Integrated Optimal Task and Motion Planning for a Tractor-Trailer Rearrangement Problem
- 2024
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Ingår i: 2023 62nd IEEE Conference on Decision and Control (CDC). - : IEEE. - 9798350301243 ; , s. 6116-6123
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Konferensbidrag (refereegranskat)abstract
- In this work, a combined task and motion planner for a tractor and a set of trailers is proposed and it is shown that it is resolution complete and resolution optimal. The proposed planner consists of a task planner and a motion planner that are both based on heuristically guided graph-search. As a step towards tighter integration of task and motion planning, we use the same heuristic that is used by the motion planner in the task planner as well. We further propose to use the motion planner heuristic to give an initial underestimate of the motion costs that are used as costs during the task planning search, and increase this estimate gradually by using the motion planner to verify the cost and feasibility of actions along paths of interest. To limit the time spent in the motion planner, the use of time and cost limits to pause or prematurely abort the motion planner is proposed, which does not affect the resolution completeness or resolution optimality. The planner is evaluated on numerical examples and the results show that the proposed planner can significantly reduce the execution time compared to a baseline resolution optimal task and motion planner.
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| 14. |
- Miloradović, Branko, 1987-, et al.
(författare)
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Multi-Criteria Optimization of Application Offloading in the Edge-to-Cloud Continuum
- 2023
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Ingår i: Proc IEEE Conf Decis Control. - : Institute of Electrical and Electronics Engineers Inc.. - 9798350301243 ; , s. 4917-4923
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Konferensbidrag (refereegranskat)abstract
- Applications are becoming increasingly data-intensive, requiring significant computational resources to meet their demand. Cloud-based services are insufficient to meet such demand, leading to a shift of the computation towards the devices closer to the edge of the network, leading to the emergence of an Edge-to-Cloud computing Continuum (E2C). An application can offload part of its computation toward the E2C. The allocation of applications to a set of available computing nodes is a challenging problem, as the allocation needs to take into account several factors, including the application requirements and demands as well as the optimization of the resource utilization in the E2C infrastructure and the minimization the CO2 footprint of the executed applications. Control and optimization techniques provide a vast array of tools for optimizing the Edge-to-Cloud continuum's management. This paper provides a mathematical formulation for the application offloading with specific requirements in the cloud computing domain. The problem is modeled as integer linear programming and constraint programming models and implemented in commercially available software. Finally, we provide the results of performed comparison between the two models.
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| 15. |
- Restrepo, Esteban, et al.
(författare)
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Simultaneous Synchronization and Topology Identification of Complex Dynamical Networks
- 2023
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Ingår i: 2023 62nd IEEE Conference on Decision and Control, CDC 2023, Proceedings. - : Institute of Electrical and Electronics Engineers (IEEE). - 9798350301243 ; , s. 393-398
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Konferensbidrag (refereegranskat)abstract
- We propose a new method for simultaneous synchronization and topology identification of a complex dynamical network that relies on the edge-agreement framework and on adaptive-control approaches by design of an auxiliary network. Our method guarantees the identification of the unknown topology and it guarantees that once the topology is identified the complex network achieves synchronization. Under our identification algorithm we are able to provide stability results for the estimation errors in the form of uniform semiglobal practical asymptotic stability. Finally, we demonstrate the effectiveness of our approach with an illustrating example.
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| 16. |
- Russo, Alessio, et al.
(författare)
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Conformal Off-Policy Evaluation in Markov Decision Processes
- 2023
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Ingår i: 2023 62ND IEEE CONFERENCE ON DECISION AND CONTROL, CDC. - : IEEE. - 9798350301243 ; , s. 3087-3094
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Konferensbidrag (refereegranskat)abstract
- Reinforcement Learning aims at identifying and evaluating efficient control policies from data. In many real-world applications, the learner is not allowed to experiment and cannot gather data in an online manner (this is the case when experimenting is expensive, risky or unethical). For such applications, the reward of a given policy (the target policy) must be estimated using historical data gathered under a different policy (the behavior policy). Most methods for this learning task, referred to as Off-Policy Evaluation (OPE), do not come with accuracy and certainty guarantees. We present a novel OPE method based on Conformal Prediction that outputs an interval containing the true reward of the target policy with a prescribed level of certainty. The main challenge in OPE stems from the distribution shift due to the discrepancies between the target and the behavior policies. We propose and empirically evaluate different ways to deal with this shift. Some of these methods yield conformalized intervals with reduced length compared to existing approaches, while maintaining the same certainty level.
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| 17. |
- Wu, Xuyang, et al.
(författare)
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Delay-agnostic Asynchronous Distributed Optimization
- 2023
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Ingår i: 2023 62Nd Ieee Conference On Decision And Control, Cdc. - : Institute of Electrical and Electronics Engineers (IEEE). - 9798350301243 ; , s. 1082-1087
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Konferensbidrag (refereegranskat)abstract
- Existing asynchronous distributed optimization algorithms often use diminishing step-sizes that cause slow practical convergence, or fixed step-sizes that depend on an assumed upper bound of delays. Not only is such a delay bound hard to obtain in advance, but it is also large and therefore results in unnecessarily slow convergence. This paper develops asynchronous versions of two distributed algorithms, DGD and DGD-ATC, for solving consensus optimization problems over undirected networks. In contrast to alternatives, our algorithms can converge to the fixed point set of their synchronous counterparts using step-sizes that are independent of the delays. We establish convergence guarantees under both partial and total asynchrony. The practical performance of our algorithms is demonstrated by numerical experiments.
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