Analysis and synthesis of safety-critical autonomous systems are carried out using models which are often dynamic. Two central features of these dynamic systems are parameters and unmodeled dynamics. Much of feedback control design is parametric in nature and as such, accurate and fast estimation of the parameters in the modeled part of the dynamic system is a crucial property for designing risk-aware autonomous systems. This paper addresses the use of a spectral lines-based approach for estimating parameters of the dynamic model of an autonomous system. Existing literature has treated all unmodeled components of the dynamic system as sub-Gaussian noise and proposed parameter estimation using Gaussian noise-based exogenous signals. In contrast, we allow the unmodeled part to have deterministic unmodeled dynamics, which are almost always present in physical systems, in addition to sub-Gaussian noise. In addition, we propose a deterministic construction of the exogenous signal in order to carry out parameter estimation. We introduce a new tool kit which employs the theory of spectral lines, retains the stochastic setting, and leads to non-asymptotic bounds on the parameter estimation error. Unlike the existing stochastic approach, these bounds are tunable through an optimal choice of the spectrum of the exogenous signal leading to accurate parameter estimation. We also show that this estimation is robust to unmodeled dynamics, a property that is not assured by the existing approach. Finally, we show that under ideal conditions with no deterministic unmodeled dynamics, the proposed approach can ensure a Õ(√t) Regret, matching existing literature. Experiments are provided to support all theoretical derivations, which show that the spectral lines-based approach outperforms the Gaussian noise-based method when unmodeled dynamics are present, in terms of both parameter estimation error and Regret obtained using the parameter estimates with a Linear Quadratic Regulator in feedback.

With the increasing penetration of distributed energy resources (DERs) in grid edge, including renewable generation, flexible loads, and storage, accurate prediction of distributed generation and consumption at the consumer level becomes important. However, DER prediction based on the transmission of customer-level data, either repeatedly or in large amounts, is not feasible due to privacy concerns. In this article, a distributed machine learning approach, federated learning (FL), is proposed to carry out DER forecasting using a network of Internet of Things (IoT) nodes, each of which transmits a model of the consumption and generation patterns without revealing consumer data. We consider a simulation study that includes 1000 DERs and show that our method leads to an accurate prediction of preserving consumer privacy, while still leading to an accurate forecast. We also evaluate grid-specific performance metrics, such as load swings and load curtailment, and show that our FL algorithm leads to satisfactory performance. Simulations are also performed on the Pecan street data set to demonstrate the validity of the proposed approach on real data.

Modeling and design of automotive systems from a cyber-physical system (CPS) perspective have lately attracted extensive attention. As the trend towards automated driving and connectivity accelerates, strong interactions between vehicles and the infrastructure are expected. This requires modeling and control of the traffic network in a similarly formal manner. Modeling of such networks involves a tradeoff between expressivity of the appropriate features and tractability of the control problem. Back-pressure control of traffic signals is gaining ground due to its decentralized implementation, low computational complexity, and no requirements on prior traffic information. It guarantees maximum stability under idealistic assumptions. However, when deployed in real traffic intersections, the existing back-pressure control algorithms may result in poor junction utilization due to (i) fixed-length control phases; (ii) stability as the only objective; and (iii) obliviousness to finite road capacities and empty roads. In this paper, we propose a CPS-oriented model of traffic intersections and control of traffic signals, aiming to address the utilization issue of the back-pressure algorithms. We consider a more realistic model with transition phases and dedicated turning lanes, the latter influencing computation of the pressure and subsequently the utilization. The main technical contribution is an adaptive controller that enables varying-length control phases and considers both stability and utilization, while taking both cases of full roads and empty roads into account. We implement a mechanism to prevent frequent changes of control phases and thus limit the number of transition phases, which have negative impact on the junction utilization. Microscopic simulation results with SUMO on a 3×3 traffic network under various traffic patterns show that the proposed algorithm is at least about 13% better in performance than the existing fixed-length backpressure control algorithms reported in previous works. This is a significant improvement in the context of traffic signal control.

Travel time in urban centers is a significant contributor to the quality of living of its citizens. Mobility on Demand (MoD) services such as Uber and Lyft have revolutionized the transportation infrastructure, enabling new solutions for passengers. Shared MoD services have shown that a continuum of solutions can be provided between the traditional private transport for an individual and the public mass transit-based transport, by making use of the underlying cyber-physical substrate that provides advanced, distributed, and networked computational and communicational support. In this article, we propose a novel shared mobility service using a dynamic framework. This framework generates a dynamic route for multi-passenger transport, optimized to reduce time costs for both the shuttle and the passengers and is designed using a new concept of a space window. This concept introduces a degree of freedom that helps reduce the cost of the system involved in designing the optimal route. A specific algorithm based on the Alternating Minimization approach is proposed. Its analytical properties are characterized. Detailed computational experiments are carried out to demonstrate the advantages of the proposed approach and are shown to result in an order of magnitude improvement in the computational efficiency with minimal optimality gap when compared to a standard Mixed Integer Quadratically Constrained Programming-based algorithm.

Automotive embedded systems are safety-critical, while being highly cost-sensitive at the same time. The former requires resource dimensioning that accounts for the worst case, even if such a case occurs infrequently, while this is in conflict with the latter requirement. In order to manage both of these aspects at the same time, one research direction being explored is to dynamically assign a mixture of resources based on needs and priorities of different tasks. Along this direction, in this paper we show that by properly modeling the physical dynamics of the systems that an automotive control software interacts with, it is possible to better save resources while still guaranteeing safety properties. Towards this, we focus on a distributed controller implementation that uses an automotive FlexRay bus. Our approach combines techniques from timing/schedulability analysis and control theory and shows the significance of synergistically combining the cyber component and physical processes in the cyber-physical systems (CPS) design paradigm.

One of the important goals of a smart city is to increase the quality of urban mobility. This paper will explore the use of dynamic tariffs for this purpose. Transactive control, the concept of feedback through economic transactions, is a promising concept for accomplishing such dynamic tariffs, and is the focus of this paper. Two specific examples of transactive control are considered in this paper, the first of which is the synthesis of dynamic toll prices with the goal of reducing traffic congestion in highways. We examine how a model-based approach can result in optimal toll pricing schemes. Sociotechnical models that combine behavioral models of drivers and traffic flow models, together with real-time traffic information obtained from on-road sensors are used to determine the transactive control strategies. The overall pricing strategy is evaluated using real traffic data from an existing dynamic toll-pricing framework. The second example of transactive control is in the context of Mobility on Demand (MoD), where new modes of transportation other than private and public are being proposed, providing a smorgasbord of options for passengers. We investigate a dynamic routing concept for multipassenger transport, and propose a transactive control strategy to regulate the achievable performance around desired values. Numerical simulations using actual passenger data are carried out to demonstrate the advantages of the proposed concept.

Control is and will continue to be a key discipline for realizing the objectives of smart grid initiatives. Research in control science and engineering is not limited to one or a few application concepts but is pervasive across the smart grid ecosystem. The principal contribution of this paper is to review, from a system-architectural perspective, how control enables smart grid applications. Application “templates” are presented for direct load control, automated demand response, microgrid optimization, control for distribution grids, wide-area control, and market-centric control. Technological developments, including in power electronics, that are enabling smart grid control research and applications are also itemized and two cross-cutting needs/opportunities for future research discussed. We conclude with a summary of a recent status report on the progress that has been made in the United States, noting also the challenges to further progress, in renewable generation, energy efficiency, and carbon reduction.

We deal with synthesis of distributed embedded control systems closed over a faulty or severely constrained communication network. Such overloaded communication networks are common in cost-sensitive domains such as automotive. Design of such systems aims to meet all deadlines following the traditional notion of schedulability. In this work, we aim to exploit robustness of the controller and propose a novel implementation approach to achieve a tighter design. Toward this, we answer two research questions: (i) given a distributed architecture, how to characterize and formally verify the bound on deadline misses, (ii) given such a bound, how to design a controller such that desired stability and Quality of Control (QoC) requirements are met. We address question (i) by modeling a distributed embedded architecture as a network of Event Count Automata (ECA), and subsequently introducing and formally verifying a property formulation with reduced complexity. We address question (ii) by introducing a novel fault-tolerant control strategy which adjusts the control input at runtime based on the occurrence of fault or drop. We show that QoC under faulty communication improves significantly using the proposed fault-tolerant strategy.

We propose a performance verification technique for cyber-physical systems that consist of multiple control loops implemented on a distributed architecture. The architectures we consider are fairly generic and arise in domains such as automotive and industrial automation; they are multiple processors or electronic control units (ECUs) communicating over buses like FlexRay and CAN. Current practice involves analyzing the architecture to estimate worst-case end-to-end message delays and using these delays to design the control applications. This involves a significant amount of pessimism since the worst-case delays often occur very rarely. We show how to combine functional analysis techniques with model checking in order to derive a delay-frequency interface that quantifies the interleavings between messages with worst-case delays and those with smaller delays. In other words, we bound the frequency with which control messages might suffer the worst-case delay. We show that such a delay-frequency interface enables us to verify much tigher control performance properties compared to what would be possible with only worst-case delay bounds.

Many cyber-physical systems consist of a collection of control loops implemented on multiple electronic control units (ECUs) communicating via buses such as FlexRay. Such buses support hybrid communication protocols consisting of a mix of time- and event-triggered slots. The time-triggered slots may be perfectly synchronized to the ECUs and hence result in zero communication delay, while the event-triggered slots are arbitrated using a priority-based policy and hence messages mapped onto them can suffer non-negligible delays. In this paper, we study a switching scheme where control messages are dynamically scheduled between the time-triggered and the event-triggered slots. This allows more efficient use of time-triggered slots which are often scarce and therefore should be used sparingly. Our focus is to perform a schedulability analysis for this setup, i.e., in the event of an external disturbance, can a message be switched from an event-triggered to a time-triggered slot within a specified deadline? We show that this analysis can check whether desired control performance objectives may be satisfied, with a limited number of time-triggered slots being used.

A novel use of neural networks for parameter estimation in nonlinear systems is proposed. The approximating ability of the neural network is used to identify the relation between system variables and parameters of a dynamic system. Two different algorithms, a block estimation method and a recursive estimation method, are proposed. The block estimation method consists of the training of a neural network to approximate the mapping between the system response and the system parameters which in turn is used to identify the parameters of the nonlinear system. In the second method, the neural network is used to determine a recursive algorithm to update the parameter estimate. Both methods are useful for parameter estimation in systems where either the structure of the nonlinearities present are unknown or when the parameters occur nonlinearly. Analytical conditions under which successful estimation can be carried but and several illustrative examples verifying the behavior of the algorithms through simulations are presented.