arXiv subjects

Anuradha M. Annaswamy

Publications and source records attributed to Anuradha M. Annaswamy.

At least 19 recordsLinked to original sources

arXiv2026

Feasibility of Simultaneous Input-Output Constraints for Tracking in a Class of LTI Systems: Part I

This paper addresses simultaneous input and output constraint satisfaction for a class of multi-input linear time-invariant systems with state feedback and integral action. A control barrier function (CBF)-based governor modifies the reference command while preserving the nominal feedback controller. Necessary and sufficient conditions are derived under which the governor is feasible at every state of a prescribed operating set and ensures simultaneous input and output constraint satisfaction, forward invariance, and bounded closed-loop solutions. The paper also shows that these conditions need not be met for certain choices of the CBF governor parameters due to conflicts between input and output constraints. A systematic design procedure is proposed that first searches for feasible free parameters and, if necessary, proposes a relaxation in the input constraint that restores feasibility. A companion paper provides numerical examples and counterexamples illustrating these results.

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arXiv2026

Feasibility of Simultaneous Input-Output Constraints for Tracking in a Class of LTI Systems: Part II

This paper illustrates the properties of a new CBF-governor that enables simultaneous input and output constraint satisfaction for a class of multi-input linear time-invariant systems with state feedback and integral action. This governor is designed so as to modify the reference command while preserving the nominal feedback controller. Necessary and sufficient conditions are derived in a companion paper under which the governor is shown to be feasible at every state of a prescribed operating set and ensures simultaneous input and output constraint satisfaction, forward invariance, and bounded closed-loop solutions. These theoretical results are illustrated in this paper through several numerical examples. In each of these examples, we show how the goals of the CBF-governor are met and corroborate the corresponding necessary and sufficient condition. The computational burden associated with the proposed CBF-governor is also articulated, with its online component comparable to either that of a QP solver or determined using an exact closed-form solution. The offline component is related to the checking of the SIOCF condition which is of $O(N)$.

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arXiv2026

A Control Architecture for Fast Frequency Regulation with Increasing Penetration of Inverter Based Resources (Extended Version)

This paper addresses frequency regulation under operational constraints in interconnected power systems with high penetration of inverter-based renewable generation. A two-layer control architecture is proposed that combines optimized droop and Virtual Synchronous Machine (VSM) primary control with a Model Predictive Control (MPC) secondary layer operating at realistic control-room update rates. Unlike recently proposed approaches, the proposed framework integrates MPC within existing grid control structures, enabling constraint-aware coordination. A reduced-order frequency response model is systematically derived from a detailed grid model using Hankel singular values, and a reduced-order Kalman-Bucy observer enables state and disturbance estimation using only measurable outputs. Evaluation using representative data from the Kingdom of Saudi Arabia demonstrates effective frequency regulation under realistic operating conditions.

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arXiv2026

Adapt and Stabilize, Then Learn and Optimize: A New Approach to Adaptive LQR

This paper focuses on adaptive control of the discrete-time linear quadratic regulator (adaptive LQR). Recent literature has made significant contributions in proving non-asymptotic convergence rates, but existing approaches have a few drawbacks that pose barriers for practical implementation. These drawbacks include (i) a requirement of an initial stabilizing controller, (ii) a reliance on exploration for closed-loop stability, and/or (iii) computationally intensive algorithms. This paper proposes a new algorithm that overcomes these drawbacks for a particular class of discrete-time systems. This algorithm leverages direct model-reference adaptive control (direct MRAC) and combines it with an epoch-based approach in order to address the drawbacks (i)-(iii) with a provable high-probability regret bound comparable to existing literature. Simulations demonstrate that the proposed approach yields regrets that are comparable to those from existing methods when the conditions (i) and (ii) are met, and yields regrets that are significantly smaller when either of these two conditions is not met.

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arXiv2026

Dynamic resource coordination can increase grid hosting capacity to support more renewables, storage, and electrified load growth

We show that dynamic coordination of distributed energy resources (DERs) can increase the capacity of low- and medium-voltage grids, improve reliability and power quality, and reduce solar curtailment. We develop three approaches to compute hosting capacity on a representative distribution grid with realistic scenarios. A deterministic iterative method provides insight into how dynamic operation and DER interactions enhance capacity and affect power flows, demonstrating clear gains over static methods even with low-to-moderate levels of storage and flexible demand. A stochastic programming approach jointly optimizes DER siting and sizing, showing that nodal colocation and complementary effects expand the feasible region of solar, heat pump, and battery penetrations by over 22X. This enables up to 200% solar, 100% battery, and 90% heat pump penetration. Batteries emerge as the most critical technology, followed by heat pumps and electric vehicles. A Monte Carlo-based extension shows that uncertainty significantly impacts hosting capacity and grid metrics, with 46% higher volatility under dynamic operation.

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arXiv2026

An Error-Based Safety Buffer for Safe Adaptive Control (Extended Version)

We consider the problem of adaptive control of a class of feedback linearizable plants with matched parametric uncertainties whose states are accessible, subject to state constraints, which often arise due to safety considerations. In this paper, we combine adaptation and control barrier functions into a real-time control architecture that guarantees stability, ensures control performance, and remains safe even with the parametric uncertainties. Two problems are considered, differing in the nature of the parametric uncertainties. In both cases, the control barrier function is assumed to have an arbitrary relative degree. In addition to guaranteeing stability, it is proved that both the control objective and safety objective are met with near-zero conservatism. No excitation conditions are imposed on the command signal. Simulation results demonstrate the non-conservatism of all of the theoretical developments.

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arXiv2025

Adaptive Control of Dubins Vehicle in the Presence of Loss of Effectiveness (Extended Version)

The control of a Dubins Vehicle when subjected to a loss of control effectiveness in the turning rate is considered. A complex state-space representation is used to model the vehicle dynamics. An adaptive control design is proposed, with the underlying stability analysis guaranteeing closed-loop boundedness and tracking of a desired path. It is shown that a path constructed by waypoints and a minimum turn radius can be specified using a reference model which can be followed by the closed loop system. The control design utilizes the complex state-space representation as well as a PID controller for the nominal closed-loop. How the design can be modified to ensure path following even in the presence input constraints is also discussed. Simulation studies are carried out to complement the theoretical derivations.

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arXiv2025

Resilience of the Electric Grid through Trustable IoT-Coordinated Assets (Extended version)

The electricity grid has evolved from a physical system to a cyber-physical system with digital devices that perform measurement, control, communication, computation, and actuation. The increased penetration of distributed energy resources (DERs) including renewable generation, flexible loads, and storage provides extraordinary opportunities for improvements in efficiency and sustainability. However, they can introduce new vulnerabilities in the form of cyberattacks, which can cause significant challenges in ensuring grid resilience. We propose a framework in this paper for achieving grid resilience through suitably coordinated assets including a network of Internet of Things (IoT) devices. A local electricity market is proposed to identify trustable assets and carry out this coordination. Situational Awareness (SA) of locally available DERs with the ability to inject power or reduce consumption is enabled by the market, together with a monitoring procedure for their trustability and commitment. With this SA, we show that a variety of cyberattacks can be mitigated using local trustable resources without stressing the bulk grid. Multiple demonstrations are carried out using a high-fidelity co-simulation platform, real-time hardware-in-the-loop validation, and a utility-friendly simulator.

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arXiv2024

Analytical Construction of CBF-Based Safety Filters for Simultaneous State and Input Constraints (Extended Version)

We revisit the problem explored in [1] of guaranteeing satisfaction of multiple simultaneous state constraints applied to a single-input, single-output plant consisting of a chain of n integrators subject to input limitations. For this problem setting, we derive an analytic, easy-to-implement safety filter which respects input limitations and ensures forward-invariance of all state constraints simultaneously. Additionally, we provide a straightforward extension to the multi-input, multi-output chained integrator setting, and provide an analytic safety filter guaranteeing satisfaction of arbitrarily many simultaneous hyperplane constraints on the output vector. Whereas the approach in [1] obtains maximal invariant sets, our approach trades off some degree of conservatism in exchange for a recursive safety filter which is analytic for any arbitrary n >= 1.

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arXiv2023

Safe and Stable Adaptive Control for a Class of Dynamic Systems

Adaptive control has focused on online control of dynamic systems in the presence of parametric uncertainties, with solutions guaranteeing stability and control performance. Safety, a related property to stability, is becoming increasingly important as the footprint of autonomous systems grows in society. One of the popular ways for ensuring safety is through the notion of a control barrier function (CBF). In this paper, we combine adaptation and CBFs to develop a real-time controller that guarantees stability and remains safe in the presence of parametric uncertainties. The class of dynamic systems that we focus on is linear time-invariant systems whose states are accessible and where the inputs are subject to a magnitude limit. Conditions of stability, state convergence to a desired value, and parameter learning are all elucidated. One of the elements of the proposed adaptive controller that ensures stability and safety is the use of a CBF-based safety filter that suitably generates safe reference commands, employs error-based relaxation (EBR) of Nagumo's theorem, and leads to guarantees of set invariance. To demonstrate the effectiveness of our approach, we present two numerical examples, an obstacle avoidance case and a missile flight control case.

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arXiv2023

Online Algorithms and Policies Using Adaptive and Machine Learning Approaches

This paper considers the problem of real-time control and learning in dynamic systems subjected to parametric uncertainties. We propose a combination of a Reinforcement Learning (RL) based policy in the outer loop suitably chosen to ensure stability and optimality for the nominal dynamics, together with Adaptive Control (AC) in the inner loop so that in real-time AC contracts the closed-loop dynamics towards a stable trajectory traced out by RL. Two classes of nonlinear dynamic systems are considered, both of which are control-affine. The first class of dynamic systems utilizes equilibrium points %with expansion forms around these points and a Lyapunov approach while second class of nonlinear systems uses contraction theory. AC-RL controllers are proposed for both classes of systems and shown to lead to online policies that guarantee stability using a high-order tuner and accommodate parametric uncertainties and magnitude limits on the input. In addition to establishing a stability guarantee with real-time control, the AC-RL controller is also shown to lead to parameter learning with persistent excitation for the first class of systems. Numerical validations of all algorithms are carried out using a quadrotor landing task on a moving platform.

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arXiv2023

Grid-SiPhyR: An end-to-end learning to optimize framework for combinatorial problems in power systems

Mixed integer problems are ubiquitous in decision making, from discrete device settings and design parameters, unit production, and on/off or yes/no decision in switches, routing, and social networks. Despite their prevalence, classical optimization approaches for combinatorial optimization remain prohibitively slow for fast and accurate decision making in dynamic and safety-critical environments with hard constraints. To address this gap, we propose SiPhyR (pronounced: cipher), a physics-informed machine learning framework for end-to-end learning to optimize for combinatorial problems. SiPhyR employs a novel physics-informed rounding approach to tackle the challenge of combinatorial optimization within a differentiable framework that has certified satisfiability of safety-critical constraints. We demonstrate the effectiveness of SiPhyR on an emerging paradigm for clean energy systems: dynamic reconfiguration, where the topology of the electric grid and power flow are optimized so as to maintain a safe and reliable power grid in the presence of intermittent renewable generation. Offline training of the unsupervised framework on representative load and generation data makes dynamic decision making via the online application of Grid-SiPhyR computationally feasible.

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arXiv2023

Accelerated Algorithms for a Class of Optimization Problems with Equality and Box Constraints

Convex optimization with equality and inequality constraints is a ubiquitous problem in several optimization and control problems in large-scale systems. Recently there has been a lot of interest in establishing accelerated convergence of the loss function. A class of high-order tuners was recently proposed in an effort to lead to accelerated convergence for the case when no constraints are present. In this paper, we propose a new high-order tuner that can accommodate the presence of equality constraints. In order to accommodate the underlying box constraints, time-varying gains are introduced in the high-order tuner which leverage convexity and ensure anytime feasibility of the constraints. Numerical examples are provided to support the theoretical derivations.

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arXiv2023

Discrete-Time Adaptive Control of a Class of Nonlinear Systems Using High-Order Tuners

This paper concerns the adaptive control of a class of discrete-time nonlinear systems with all states accessible. Recently, a high-order tuner algorithm was developed for the minimization of convex loss functions with time-varying regressors in the context of an identification problem. Based on Nesterov's algorithm, the high-order tuner was shown to guarantee bounded parameter estimation when regressors vary with time, and to lead to accelerated convergence of the tracking error when regressors are constant. In this paper, we apply the high-order tuner to the adaptive control of a particular class of discrete-time nonlinear dynamical systems. First, we show that for plants of this class, the underlying dynamical error model can be causally converted to an algebraic error model. Second, we show that using this algebraic error model, the high-order tuner can be applied to provably stabilize the class of dynamical systems around a reference trajectory.

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arXiv2023

Discrete-Time High Order Tuner With A Time-Varying Learning Rate

We propose a new discrete-time online parameter estimation algorithm that combines two different aspects, one that adds momentum, and another that includes a time-varying learning rate. It is well known that recursive least squares based approaches that include a time-varying gain can lead to exponential convergence of parameter errors under persistent excitation, while momentum-based approaches have demonstrated a fast convergence of tracking error towards zero with constant regressors. The question is when combined, will the filter from the momentum method come in the way of exponential convergence. This paper proves that exponential convergence of parameter is still possible with persistent excitation. Simulation results demonstrated competitive properties of the proposed algorithm compared to the recursive least squares algorithm with forgetting.

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arXiv2023

Indirect Adaptive Optimal Control in the Presence of Input Saturation

In this paper, we propose a combined Magnitude Saturated Adaptive Control (MSAC)-Model Predictive Control (MPC) approach to linear quadratic tracking optimal control problems with parametric uncertainties and input saturation. The proposed MSAC-MPC approach first focuses on a stable solution and parameter estimation, and switches to MPC when parameter learning is accomplished. We show that the MSAC, based on a high-order tuner, leads to parameter convergence to true values while providing stability guarantees. We also show that after switching to MPC, the optimality gap is well-defined and proportional to the parameter estimation error. We demonstrate the effectiveness of the proposed MSAC-MPC algorithm through a numerical example based on a linear second-order, two input, unstable system.

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arXiv2023

Neural Network Adaptive Control with Long Short-Term Memory

In this study, we propose a novel adaptive control architecture, which provides dramatically better transient response performance compared to conventional adaptive control methods. What makes this architecture unique is the synergistic employment of a traditional, Adaptive Neural Network (ANN) controller and a Long Short-Term Memory (LSTM) network. LSTM structures, unlike the standard feed-forward neural networks, can take advantage of the dependencies in an input sequence, which can contain critical information that can help predict uncertainty. Through a novel training method we introduced, the LSTM network learns to compensate for the deficiencies of the ANN controller during sudden changes in plant dynamics. This substantially improves the transient response of the system and allows the controller to quickly react to unexpected events. Through careful simulation studies, we demonstrate that this architecture can improve the estimation accuracy on a diverse set of uncertainties for an indefinite time span. We also provide an analysis of the contributions of the ANN controller and LSTM network to the control input, identifying their individual roles in compensating low and high-frequency error dynamics. This analysis provides insight into why and how the LSTM augmentation improves the system's transient response. The stability of the overall system is also shown via a rigorous Lyapunov analysis.

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arXiv2022

A Reactive Power Market for the Future Grid

As pressures to decarbonize the electricity grid increase, the grid edge is witnessing a rapid adoption of distributed and renewable generation. As a result, traditional methods for reactive power management and compensation may become ineffective. Current state of art for reactive power compensation, which rely primarily on capacity payments, exclude distributed generation (DG). We propose an alternative: a reactive power market at the distribution level designed to meet the needs of decentralized and decarbonized grids. The proposed market uses variable payments to compensate DGs equipped with smart inverters, at an increased spatial and temporal granularity, through a distribution-level Locational Marginal Price (d-LMP). We validate our proposed market with a case study of the US New England grid on a modified IEEE-123 bus, while varying DG penetration from 5% to 160%. Results show that our market can accommodate such a large penetration, with stable reactive power revenue streams. The market can leverage the considerable flexibility afforded by inverter-based resources to meet over 40% of reactive power load when operating in a power factor range of 0.6 to 1.0. DGs participating in the market can earn up to 11% of their total revenue from reactive power payments. Finally, the corresponding daily d-LMPs determined from the proposed market were observed to exhibit limited volatility.

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