Veronica Adetola

• United Technologies Research Center

Efficient electrification of space heating/cooling presents the most viable pathway to GHG emissions reduction, and heat pumps (HPs) remain the dominant alternative for replacing gas/oil-based space heating systems. To achieve widespread adoption of HPs, it is imperative to improve their energy efficiency and operational cost. In this paper, a scal...

With the emergence of low-inertia microgrids powered by inverter-based generation, there remains a concern about the operational resilience of these systems. Grid-forming inverters (GFMs), enabled by various device-level (primary) and system-level (secondary) control methods, are poised to play a significant role in achieving certain operational ob...

Our power and energy systems are becoming more and more integrated and interconnected. The increasing integration of edge devices and dependence on cyber infrastructure provides both the potential for benefits and risks. The integration enables more dynamic and flexible control paradigms while at the same time increasing the cyberattack surface and...

The increasing use of remote or mobile access, integrated wearable technologies, data exchange, and cloud-based data analytics in modern smart buildings is steering the building industry towards open communication technologies. The increased connectivity and accessibility could lead to more cyber-attacks in smart buildings. On the other hand, physi...

Offshore wind farms have emerged as a popular renewable energy source that can generate substantial electric power with a low environmental impact. However, integrating these farms into the grid poses significant complexities. To address these issues, optimal-sized energy storage can provide potential solutions and help improve the reliability, eff...

Improving system-level resiliency of networked microgrids against adversarial cyber-attacks is an important aspect in the current regime of increased inverter-based resources (IBRs). To achieve that, this paper contributes in designing a hierarchical control layer, in conjunction with the existing control layers, resilient to adversarial attack sig...

Modern Building Automation Systems (BASs), as the brain that enable the smartness of a smart building, often require increased connectivity both among system components as well as with outside entities, such as the cloud, to enable low-cost remote management, optimized automation via outsourced cloud analytics, and increased building-grid integrati...

Both model predictive control (MPC) and deep reinforcement learning control (DRL) have been presented as a way to approximate the true optimality of a dynamic programming problem, and these two have shown significant operational cost saving potentials for building energy systems. However, there is still a lack of in-depth quantitative studies on th...

Critical infrastructure networks, such as power and transportation networks, are often modeled as Cyber–Physical systems (CPSs). With ever increasing complexity of these systems, there is a need for newer and more relevant metrics and design tools that will co-optimize the physical system components and control policies to guarantee resilience agai...

This paper presents a novel federated reinforcement learning (Fed-RL) methodology to enhance the cyber resiliency of networked microgrids. We formulate a resilient reinforcement learning (RL) training setup which (a) generates episodic trajectories injecting adversarial actions at primary control reference signals of the grid forming (GFM) inverter...

Critical energy infrastructure are constantly under stress due to the ever increasing disruptions caused by wildfires, hurricanes, other weather related extreme events and cyber-attacks. Hence it becomes important to make critical infrastructure resilient to threats from such cyber-physical events. However, such events are hard to predict and numer...

Modern Building Automation Systems (BASs), as the brain that enables the smartness of a smart building, often require increased connectivity both among system components as well as with outside entities, such as optimized automation via outsourced cloud analytics and increased building-grid integrations. However, increased connectivity and accessib...

Occupancy-Centric Controls (OCC) are specialized control sequences which modulate building operation as a function of the sensed occupancy status, thereby leading to increased energy savings, while maintaining thermal comfort. However, occupancy sensor nonidealities, such as bias, latency, random noise, or misdetection, can degrade the performance...

This report documents recent technical work on developing and validating stochastic occupancy models in commercial buildings, performed by the Pacific Northwest National Laboratory (PNNL) as part of the Sensor Impact Evaluation and Verification project under the U.S. Department of Energy (DOE) Building Technologies Office (BTO). In this report, we...

Building operations rely heavily on control systems and sensors. This paper provides a sophisticated literature review on sensor systems in building/HVAC systems, particularly in the context of controls, and their impacts on energy efficiency and thermal comfort. This study aims to understand the previous and current research and identify future re...

We consider the co-design problem of sparse output feedback and row/column-sparse output matrix. A row-sparse (resp. column-sparse) output matrix implies a small number of outputs (resp. sensor measurements). We impose row/column-cardinality constraint on the output matrix and the cardinality constraint on the output feedback gain. The resulting no...

Most physical systems possess parametric uncertainties or unmeasurable parameters and, since parametric uncertainty may degrade the performance of model predictive control (MPC), mechanisms to update the unknown or uncertain parameters are desirable in application. One possibility is to apply adaptive extensions of MPC in which parameter estimation...

The paper1 This is an extension of the paper (Bengea et al., 2014) presented at 3rd International High Performance Buildings Conference at Purdue, July 14–17, 2014, West Lafayette, IN.View all notes presents the development and application of a fault-tolerant control (FTC) technology, its on-line implementation, and results from several tests condu...

The paper presents a new technique for the adaptive parameter estimation in nonlinear parameterized dynamical systems. The technique proposes an uncertainty set-update approach that guarantees forward invariance of the true value of the parameters. In addition, it is shown that in the presence of sufficiently exciting state trajectories, the parame...

In this paper, we propose the design of economic model predictive control (MPC) systems based on a single-step approach of the adaptive MPC technique proposed for a class of uncertain nonlinear systems subject to parametric uncertainties and exogenous variables. The framework considered assumes that the economic function is a known function of cons...

Predictive-control methods have been recently employed for demand-response control of building and district-level HVAC systems. Such approaches rely on models and parameter estimates to meet comfort constraints and to achieve the theoretical system-efficiency gains. In this paper we present a methodology that establishes achievable targets for cont...

The paper presents a new technique for the adaptive parameter estimation in nonlinear parameterized dynamical systems. The technique proposes an uncertainty set-update approach that guarantees forward invariance of the true value of the parameters. In addition, it is shown that in the presence of sufficiently exciting state trajectories, the parame...

In this paper, we consider the problem of adaptive model predictive control subject to exogenous disturbances. Using a novel set-based adaptive estimation, the problem of robust adaptive MPC is proposed and solved for a class of linearly parameterized uncertain nonlinear systems subject to state and input constraints. Two formulations of the adapti...

This technical note demonstrates how the finite-time identification procedure can be used to improve the overall performance of adaptive control systems. First, we develop an adaptive compensator which guarantees exponential convergence of the estimation error provided the integral of a filtered regressor matrix is positive definite. The approach d...

This paper presents three techniques for parameter identification for non-linear, discrete-time systems. The methods presented are intended to improve the performance of adaptive control systems. The first two methods rely on system excitation and a regressor matrix, in either case, the true parameters are identified when the regressor matrix is of...

This paper proposes a controller design approach that integrates RTO and MPC for the control of constrained uncertain nonlinear systems. Assuming that the economic function is a known function of constrained system’s states, parameterized by unknown parameters and time-varying, the controller design objective is to simultaneously identify and regul...

This paper demonstrates how the finite-time identification procedure can be used to improve the overall performance of adaptive control systems. First, we develop an adaptive compensator which guarantees exponential convergence of the estimation error provided the integral of a filtered regressor matrix is positive definite. The approach does not i...

In this paper, we consider the problem of Adaptive model predictive control subject to exogenous disturbances. Using a novel set-based adaptive estimation, the problem of robust adaptive MPC is proposed and solved for a class of linearly parameterized uncertain nonlinear systems subject to state and input constraints. Two formulations of the adapti...

In this paper, a method is proposed for the adaptive model predictive control of constrained nonlinear system. Rather than relying on the inherent robustness properties of standard NMPC, the developed technique explicitly account for the transient effect of parametric estimation error by combining a parameter adjustment mechanism with robust MPC al...

The work presented in this chapter transcends beyond characterizing the parameter convergence rate. A method is presented for computing the exact parameter value at a finite-time selected according to the observed excitation in the system. A smooth transition from a standard estimate to the FT estimate is proposed. In the presence of unknown bounde...

This note presents a parameter estimation routine that allows exact reconstruction of the unknown parameters in finite time provided a given excitation condition is satisfied. The robustness of the routine to an unknown bounded disturbance or modeling error is also shown. The result is independent of the control and identifier structures employed....

This paper addresses the problem of parameter convergence in adaptive extremum-seeking control design. An alternate version of the popular persistence of excitation condition is proposed for a class of nonlinear systems with parametric uncertainties. The condition is translated to an asymptotic sufficient richness condition on the reference set-poi...

In most adaptive control approaches, parameter convergence to their true values can only be ensured if the closed-loop trajectories provide sufficient excitation for the parameter estimation method. In this paper, the design of excitation signal for the adaptive control of linearizable systems is investigated. Based on a sufficient richness conditi...

This paper addresses the problem of parameter convergence in adaptive extremum seeking control design. An alternate version of the popular persistence of excitation condition is proposed for a class of nonlinear systems with parametric uncertainties. The condition is translated to an asymptotic sufficient richness condition on the reference set-poi...

In this paper, we present a control algorithm that incorporates real-time optimization (RTO) and receding horizon control (RHC) technique to solve an output feedback extremum seeking control problem for a linear unknown system. The development of the controller consist of two steps. First, the optimum setpoint that minimizes a given performance fun...

In this paper, we present an output feedback receding horizon control for a class of SISO nonlinear systems. A globally stabilizing state feedback receding horizon control scheme is combined with a discretized high gain observer. This is motivated by the fact that measurable system’s outputs are only available at specific sampling intervals. Our re...

In this paper, a method is proposed for the adaptive receding horizon control of nonlinear systems. The method is based on known receding horizon control methods that employ control Lyapunov functions. The method proposed uses input to state stabilizing control Lyapunov functions to ensure stability and achieve a certain level of performance. A sim...

We present a control algorithm that incorporates real time optimization and receding horizon control technique to solve an extremum seeking control problem for a class of nonlinear systems with parametric uncertainties. A Lyapunov-based technique is employed to develop a receding horizon controller that drives the system states to the desired unkno...

In this paper, we present an output feedback receding horizon control for a class of nonlinear systems. A globally stabilizing state feedback receding horizon scheme is combined with a discretized high gain observer. It is shown that the output feedback scheme recovers the performance (rate of convergence) achieved under state feedback receding hor...

Although there is great motivation for adaptive approaches to nonlinear model prediction control, few results to date can guarantee feasible adaptive stabilization in the presence of state or input constraints. By adapting a set-valued measure of the parametric uncertainty within the framework of robust nonlinear-MPC, the results of this paper esta...