A Delay-Aware Cyber-Physical Architecture for Wide-Area Control of Power Systems

In this paper we address the problem of widearea control of power systems in presence of different classes of network delays. We pose the control objective as an LQR minimization of the electro-mechanical states of the swing equations, and exploit flexibilities and transparencies of the communication network such as scheduling policies, bandwidth to co-design a delay-aware state feedback control law. Hence, unlike the traditional robust control designs, our design is delayaware, not delay-tolerant. A key feature of our method is to retain the samples of the control input until a desired time instant using shapers before releasing them for actuation to regulate the delays entering the controller. In addition, our codesign includes an overrun management strategy to guarantee stability of the closed-loop power system model in case of occasional PMU data losses. This strategy allows dropping messages with very large delays, reducing resource utilization during busy network times, and improving overall performance of the system. We illustrate our results using a 50-bus, 14- generator, 4-area power system model, and show how the proposed arbitrated controller can guarantee significantly better closed-loop performance than traditional robust controllers.NSF Grant No. ECCS-113581

Platooning-based control techniques in transportation and logistic

This thesis explores the integration of autonomous vehicle technology with smart manufacturing systems. At first, essential control methods for autonomous vehicles, including Linear Matrix Inequalities (LMIs), Linear Quadratic Regulation (LQR)/Linear Quadratic Tracking (LQT), PID controllers, and dynamic control logic via flowcharts, are examined. These techniques are adapted for platooning to enhance coordination, safety, and efficiency within vehicle fleets, and various scenarios are analyzed to confirm their effectiveness in achieving predetermined performance goals such as inter-vehicle distance and fuel consumption. A first approach on simplified hardware, yet realistic to model the vehicle's behavior, is treated to further prove the theoretical results. Subsequently, performance improvement in smart manufacturing systems (SMS) is treated. The focus is placed on offline and online scheduling techniques exploiting Mixed Integer Linear Programming (MILP) to model the shop floor and Model Predictive Control (MPC) to adapt scheduling to unforeseen events, in order to understand how optimization algorithms and decision-making frameworks can transform resource allocation and production processes, ultimately improving manufacturing efficiency. In the final part of the work, platooning techniques are employed within SMS. Autonomous Guided Vehicles (AGVs) are reimagined as autonomous vehicles, grouping them within platoon formations according to different criteria, and controlled to avoid collisions while carrying out production orders. This strategic integration applies platooning principles to transform AGV logistics within the SMS. The impact of AGV platooning on key performance metrics, such as makespan, is devised, providing insights into optimizing manufacturing processes. Throughout this work, various research fields are examined, with intersecting future technologies from precise control in autonomous vehicles to the coordination of manufacturing resources. This thesis provides a comprehensive view of how optimization and automation can reshape efficiency and productivity not only in the domain of autonomous vehicles but also in manufacturing

A Systematic Survey of Control Techniques and Applications: From Autonomous Vehicles to Connected and Automated Vehicles

Vehicle control is one of the most critical challenges in autonomous vehicles (AVs) and connected and automated vehicles (CAVs), and it is paramount in vehicle safety, passenger comfort, transportation efficiency, and energy saving. This survey attempts to provide a comprehensive and thorough overview of the current state of vehicle control technology, focusing on the evolution from vehicle state estimation and trajectory tracking control in AVs at the microscopic level to collaborative control in CAVs at the macroscopic level. First, this review starts with vehicle key state estimation, specifically vehicle sideslip angle, which is the most pivotal state for vehicle trajectory control, to discuss representative approaches. Then, we present symbolic vehicle trajectory tracking control approaches for AVs. On top of that, we further review the collaborative control frameworks for CAVs and corresponding applications. Finally, this survey concludes with a discussion of future research directions and the challenges. This survey aims to provide a contextualized and in-depth look at state of the art in vehicle control for AVs and CAVs, identifying critical areas of focus and pointing out the potential areas for further exploration

Fractional Order State Feedback Control for Improved Lateral Stability of Semi-Autonomous Commercial Heavy Vehicles

With the growing development of autonomous and semi-autonomous large commercial heavy vehicles, the lateral stability control of articulated vehicles have caught the attention of researchers recently. Active vehicle front steering (AFS) can enhance the handling performance and stability of articulated vehicles for an emergency highway maneuver scenario. However, with large vehicles such tractor-trailers, the system becomes more complex to control and there is an increased occurrence of instabilities. This research investigates a new control scheme based on fractional calculus as a technique that ensures lateral stability of articulated large heavy vehicles during evasive highway maneuvering scenarios. The control method is first implemented to a passenger vehicle model with 2-axles based on the well-known “bicycle model”. The model is then extended and applied onto larger three-axle commercial heavy vehicles in platooning operations. To validate the proposed new control algorithm, the system is linearized and a fractional order PI state feedback control is developed based on the linearized model. Then using Matlab/Simulink, the developed fractional-order linear controller is implemented onto the non-linear tractor-trailer dynamic model. The tractor-trailer system is modeled based on the conventional integer-order techniques and then a non-integer linear controller is developed to control the system. Overall, results confirm that the proposed controller improves the lateral stability of a tractor-trailer response time by 20% as compared to a professional truck driver during an evasive highway maneuvering scenario. In addition, the effects of variable truck cargo loading and longitudinal speed are evaluated to confirm the robustness of the new control method under a variety of potential operating conditions

Cyber-resilient Automatic Generation Control for Systems of AC Microgrids

In this paper we propose a co-design of the secondary frequency regulation in systems of AC microgrids and its cyber securty solutions. We term the secondary frequency regulator a Micro-Automatic Generation Control (Micro-AGC) for highlighting its same functionality as the AGC in bulk power systems. We identify sensory challenges and cyber threats facing the Micro-AGC. To address the sensory challenges, we introduce a new microgrid model by exploiting the rank-one deficiency property of microgrid dynamics. This model is used to pose an optimal Micro-AGC control problem that is easily implemented, because it does not require fast frequency measurements. An end-to-end cyber security solution to the False Data Injection (FDI) attack detection and mitigation is developed for the proposed Micro-AGC. The front-end barrier of applying off-the-shelf algorithms for cyber attack detection is removed by introducing a data-driven modeling approach. Finally, we propose an observer-based corrective control for an islanded microgrid and a collaborative mitigation schemes in systems of AC microgrids. We demonstrate a collaborative role of systems of microgrids during cyber attacks. The performance of the proposed cyber-resilient Micro-AGC is tested in a system of two networked microgrids.Comment: The manuscript has been accepted by IEEE Transactions on Smart Gri

Modeling of a Hybrid-Electric System and Design of Control Laws for Hybrid-Electric Urban Air Mobility Power Plants

Advanced Air Mobility (AAM) is an emerging market and technology in the aerospace industry. These systems are being developed to overcome traffic congestion. The current designs make use of Distributed Electric Propulsion (DEP): either fully electric or hybrid electric. The hybrid engine system consists of two power sources: prime movers, such as turbine engines, and batteries. The hybrid systems offer higher range and endurance compared with the existing fully electric systems. Hybrid-electric power generation systems for AAM have different mission requirements when compared to systems used in automobiles. Therefore, there is a particular need to model hybrid-electric systems and the development of control logic specifically for AAM aircraft. This thesis focusses on the modeling and design of control logic for hybrid-electric power plants for Advanced Air Mobility (AAM) applications. The developed model can assist in designing and optimizing the system as well as supporting the system architecture. These models can also help the testing and integration of hardware and software of systems and sub-systems, also known as software-in-the-loop and hardware-in-the-loop simulations. A state-space representation of the hybrid-electric system is created and validated with experimental results to facilitate the use of modern controls methods. A control law for the hybrid-electric system was also developed to meet the AAM aircraft mission requirement of generating the required electrical power and maintaining the State of Charge (SOC) of the batteries

Reset controller design based on error minimization for a lane change maneuver

An intelligent vehicle must face a wide variety of situations ranging from safe and comfortable to more aggressive ones. Smooth maneuvers are adequately addressed by means of linear control, whereas more aggressive maneuvers are tackled by nonlinear techniques. Likewise, there exist intermediate scenarios where the required responses are smooth but constrained in some way (rise time, settling time, overshoot). Due to the existence of the fundamental linear limitations, which impose restrictions on the attainable time-domain and frequency-domain performance, linear systems cannot provide smoothness while operating in compliance with the previous restrictions. For this reason, this article aims to explore the effects of reset control on the alleviation of these limitations for a lane change maneuver under a set of demanding design conditions to guarantee a suitable ride quality and a swift response. To this end, several reset strategies are considered, determining the best reset condition to apply as well as the magnitude thereto. Concerning the reset condition that triggers the reset action, three strategies are considered: zero crossing of the controller input, fixed reset band and variable reset band. As far as the magnitude of the reset action is concerned, a full-reset technique is compared to a Lyapunov-based error minimization method to calculate the optimal reset percentage. The base linear controller subject to the reset action is searched via genetic algorithms. The proposed controllers are validated by means of CarSim.Agencia Estatal de Investigación | Ref. DPI2016-79278-C2-2-

Time Delay Compensation Schemes with Application to Networked Control System

In the project an extensive study of network parameters like data packet drops, data packet delays are pursued by using a network simulator (NS-2). It simulates the transmission of sensor and control packets between plants and controllers. Some fundamental examples were done using this network simulator. Time delay occurs used for networked control system when the exchange of data among sensors, actuators and controllers connected through the shared medium. Such delays affect the system Performance degradation and the reduced stability or total instability of the closed-loop system. In order to compensate time-delay in the networked control system (NCS) there are different time delay compensation schemes are available, which is given by predictive controller, PID controller, LQR controller, fuzzy controller, etc. In this thesis the discrete-time PID controller is used for compensating the time delays in the networked control system. To study in reality an experimental work is done to transfer packet data between two computer systems through a Local area Network (LAN) using UDP protocol. Subsequently the transfer of signal between two computer systems through a LAN using UDP protocol has been also made. These experiments were carried out using SIMULINK Instrument Control Toolbox (ver7.6). Networked predictive control is also designed for networked control of servo system. This control strategy is applied to a servo control system through the Local Area Network (LAN).SMITH-PREDICTOR proposed to compensate the communication delays in the networked control system

Online HVAC Temperature and Air Quality Control for Cost-efficient Commercial Buildings Based on Lyapunov Optimization Technique

Commercial buildings consume up to 35.5% of total electricity consumed in the United States. As a subsystem in the smart building management system, Heating, Ventilation, and Air Conditioning (HVAC) systems are responsible for 45% of electricity consumption in commercial buildings. Therefore, energy management of HVAC systems is of interest. The HVAC system brings thermal and air quality comfort to the occupants of the building, designing a controller that maximizes this comfort is the first objective. Inevitably, ideal comfort tracking means more energy consumption and energy cost. Hence, the more advanced objective is balancing the comfort-cost tradeoff. Since HVAC systems have nonlinear, complex and MIMO characteristics, modeling the system and formulating an optimization problem for them is challenging. Moreover, there are physical and comfort constraints to be satisfied, and randomness of parameters such as thermal disturbances, number of occupants in the building that affects the air quality, thermal and air quality setpoints we want to track, electricity price and outside temperature to be considered. Adding real time analysis to this problem furthers the challenge. In this thesis, utilizing Lyapunov optimization technique, we first transform the constraints to stability equations, and formulate a stochastic optimization problem, then we minimize the time average of the expected cost of the system while the cost is a weighted sum of the discomfort and energy cost. Results show that using the proposed algorithm and real data, the algorithm is feasible, and an optimal solution for the problem is achieved

Event-triggered near optimal adaptive control of interconnected systems

Increased interest in complex interconnected systems like smart-grid, cyber manufacturing have attracted researchers to develop optimal adaptive control schemes to elicit a desired performance when the complex system dynamics are uncertain. In this dissertation, motivated by the fact that aperiodic event sampling saves network resources while ensuring system stability, a suite of novel event-sampled distributed near-optimal adaptive control schemes are introduced for uncertain linear and affine nonlinear interconnected systems in a forward-in-time and online manner. First, a novel stochastic hybrid Q-learning scheme is proposed to generate optimal adaptive control law and to accelerate the learning process in the presence of random delays and packet losses resulting from the communication network for an uncertain linear interconnected system. Subsequently, a novel online reinforcement learning (RL) approach is proposed to solve the Hamilton-Jacobi-Bellman (HJB) equation by using neural networks (NNs) for generating distributed optimal control of nonlinear interconnected systems using state and output feedback. To relax the state vector measurements, distributed observers are introduced. Next, using RL, an improved NN learning rule is derived to solve the HJB equation for uncertain nonlinear interconnected systems with event-triggered feedback. Distributed NN identifiers are introduced both for approximating the uncertain nonlinear dynamics and to serve as a model for online exploration. Next, the control policy and the event-sampling errors are considered as non-cooperative players and a min-max optimization problem is formulated for linear and affine nonlinear systems by using zero-sum game approach for simultaneous optimization of both the control policy and the event based sampling instants. The net result is the development of optimal adaptive event-triggered control of uncertain dynamic systems --Abstract, page iv