Robust and Resilient Control Design and Performance Analysis for Uncertain Systems with Finite Energy Disturbances

This dissertation addresses the problem of robust and resilient control design with additional performance analysis for uncertain systems with finite energy disturbances. The control design is robust and resilient in the sense of maintaining certain performance criteria in the presence of perturbations in both system parameters and feedback gains. The performance analysis evaluates resilience properties of state feedback and dynamic (state estimate) feedback controllers. A resilient and robust state feedback controller is designed using linear matrix inequality (LMI) techniques for the characterization of solutions to the analysis and design problems posed in this work. Uncertainties are allowed in the linear and nonlinear parts of the system model and also in the feedback gain so that the designed controller is robust in addition to being resilient. The design of controllers for various performance criteria including asymptotic stability, H2, Hinf, input strict passivity, output strict passivity and very strict passivity are presented. In addition to the design problem, an approach is presented for performance analysis of the resilience property of perturbed controller and observer gains. The resilience property is defined in terms of both multiplicative and additive perturbations on the gains so that the closed loop eigenvalues do not leave a specified region in the complex plane, such as a vertical strip, disk, sector region, etc. The method presented allows maximum gain perturbation bounds to be obtained based on the designer’s choices of controller eigenvalue region. The LMI technique is used also for the analysis process. Both design and analysis problems are treated using Lyapunov functions. All work is conducted for both continuous- and discrete-time cases. Several illustrative simulation examples are included to show the effectiveness of the proposed design and analysis approaches

Performance-Robust Dynamic Feedback Control of Lipschitz Nonlinear Systems

This dissertation addresses the dynamic control of nonlinear systems with finite energy noise in the state and measurement equations. Regional eigenvalue assignment (REA) is used to ensure that the state estimate error is driven to zero significantly faster than the state itself. Moreover, the controller is designed for the resulting closed loop system to achieve any one of a set of general performance criteria (GPC). The nonlinear model is assumed to have a Lipschitz nonlinearity both in the state and measurement equations. By using the norm bound of the nonlinearity, the controller is designed to be robust against all nonlinearities satisfying the norm-bound. A Luenberger-type nonlinear observer is used to estimate the system state, which is not directly measurable. The choice of the eigenvalue locations for the linear part of the system is based on the transient response specifications and the separation of the controller dynamics from the observer dynamics. Furthermore, the GPC are incorporated to achieve performance requirements such as H2, H∞, etc. The advantage of using GPC is it allows the designer flexibility in choosing a performance objective to tune the system. The design problem introduced in this dissertation uses various mathematical techniques to derive LMI conditions for the controller and observer design using REA, GPC, and the bounds on the Lipschitz nonlinearities. All work will be demonstrated in both continuous- and discrete-time. Illustrative examples in both time domains are given to demonstrate the proposed design procedure. Multiple numerical approaches are also presented and compared in simulations for ease of use, applicability, and conservatism

Sparse Wide-Area Control of Power Systems using Data-driven Reinforcement Learning

In this paper we present an online wide-area oscillation damping control (WAC) design for uncertain models of power systems using ideas from reinforcement learning. We assume that the exact small-signal model of the power system at the onset of a contingency is not known to the operator and use the nominal model and online measurements of the generator states and control inputs to rapidly converge to a state-feedback controller that minimizes a given quadratic energy cost. However, unlike conventional linear quadratic regulators (LQR), we intend our controller to be sparse, so its implementation reduces the communication costs. We, therefore, employ the gradient support pursuit (GraSP) optimization algorithm to impose sparsity constraints on the control gain matrix during learning. The sparse controller is thereafter implemented using distributed communication. Using the IEEE 39-bus power system model with 1149 unknown parameters, it is demonstrated that the proposed learning method provides reliable LQR performance while the controller matched to the nominal model becomes unstable for severely uncertain systems.Comment: Submitted to IEEE ACC 2019. 8 pages, 4 figure

Analysis, filtering, and control for Takagi-Sugeno fuzzy models in networked systems

Copyright © 2015 Sunjie Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.The fuzzy logic theory has been proven to be effective in dealing with various nonlinear systems and has a great success in industry applications. Among different kinds of models for fuzzy systems, the so-called Takagi-Sugeno (T-S) fuzzy model has been quite popular due to its convenient and simple dynamic structure as well as its capability of approximating any smooth nonlinear function to any specified accuracy within any compact set. In terms of such a model, the performance analysis and the design of controllers and filters play important roles in the research of fuzzy systems. In this paper, we aim to survey some recent advances on the T-S fuzzy control and filtering problems with various network-induced phenomena. The network-induced phenomena under consideration mainly include communication delays, packet dropouts, signal quantization, and randomly occurring uncertainties (ROUs). With such network-induced phenomena, the developments on T-S fuzzy control and filtering issues are reviewed in detail. In addition, some latest results on this topic are highlighted. In the end, conclusions are drawn and some possible future research directions are pointed out.This work was supported in part by the National Natural Science Foundation of China under Grants 61134009, 61329301, 11301118 and 61174136, the Natural Science Foundation of Jiangsu Province of China under Grant BK20130017, the Fundamental Research Funds for the Central Universities of China under Grant CUSF-DH-D-2013061, the Royal Society of the U.K., and the Alexander von Humboldt Foundation of Germany

Robust Decentralized Secondary Frequency Control in Power Systems: Merits and Trade-Offs

Frequency restoration in power systems is conventionally performed by broadcasting a centralized signal to local controllers. As a result of the energy transition, technological advances, and the scientific interest in distributed control and optimization methods, a plethora of distributed frequency control strategies have been proposed recently that rely on communication amongst local controllers. In this paper we propose a fully decentralized leaky integral controller for frequency restoration that is derived from a classic lag element. We study steady-state, asymptotic optimality, nominal stability, input-to-state stability, noise rejection, transient performance, and robustness properties of this controller in closed loop with a nonlinear and multivariable power system model. We demonstrate that the leaky integral controller can strike an acceptable trade-off between performance and robustness as well as between asymptotic disturbance rejection and transient convergence rate by tuning its DC gain and time constant. We compare our findings to conventional decentralized integral control and distributed-averaging-based integral control in theory and simulations

A novel technique for load frequency control of multi-area power systems

In this paper, an adaptive type-2 fuzzy controller is proposed to control the load frequency of a two-area power system based on descending gradient training and error back-propagation. The dynamics of the system are completely uncertain. The multilayer perceptron (MLP) artificial neural network structure is used to extract Jacobian and estimate the system model, and then, the estimated model is applied to the controller, online. A proportional–derivative (PD) controller is added to the type-2 fuzzy controller, which increases the stability and robustness of the system against disturbances. The adaptation, being real-time and independency of the system parameters are new features of the proposed controller. Carrying out simulations on New England 39-bus power system, the performance of the proposed controller is compared with the conventional PI, PID and internal model control based on PID (IMC-PID) controllers. Simulation results indicate that our proposed controller method outperforms the conventional controllers in terms of transient response and stability