Recent advances on filtering and control for nonlinear stochastic complex systems with incomplete information: A survey

This Article is provided by the Brunel Open Access Publishing Fund - Copyright @ 2012 Hindawi PublishingSome recent advances on the filtering and control problems for nonlinear stochastic complex systems with incomplete information are surveyed. The incomplete information under consideration mainly includes missing measurements, randomly varying sensor delays, signal quantization, sensor saturations, and signal sampling. With such incomplete information, the developments on various filtering and control issues are reviewed in great detail. In particular, the addressed nonlinear stochastic complex systems are so comprehensive that they include conventional nonlinear stochastic systems, different kinds of complex networks, and a large class of sensor networks. The corresponding filtering and control technologies for such nonlinear stochastic complex systems are then discussed. Subsequently, some latest results on the filtering and control problems for the complex systems with incomplete information are given. Finally, conclusions are drawn and several possible future research directions are pointed out.This work was supported in part by the National Natural Science Foundation of China under Grant nos. 61134009, 61104125, 61028008, 61174136, 60974030, and 61074129, the Qing Lan Project of Jiangsu Province of China, the Project sponsored by SRF for ROCS of SEM of China, the Engineering and Physical Sciences Research Council EPSRC of the UK under Grant GR/S27658/01, the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany

Advanced Control of Small-Scale Power Systems with Penetration of Renewable Energy Sources

Stability, protection, and operational restrictions are important factors to be taken into account in a proper integration of distributed energy. The objective of this research is presenting advanced controllers for small-scale power systems with penetration of renewable energy sources resources to ensure stable operation after the network disturbances. Power systems with distributed energy resources are modeled and controlled through applying nonlinear control methods to their power electronic interfaces in this research. The stability and control of both ac and dc systems have been studied in a multi-source framework. The dc distribution system is represented as a class of interconnected, nonlinear discrete-time systems with unknown dynamics. It comprises several dc sources, here called subsystems, along with resistive and constant-power loads (which exhibit negative resistance characteristics and reduce the system stability margins.) Each subsystem includes a dc-dc converter (DDC) and exploits distributed energy resources (DERs) such as photovoltaic, wind, etc. Due to the power system frequent disturbances this system is prone to instability in the presence of the DDC dynamical components and constant-power loads. On the other hand, designing a centralized controller may not be viable due to the distance between the subsystems (dc sources.) In this research it is shown that the stability of an interconnected dc distribution system is enhanced through decentralized discrete-time adaptive nonlinear controller design that employs neural networks (NNs) to mitigate voltage and power oscillations after disturbances have occurred. The ac power system model is comprised of conventional synchronous generators (SGs) and renewable energy sources, here, called renewable generators (RGs,) via grid-tie inverters (GTI.) A novel decentralized adaptive neural network (NN) controller is proposed for the GTI that makes the device behave as a conventional synchronous generator. The advantage of this modeling is that all available damping controllers for synchronous generator, such as AVR (Automatic Voltage Regulator) + PSS (Power System Stabilizer), can be applied to the renewable generator. Simulation results on both types of grids show that the proposed nonlinear controllers are able to mitigate the oscillations in the presence of disturbances and adjust the renewable source power to maintain the grid voltage close to its reference value. The stability of the interconnected grids has been enhanced in comparison to the conventional methods

Robust model-based fault estimation and fault-tolerant control : towards an integration

To maintain robustly acceptable system performance, fault estimation (FE) is adopted to reconstruct fault signals and a fault-tolerant control (FTC) controller is employed to compensate for the fault effects. The inevitably existing system and estimation uncertainties result in the so-called bi-directional robustness interactions defined in this work between the FE and FTC functions, which gives rise to an important and challenging yet open integrated FE/FTC design problem concerned in this thesis. An example of fault-tolerant wind turbine pitch control is provided as a practical motivation for integrated FE/FTC design.To achieve the integrated FE/FTC design for linear systems, two strategies are proposed. A H∞ optimization based approach is first proposed for linear systems with differentiable matched faults, using augmented state unknown input observer FE and adaptive sliding mode FTC. The integrated design is converted into an observer-based robust control problem solved via a single-step linear matrix inequality formulation.With the purpose of an integrated design with more freedom and also applicable for a range of general fault scenarios, a decoupling approach is further proposed. This approach can estimate and compensate unmatched non-differentiable faults and perturbations by combined adaptive sliding mode augmented state unknown input observer and backstepping FTC controller. The observer structure renders a recovery of the Separation Principle and allows great freedom for the FE/FTC designs.Integrated FE/FTC design strategies are also developed for Takagi-Sugeno fuzzy modelling nonlinear systems, Lipschitz nonlinear systems, and large-scale interconnected systems, based on extensions of the H∞ optimization approach for linear systems.Tutorial examples are used to illustrate the design strategies for each approach. Physical systems, a 3-DOF (degree-of-freedom) helicopter and a 3-machine power system, are used to provide further evaluation of the proposed integrated FE/FTC strategies. Future research on this subject is also outlined

On the Control of Microgrids Against Cyber-Attacks: A Review of Methods and Applications

Nowadays, the use of renewable generations, energy storage systems (ESSs) and microgrids (MGs) has been developed due to better controllability of distributed energy resources (DERs) as well as their cost-effective and emission-aware operation. The development of MGs as well as the use of hierarchical control has led to data transmission in the communication platform. As a result, the expansion of communication infrastructure has made MGs as cyber-physical systems (CPSs) vulnerable to cyber-attacks (CAs). Accordingly, prevention, detection and isolation of CAs during proper control of MGs is essential. In this paper, a comprehensive review on the control strategies of microgrids against CAs and its defense mechanisms has been done. The general structure of the paper is as follows: firstly, MGs operational conditions, i.e., the secure or insecure mode of the physical and cyber layers are investigated and the appropriate control to return to a safer mode are presented. Then, the common MGs communication system is described which is generally used for multi-agent systems (MASs). Also, classification of CAs in MGs has been reviewed. Afterwards, a comprehensive survey of available researches in the field of prevention, detection and isolation of CA and MG control against CA are summarized. Finally, future trends in this context are clarified

Decentralized adaptive neural network control of interconnected nonlinear dynamical systems with application to power system

Traditional nonlinear techniques cannot be directly applicable to control large scale interconnected nonlinear dynamic systems due their sheer size and unavailability of system dynamics. Therefore, in this dissertation, the decentralized adaptive neural network (NN) control of a class of nonlinear interconnected dynamic systems is introduced and its application to power systems is presented in the form of six papers. In the first paper, a new nonlinear dynamical representation in the form of a large scale interconnected system for a power network free of algebraic equations with multiple UPFCs as nonlinear controllers is presented. Then, oscillation damping for UPFCs using adaptive NN control is discussed by assuming that the system dynamics are known. Subsequently, the dynamic surface control (DSC) framework is proposed in continuous-time not only to overcome the need for the subsystem dynamics and interconnection terms, but also to relax the explosion of complexity problem normally observed in traditional backstepping. The application of DSC-based decentralized control of power system with excitation control is shown in the third paper. On the other hand, a novel adaptive NN-based decentralized controller for a class of interconnected discrete-time systems with unknown subsystem and interconnection dynamics is introduced since discrete-time is preferred for implementation. The application of the decentralized controller is shown on a power network. Next, a near optimal decentralized discrete-time controller is introduced in the fifth paper for such systems in affine form whereas the sixth paper proposes a method for obtaining the L2-gain near optimal control while keeping a tradeoff between accuracy and computational complexity. Lyapunov theory is employed to assess the stability of the controllers --Abstract, page iv

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

Load frequency controllers considering renewable energy integration in power system

Abstract: Load frequency control or automatic generation control is one of the main operations that take place daily in a modern power system. The objectives of load frequency control are to maintain power balance between interconnected areas and to control the power flow in the tie-lines. Electric power cannot be stored in large quantity that is why its production must be equal to the consumption in each time. This equation constitutes the key for a good management of any power system and introduces the need of more controllers when taking into account the integration of renewable energy sources into the traditional power system. There are many controllers presented in the literature and this work reviews the traditional load frequency controllers and those, which combined the traditional controller and artificial intelligence algorithms for controlling the load frequency

Adaptive NN output-feedback control for stochastic time-delay nonlinear systems with unknown control coefficients and perturbations

This paper addresses the problem of adaptive output-feedback control for more general class of stochastic time-varying delay nonlinear systems with unknown control coefficients and perturbations. By using Lyapunov–Krasovskii functional, backstepping and tuning function technique, a novel adaptive neural network (NN) output-feedback controller is constructed with fewer learning parameters. The designed controller guarantees that all the signals in the closed-loop system are 4-moment (or mean square) semi-globally uniformly ultimately bounded (SGUUB). Finally, a simulation example is shown to demonstrate the effectiveness of the proposed control scheme