Control Of Large-scale Systems Research Articles

Modeling and simulation to optimize direct power control of DFIG in variable-speed pumped-storage power plant using teaching–learning-based optimization technique

The issues related to the optimal control of large-scale storage systems in electric power systems such as pumped storage (PS) plant have turned into vital challenges in the way of integrating renewable energy sources into power systems to provide reliable and economical electric energy. In this regard, this paper uses the direct power control strategy to model and simulate a variable-speed PS plant, which includes a doubly fed induction generator (DFIG). The active and the reactive power of the stator would be able to be controlled, separately. This approach has a better dynamic performance compared to other methods, while it would be quite simple to implement. But there are some shortfalls with this method, such as high ripple relating to the active power as well as reactive power together with the current harmonics. In this respect, the space vector modulation (SVM) is applied to eliminate these shortfalls. In the proposed control technique, including SVM, the dynamic performance of the studied DFIG unit is controlled using the proportional–integral (PI) controller. It should be noted that the teaching–learning-based optimization (TLBO) method is employed to tune the PI controller for controlling the DFIG system in the PS plant. Finally, in order to validate the performance of the suggested framework, a comparison is made between the results obtained by the TLBO and the ones reported by other optimization methods. The obtained results using the TLBO algorithm indicate better performance of the PI controller to reduce the ripples of the active and reactive power of the stator as well as the harmonic power.

Adaptive Decentralized Output Feedback Tracking Control for Large-Scale Interconnected Systems with Time-Varying Output Constraints

This paper investigates a novel adaptive output feedback decentralized control scheme for nonstrict feedback large-scale interconnected systems with time-varying constraints. A decentralized linear state observer is designed to estimate the unmeasurable states of subsystems. Time-varying barrier Lyapunov functions are designed to ensure outputs are not violating constraints. A variable separation approach is applied to deal with the nonstrict feedback problem. Moreover, dynamic surface control and minimal parameter learning technologies are adopted to reduce the computation burden, and there are only two parameters for every subsystem to be updated online. The proof of stability is obtained by the Lyapunov method. Finally, simulation results are given to show the effectiveness of the proposed control scheme.

Observer-Based Adaptive Neural Networks Control for Large-Scale Interconnected Systems With Nonconstant Control Gains.

In this article, an adaptive neural network (NN) decentralized output-feedback control design is studied for the uncertain strict-feedback large-scale interconnected nonlinear systems with nonconstant virtual and control gains. NNs are utilized to approximate the unknown nonlinear functions, and the immeasurable states are estimated via designing an NN decentralized state observer. By constructing the logarithm Lyapunov functions, an observer-based NN adaptive decentralized backstepping output-feedback control is developed in the framework of the decentralized backstepping control. The proposed adaptive decentralized backstepping output-feedback control can make that the closed-loop system is semiglobally uniformly ultimately bounded (SGUUB) and that the tracking and observer errors converge to a small neighborhood of the origin. The most important contribution of this article is that it removes the restrictive assumption in the existing results that both virtual and control gain functions in each subsystem must be constants. A numerical simulation example is provided to validate the effectiveness of the proposed control method and theory.

Event-triggered distributed fault detection and control of multi-weighted and multi-delayed large-scale systems

This paper addresses the problem of distributed simultaneous fault detection and control (SFDC) of multi-weighted and multi-delayed (MWMD) large-scale interconnected systems which are subjected to event-triggered communication, nonlinear perturbations, measured output quantization, redundant channels, and stochastic deception attacks. The large-scale systems under consideration have multiple coupling links between neighboring subsystems, and all the links are considered to have different coupling weights and delays. A distributed fault detector and controller (FDC) module is designed to guarantee the exponential mean square stability of the overall closed-loop system along with a prescribed extended dissipative control performance and H∞ fault detection performance. The gain parameters of the distributed fault detector and controller module are determined by using the cone complementarity linearization (CCL) algorithm. In the end, a numerical example involving a continuous stirred tank reactor (CSTR) system is described to prove the validity of the presented results.

Distributed control system for catalysing the formation of artificial rainfall and snow using charged particles

In order to solve the limitations and shortcomings of the traditional artificial rainfall technology, researchers all over the world are actively researching new artificial rainfall technologies. The charged particle catalytic artificial rainfall technology is one of the new artificial rainfall technologies. This method uses a charged particle generator set up in the wild to produce charged particles to catalyse rainfall. It is necessary to control all equipment in the experiment site remotely during the experiment. Thus, a control system needs to be implemented. There are many kinds of controlled equipment in this control system, such as high-voltage power supply, meteorological data acquisition devices, and various experimental auxiliary equipment. In this paper, we design and implement a comprehensive control system based on the characteristics of the charged particle catalysed artificial rainfall field experiment and the controlled equipment in the experiment site. The software interface connected to different types of equipment in the system is developed based on the large-scale experimental control system framework CFET, which can easily add equipment and view measurement data. The system is designed as a distributed system connected through a network, which can meet the needs of centralized control of multiple experimental sites.

Tillage based, site-specific weed control for conservation cropping systems

AbstractAustralian conservation cropping systems are practiced on very large farms (approximately 3,000 ha) where herbicides are relied on for effective and timely weed control. In many fields, though, there are low weed densities (e.g., <1.0 plant 10 m−2) and whole-field herbicide treatments are wasteful. For fallow weed control, commercially available weed detection systems provide the opportunity for site-specific herbicide treatments, removing the need for whole-field treatment of fallow fields with low weed densities. Concern about the sustainability of herbicide-reliant weed management systems remain and there has not been interest in the use of weed detection systems for alternative weed control technologies, such as targeted tillage. In this paper, we discuss the use of a targeted tillage technique for site-specific weed control in large-scale crop production systems. Three small-scale prototypes were used for engineering and weed control efficacy testing across a range of species and growth stages. With confidence established in the design approach and a demonstrated 100% weed-control potential, a 6-m wide pre-commercial prototype, the “Weed Chipper,” was built incorporating commercially available weed-detection cameras for practical field-scale evaluation. This testing confirmed very high (90%) weed control efficacies and associated low levels (1.8%) of soil disturbance where the weed density was fewer than 1.0 plant 10 m−2 in a commercial fallow. These data established the suitability of this mechanical approach to weed control for conservation cropping systems. The development of targeted tillage for fallow weed control represents the introduction of site-specific, nonchemical weed control for conservation cropping systems.

Predictor methods for decentralized control of large-scale systems with input delays

This paper develops a decentralized predictor-based control for large-scale systems with large input delays under the premise that the interconnections between subsystems are not strong. The local controller operates independently. Given any large delays, the predictor which exponentially stabilizes each uncoupled system, will stabilize the coupled one provided that the coupling is not solid. We propose two methods for the delay compensation: the backstepping-based partial differential equation (PDE) approach and the reduction-based ordinary differential equation (ODE) approach. We present decentralized Lyapunov-based analysis under the two predictor methods. It appears that the first predictor method leads to simpler conditions and manages with larger delays, whereas the second is easily applied to decentralized asynchronous sampled-data implementation, both under continuous and under discrete-time measurements. Through a benchmark example of two coupled cart–pendulum systems, the proposed methods are demonstrated to be effective when the input delays are too large for the system to be stabilized without a predictor.

Distributed Fault-Tolerant Control of Large-Scale Systems: An Active Fault Diagnosis Approach

The paper proposes a methodology to effectively address the increasingly important problem of distributed fault-tolerant control for large-scale interconnected systems. The approach dealt with combines, in a holistic way, a distributed fault detection and isolation algorithm with a specific tube-based model predictive control scheme. A distributed fault-tolerant control strategy is illustrated to guarantee overall stability and constraint satisfaction even after the occurrence of a fault. In particular, each subsystem is controlled and monitored by a local unit. The fault diagnosis component consists of a passive set-based fault detection algorithm and an active fault isolation one, yielding fault-isolability subject to local input and state constraints. The distributed active fault isolation module–thanks to a modification of the local inputs–allows to isolate the fault that has occurred, avoiding the usual drawback of controllers that possibly hide the effect of the faults. The Active Fault Isolation method is used as a decision support tool for the fault-tolerant control strategy after fault detection. The distributed design of the tube-based model predictive control allows the possible disconnection of faulty subsystems or the reconfiguration of local controllers after fault isolation. Simulation results on a well-known power network benchmark show the effectiveness of the proposed methodology.

Nash-based robust distributed model predictive control for large-scale systems

In this paper, a new robust distributed model predictive control (RDMPC) is proposed for large-scale systems with polytopic uncertainties. The time-varying system is first decomposed into several interconnected subsystems. Interactions between subsystems are obtained by a distributed Kalman filter, in which unknown parameters of the system are estimated using local measurements and measurements of neighboring subsystems that are available via a network. Quadratic boundedness is used to guarantee the stability of the closed-loop system. In the MPC algorithm, an output feedback-interaction feedforward control input is computed by an LMI-based optimization problem that minimizes an upper bound on the worst case value of an infinite-horizon objective function. Then, an iterative Nash-based algorithm is presented to achieve the overall optimal solution of the whole system in partially distributed fashion. Finally, the proposed distributed MPC approach is applied to a load frequency control (LFC) problem of a multi-area power network to study the efficiency and applicability of the algorithm in comparison with the centralized, distributed and decentralized MPC schemes.

A long-term unit commitment problem with hydrothermal coordination for economic and emission control in large-scale electricity systems

The paper describes a long-term scheduling problem for thermal power plants and energy storages. In addition, renewable energy sources are integrated by considering the residual demand. Besides the classical minimization of the production costs, emission-related costs are taken into account. Thereby, emission costs are determined by market prices for hbox {CO}_2 emission certificates (i.e., using the EU emissions trading system). For the proposed unit commitment problem with hydrothermal coordination for economic and emission control, an enhanced mixed-integer linear programming model is presented. Moreover, a new heuristic approach is developed, which consists of two solution stages. The heuristic first performs an isolated dispatching of thermal plants. Then, a re-optimization stage is included in order to embed activities of energy storages into the final solution schedule. The considered approach is able to find outstanding schedules for benchmark instances with a planning horizon of up to one year. Furthermore, promising results are also obtained for large-scale real-world electricity systems. For the German electricity market, the relationship of hbox {CO}_2 certificate prices and the optimal thermal dispatch is illustrated by a comprehensive sensitivity analysis.

Echo State Network-Based Decentralized Control of Continuous-Time Nonlinear Large-Scale Interconnected Systems

This article addresses the decentralized control problem of continuous-time nonlinear large-scale interconnected systems by the means of echo state network (ESN). The interconnected terms between the subsystems are treated as the disturbances added to the system dynamics, then the control problem is solved by the proposed robust controllers. By designing a cost function for the nominal subsystems, the robust controller is obtained by solving optimal controllers. The stability of the interconnected systems is proved by a composite Lyapunov function. The online adaptive dynamic program (ADP) method is employed to solve the transformed optimal problem, where a single ESN is utilized to approximate the critic cost and control policy. The optimal control policies of all the isolated subsystems are obtained simultaneously. The closed-loop stability of the feedback nominal systems is also proved, in a uniformly ultimately boundedness (UUB) manner. In the simulation, a large-scale interconnected system with four subsystems is provided to verify the effectiveness of the proposed decentralized controllers.

Differential-game for resource aware approximate optimal control of large-scale nonlinear systems with multiple players

In this paper, we propose a novel differential-game based neural network (NN) control architecture to solve an optimal control problem for a class of large-scale nonlinear systems involving N-players. We focus on optimizing the usage of the computational resources along with the system performance simultaneously. In particular, the N-players’ control policies are desired to be designed such that they cooperatively optimize the large-scale system performance, and the sampling intervals for each player are desired to reduce the frequency of feedback execution. To develop a unified design framework that achieves both these objectives, we propose an optimal control problem by integrating both the design requirements, which leads to a multi-player differential-game. A solution to this problem is numerically obtained by solving the associated Hamilton-Jacobi (HJ) equation using event-driven approximate dynamic programming (E-ADP) and artificial NNs online and forward-in-time. We employ the critic neural networks to approximate the solution to the HJ equation, i.e., the optimal value function, with aperiodically available feedback information. Using the NN approximated value function, we design the control policies and the sampling schemes. Finally, the event-driven N-player system is remodeled as a hybrid dynamical system with impulsive weight update rules for analyzing its stability and convergence properties. The closed-loop practical stability of the system and Zeno free behavior of the sampling scheme are demonstrated using the Lyapunov method. Simulation results using a numerical example are also included to substantiate the analytical results.

Decentralized observer-based optimal control using two-point boundary value successive approximation approach application for a multimachine power system

In this paper, we present a new decentralized observer for a class of nonlinear interconnected systems, based on an optimal control law using the two-point boundary value (TPBV) successive approximation approach. This technique, used previously to develop a nonlinear decentralized suboptimal control for a multimachine power system, inspired us to develop a new method to design a decentralized observer-based suboptimal control law for the same system. The TPBV approach is characterized by the transformation of each high order coupling nonlinear TPBV problem into a sequence of linear decoupling TPBV problems that uniformly converge to the optimal control for nonlinear interconnected large-scale systems. Sufficient conditions for the asymptotic stability of the developed feedback control scheme are proposed in terms of linear matrix inequalities (LMIs) constraints, which can be efficiently solved by the LMI optimization techniques, to compute the control and the observation gains of the overall system. We applied this approach to a three-machine power system which generators are nonlinear systems strongly interconnected. We demonstrated clearly, via advanced simulations, that this approach was able to bring good performances, improving effectively transient stability of this power system in few iterative sequences while allowing the reconstruction of its non-measurable state variables.

Annular Domain Finite-Time Connective Control for Large-Scale Systems With Expanding Construction

This article focuses on an annular domain finite-time connectively bounded (ADFTCB) problem for a large-scale system with expanding construction (LSWEC). The LSWEC is built via adding new subsystems into the original system which is operating. Inspired by the notion of finite-time annular domain stability (FTADS), a new concept, ADFTCB, is presented in this article, and it is extended to LSWEC for the first time. First of all, the mathematical models of LSWEC and LSWEC with observer are built, and then the corresponding decentralized state-feedback stabilizers and output-feedback stabilizers with observers are designed with the aid of finite-time Lyapunov theory and linear matrix inequality (LMI) method which can make the closed-loop system ADFTCB. A simulation study is provided to demonstrate the feasibility and effectiveness of the presented strategy.

Finite-time decentralized H∞ control for singular large-scale systems

Abstract In this paper, the definition of finite-time robust H∞ control for linear continuous-time singular large-scale systems is presented. The main aim of this paper is to design a decentralized state feedback controller which ensures that the closed-loop system is finite-time bounded (FTB), and the effect of the disturbance input on the controller output, meanwhile, is reduced to a prescribed level. A sufficient condition is presented for the solvability of this problem, which can be reduced to a feasibility problem involving linear matrix inequalities (LMIs). A detailed solving method is proposed for the restricted linear matrix inequalities. Finally, examples are given to show the validity of the methodology.

Distributed Model Predictive Safety Certification for Learning-based Control

While distributed algorithms provide advantages for the control of complex large-scale systems by requiring a lower local computational load and less local memory, it is a challenging task to design high-performance distributed control policies. Learning-based control algorithms offer promising opportunities to address this challenge, but generally cannot guarantee safety in terms of state and input constraint satisfaction. A recently proposed safety framework for centralized linear systems ensures safety by matching the learning-based input online with the initial input of a model predictive control law capable of driving the system to a terminal set known to be safe. We extend this idea to derive a distributed model predictive safety certification (DMPSC) scheme, which is able to ensure state and input constraint satisfaction when applying any learning-based control algorithm to an uncertain distributed linear system with dynamic couplings. The scheme is based on a distributed tube-based model predictive control (MPC) concept, where subsystems negotiate local tube sizes among neighbors in order to mitigate restrictiveness of the safety approach. In addition, we present a technique for generating a structured ellipsoidal robust positive invariant tube. In numerical simulations, we show that the safety framework ensures constraint satisfaction for an initially unsafe control policy and allows to improve overall control performance compared to robust distributed MPC.

Non-Linear Tank Level Control for Industrial Applications

Tank level control is ubiquitous in industry. The focus of this paper is on accurate liquid level control in single tank systems which can be actuated continuously and modulation of the level setpoint is also required, for example in cascade control loops or supervisory Model Predictive Control (MPC) applications. To avoid common problems encountered when using fixed gain or adaptive/gain scheduled schemes, an accurate technique based around feedback linearization and Proportional Integral (PI) control is introduced. This simple controller can maintain linear performance over the full operating range of a uniform tank. As will be demonstrated, the implementation overhead compared to a regular PI controller is negligible, making it ideal for industrial implementation. Implementation details and parameter identification for adaptive implementation are discussed. Simulations coupled with experimental results using a large-scale laboratory level control system using commercial industrial control equipment validate the approach, and illustrate its effectiveness for both level tracking and disturbance rejection.

Resource Allocation in Large-Scale Wireless Control Systems with Graph Neural Networks

Modern control systems routinely employ wireless networks to exchange information between a large number of plants, actuators and sensors. While wireless networks are defined by random, rapidly changing conditions that challenge common control design assumptions, properly allocating communication resources helps to maintain operation reliable. Designing resource allocation policies is usually challenging and requires explicit knowledge of the system and communication dynamics, but recent works have successfully explored deep reinforcement learning techniques to find optimal model-free resource allocation policies. Deep reinforcement learning algorithms do not necessarily scale well, however, which limits the immediate generalization of those approaches to large-scale wireless control systems. In this paper we discuss the use of reinforcement learning and graph neural networks (GNNs) to design model-free, scalable resource allocation policies. On the one hand, GNNs generalize the spatial-temporal convolutions present in convolutional neural networks (CNNs) to data defined over arbitrary graphs. In doing so, GNNs manage to exploit local regular structure encoded in graphs to reduce the dimensionality of the learning space. The architecture of the wireless network, on the other, defines an underlying communication graph that can be used as basis for a GNN model. Numerical experiments show the learned policies outperform baseline resource allocation solutions.

Hierarchical Attack Identification for Distributed Robust Nonlinear Control

Developing tools for attack identification in large-scale networked control systems is a research area of increasing significance for the secure and reliable operation of autonomous control systems. Due to scalability limits and privacy issues of individual subsystems, attack identification methods should not rely on global model knowledge. We address systems of interconnected nonlinear subsystems with coupled dynamics or constraints in a distributed control setup. The local controllers share information about the coupling variables of the subsystems and are designed to be robust towards attacks and uncertain influences through neighboring subsystems. We present a scalable hierarchical attack identification method which monitors the evolution of the coupling variables after an attack occurred in some unknown subsystem. Based on the mutual exchange of local sensitivity information among the subsystems, the propagation of the attack through the network is approximated. The propagation equations are used to formulate a quadratic program whose solution determines the attack signal that explains the observed network evolution best. The developed approach is applied to the IEEE 30 bus system to illustrate attack identification in power systems with faulty buses.

Special Issue “Uncertainties in large-scale networked control systems”

Special Issue “Uncertainties in large-scale networked control systems”