Control Of Large-scale Systems Research Articles

Attack-Isolation-Based Resilient Control of Large-Scale Systems Against Collusive Attacks

This paper investigates the resilient stability problem of large-scale systems under covert attacks. Covert attacks are difficult to detect, and the collusion among attacks on different subsystems makes the problem more challenging. To address this issue, a two-stage fixed-time observer is introduced for each subsystem to make the covert attack be exposed to its neighboring subsystems. Based on this, an attack isolation algorithm is developed against non-collusive covert attacks, and the notion of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$r^*$</tex-math></inline-formula> -isolability is introduced to provide a necessary and sufficient condition for the success of attack isolation under the algorithm. To deal with collusive covert attacks, another attack isolation algorithm is further designed, and the notion of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$(r,s)$</tex-math></inline-formula> -isolability is used to provide a sufficient condition for the effectiveness of the algorithm. Based on the proposed attack isolation techniques, a control law is developed to ensure the resilient stability of the system. Finally, two numerical examples are given to illustrate the effectiveness of the theoretical results.

Energy-aware scheduling for dependent tasks in heterogeneous multiprocessor systems

Heterogeneous multiprocessor platform has been widely adopted as an effective approach to providing strong calculation capability while keeping complexity and energy consumption under control in large-scale systems. Although this platform is able to achieve efficient cost reduction and flexibility enhancement in the design and development process of real-time applications, it brings a serious and complex multi-task scheduling problem, especially for dependent tasks with energy consumption constraints. All tasks should be scheduled according to appropriate strategies such that their dependence requirements and energy consumption limitations would be satisfied even in the worst-case situations. In this work, we focus on the energy-aware scheduling problem of dependent tasks in heterogeneous multiprocessor systems. First, we model the dependent tasks and heterogeneous processors, and formulate the energy-aware scheduling problem as a constrained optimization one with an objective of minimizing the schedule length of tasks. Then, by adopting an efficient task prioritization strategy and a weight-based energy distribution strategy, we propose a list-based energy-aware scheduling algorithm to seek an approximate optimal start time and processor allocation for each task, guaranteeing that all tasks would be executed efficiently while meeting the dependence and energy requirements. Experiments with randomly generated tasks are conducted to evaluate the performances of the proposed approach in terms of schedule length, optimal solution ratio, and execution time.

Removing the feasibility conditions on adaptive fuzzy decentralized tracking control of large-scale nonlinear systems with full-state constraints

This work is dedicated to solving the adaptive fuzzy decentralized tracking control issue of large-scale nonlinear systems with full-state constraints. Different with barrier Lyapunov function, the main difference is that a novel nonlinear state-dependent function (NSDF) is introduced to prevent the state constraints being overstepped. Based on NSDF, the necessary feasibility conditions for virtual controllers are completely removed. Then, the prior knowledge of the unknown virtual control coefficients is no longer required since the original system is transformed via the new affine variable. Under the control strategy, three objectives on system performance are achieved: (a) all signals of the closed-loop system are bounded; (b) the subsystem output closely tracks the reference trajectory and original error is ultimately uniformly bounded; (c) the full-state constraints are not violated for all the time. At the end, two simulation examples are shown to verify the effectiveness of the control method.

Distributed Double-Layered Dynamic Matrix Control for Large-Scale System

A distributed double-layered dynamic matrix control is proposed for large-scale systems. In the scheme, a large-scale system is first decomposed into several low-dimensional subsystems, and then a double-layered dynamic matrix control algorithm with distributed structure is developed for these subsystems. The distributed open-loop prediction equation of each subsystem is formed based on the predicted output of each local subsystem and effects of its interconnecting neighbor subsystems. Due to simultaneous optimization, at each prediction, the coupling effects of neighbor subsystems are not available in time. Thus, the assumed value is utilized instead. In the economic optimization stage, conflicts may occur among different economic optimization goals. Utopia-tracking strategy is introduced to optimize multiple steady-state targets. Then, the obtained steady-state target values are taken as reference values and tracked in subsequent dynamic control. The actual control move for each subsystem is finally calculated. The proposed algorithm is tested on Shell heavy oil fractionator benchmark, and the effectiveness is demonstrated by comparing with the typical double-layered dynamic matrix control algorithm.

A framework for randomized time-splitting in linear-quadratic optimal control

Inspired by the successes of stochastic algorithms in the training of deep neural networks and the simulation of interacting particle systems, we propose and analyze a framework for randomized time-splitting in linear-quadratic optimal control. In our proposed framework, the linear dynamics of the original problem is replaced by a randomized dynamics. To obtain the randomized dynamics, the system matrix is split into simpler submatrices and the time interval of interest is split into subintervals. The randomized dynamics is then found by selecting randomly one or more submatrices in each subinterval. We show that the dynamics, the minimal values of the cost functional, and the optimal control obtained with the proposed randomized time-splitting method converge in expectation to their analogues in the original problem when the time grid is refined. The derived convergence rates are validated in several numerical experiments. Our numerical results also indicate that the proposed method can lead to a reduction in computational cost for the simulation and optimal control of large-scale linear dynamical systems.

A Noise History Decomposition Approach for Decentralized Optimal Control of Large-Scale Systems Defined Over a Weakly Connected Graph

This article investigates the decentralized optimal linear quadratic regulation (LQR) control for large-scale systems. The large-scale system is defined over a weakly connected graph. Assume that the information is transmitted along the edges in the graph, and one sampling period is required for the information to travel across an edge. Under the above setup, the LQR control problem for the strongly connected graph case has been fully solved. However, for the weakly connected graph case, the existing results fail. In this article, a new decomposition approach for noise history is proposed. Then, the LQR control problem of large-scale systems defined over a weakly connected graph can be well solved. In addition, the decentralized realization of the control input is derived based on the decentralized information hierarchy graph (DIHG). The DIHG construction algorithm is also provided. Finally, the effectiveness of the developed design scheme is illustrated by a numerical example.

Optimization strategy of secondary frequency modulation based on dynamic loss model of the energy storage unit

When the Energy Storage System (ESS) participates in the secondary frequency regulation, the traditional control strategy generally adopts the simplified first-order inertia model, and the power allocated to each energy storage unit follows the principle of equal distribution. Therefore, it is impossible to consider the inconsistency of each internal unit for a long time, increasing capacity loss of the energy storage system, and the difficulty in improving the frequency regulation effect. This paper proposes an optimal control strategy based on the ESS unit loss and dynamic model. Firstly, we established the dynamic variable-parameter model of lithium batteries and gave the capacity loss of the Li-cell model. And then on this basis, we deduced the capacity loss of each unit to apply in the power system. Secondly, the requirements of Automatic Generation Control (AGC) signals to the differences between generator unit and ESS units are analyzed, and the output constraints of generator units and ESS are given, and the weight coefficients are determined by using the fuzzy control theory. Finally, a double-level control strategy considering frequency deviation rate and the capacity loss rate is formed, and the implementation process is given. The capacity loss of each ESS unit is 16.19% lower than that of the traditional method, and the frequency deviation of the regional power grid is effectively improved by more than 23.07%. The control strategy of this paper is of great significance for the design of a large-scale ESS control system.

Event-based fuzzy adaptive decentralised asymptotic tracking control for large-scale fractional-order nonlinear systems with strong interconnections

An adaptive fuzzy event-triggered asymptotic tracking control scheme is investigated for large-scale fractional-order nonlinear systems with unknown strong interconnections in this article. First, intelligent estimation technologies are employed to deal with interconnection terms and unknown nonlinear functions, which can largely relax some additional assumptions regarding the interconnections and eliminate the impacts of uncertainties. Second, by introducing dynamic surface control technology, the problem of ‘explosion of complexity’ is solved. Moreover, a novel event-triggered mechanism is proposed to save communication resources. The system stability and the asymptotic tracking performance are proved by utilising fractional Lyapunov function theory. Finally, a simulation example verifies the effectiveness of the proposed control method.

Digital Twin-Driven Performance Optimization for Hazardous Waste Landfill Systems

Hazardous waste landfill is the final way to dispose of hazardous waste and its environmental risk is different from that of domestic waste landfill. Its dangerous characteristics will not decrease with time, but will exist for a long. The environmental risks of hazardous waste landfills are concentrated in the probability risk of leakage of the impermeable layer. Therefore, it is of great significance to calculate the environmental risk of the leakage source intensity of typical hazardous waste landfills. In order to adapt to the personalized customer needs caused by social and economic development and to pursue new competitive advantages, the production mode of manufacturing enterprises is gradually changing to a small-batch and multi-variety customized mode. The complex and dynamic production environment caused by determinism puts forward higher requirements for the operation control accuracy of large-scale multiunit production systems. To support the efficient and coordinated operation of each link is a key challenge for process control of large-scale multiunit production logistics systems. This article proposes to accelerate the integration of information technology and manufacturing technology to promote the application of intelligent manufacturing process, intelligent logistics management, and other technologies in the production process. The research object is to study the multiunit linkage optimization problem under the framework of digital twin technology, through the real-time collaborative decision-making and feedback execution of the production, transportation, and warehousing multi-decision links to solve the “production-transportation-storage” decision-making in a dynamic environment. We analyzed the problems brought about by the dynamic interference of the execution process on the “production-transportation-storage” linkage operation process of the industrial park and analyzed the challenges of realizing the online linkage decision-making of “production-transportation-storage.” Based on the classic digital twin framework, a cross-unit digital twin linkage decision information architecture is proposed. Compared with the conclusions of the original environmental impact assessment report of the project, the post-environmental impact assessment results basically conform to the relevant descriptions, and a few nonconformities have been resolved during the project operation. The environmental protection measures of this project are relatively complete, which can meet various environmental protection requirements and enterprise development needs during the operation period of the project. For some environmental risks found in the process of project operation, evasion measures are proposed to reduce the possibility of environmental pollution accidents, and emergency measures can be taken in time when accidents occur. The practice of post-assessment of the environmental impact of this hazardous waste landfill is generally successful, and it has certain reference significance for the subsequent post-assessment of the environmental impact of other hazardous waste landfills.

Adaptive output feedback control of large-scale nonlinear time-delay systems with uncertain output equations

For a class of large-scale nonlinear time-delay systems with uncertain output equations, the problem of global state asymptotic regulation is addressed by output feedback. The class of systems under consideration are subject to feedforward growth conditions with unknown growth rate and time delays in inputs and outputs. To deal with the system uncertainties and the unknown delays, a novel low-gain observer with adaptive gain is firstly proposed; next, an adaptive output feedback delay-free controller is constructed by combining Lyapunov-Krasovskii functional with backstepping algorithm. Compared with the existing results, the controllers proposed are capable of handling both the uncertain output functions and the unknown time delays in inputs and outputs. With the help of dynamic scaling technique, it is shown that the closed-loop states converge asymptotically to zero, while the adaptive gain is bounded globally. Finally, the effectiveness of our control schemes are illustrated by three examples.

Adaptive neural smooth finite-time controller for large-scale stochastic nonlinear state-constrained systems with feasibility removal and time delays

This paper studies the adaptive smooth finite-time neural controller design for large-scale stochastic nonlinear systems subject to command filter and time-delay interactions. The main features are (1) the communication interconnection has the output delay between subsystems; (2) the smooth finite time strategy is given for stochastic disturbances; (3) all states are confined in the constrained space without feasibility conditions. First, the state time-delay interactions are tackled by the appropriate establishment of Lyapunov–Krasovskii functionals. Second, for full-state constraints, state-related mappings are employed to remove the feasibility. Then, by constructing the switching virtual control signals, the smooth fast finite-time feature is attached to the controller. Finally, based on finite-time stability theory in probability, command filter, and backstepping technique, the smooth fast finite-time neural controller is constructed to make that all system states converge in finite time, all closed-loop variables are bounded in probability, and full-state constraints are satisfied. Simulation results are provided to test the correctness of the method.

An improved iterative solution for cooperative distributed MPC

This note proposes an improved iterative solution of the cooperative distributed model predictive control for large-scale linear systems with interconnected dynamics. In particular, for the purpose of improving the convergence rate, the iteration error is integrated into the iteration process, based on which an algorithm is proposed to optimize the gain matrix of the iteration error. The effectiveness of the proposed approach is verified through the numerical simulation.

Small Signal Stability Analysis and Optimize Control of Large-Scale Wind Power Collection System

The frequency security problem of power system is highlighted as wind power penetration increases yearly, the eigenvalue analysis method based on the deterministic model is difficult to accurately evaluate the small signal stability of power system. The Weibull probability distribution is used to describe the uncertainty of wind speed. In this paper, the shape and scale parameters of the Weibull distribution are calculated by using the maximum likelihood approach and combining with the measured data, and the stability of small signal with large-scale wind power access is analyzed by the probability distribution method. Meanwhile, considering that the lack of inertia of the system caused by high-penetration wind power access can easily lead to the problem of frequency stability, it is proposed to take the maximum damping ratio of regional oscillation mode as optimization objective, virtual inertia and pitch angle control parameters as variables, and frequency stability as constraints, an small signal stability optimization model of wind power access to power system is established according to the proposed probabilistic small signal stability model. The sensitivity algorithm is used to obtain the optimal solution for the virtual inertia and pitch angle control parameters, and the validity of the proposed method and model is verified by two-area four-machine examples. Simulation results show that by optimizing the virtual inertia and pitch angle parameters of wind turbines, the small signal stability of the high-penetration wind power access system can be improved under the condition of ensuring sufficient inertia of the system, which provides a certain theoretical basis for the safe and reliable operation of wind power large-scale cluster access power system.

Worst-case control energy of single-input network systems

The controllability of large-scale network systems has been extensively investigated in the past few years. In spite of the recent advances in this field, there are still a number of unsolved problems which are of critical importance to fully understand the practical limitations arising in the control of large-scale networks. These include the derivation of informative bounds and scaling laws for the control energy of networks controlled by a limited number of nodes. In this paper, we aim to fill this gap by establishing new numerically reliable bounds and asymptotic estimates on the worst-case control energy of continuous-time linear network systems controlled by a single node. Our results rely on a convenient reformulation of the controllability Gramian of a single-input linear system in terms of a Cauchy matrix. We illustrate and validate our theoretical findings through several examples, ranging from structured networks to random ones.

Asynchronously Intermittent Decentralized Control of Large-Scale Systems

This letter develops an asynchronously intermittent decentralized control for large-scale systems. Different from the existing works on intermittent decentralized control for large-scale systems where the controllers of all subsystems are activated or not activated simultaneously, in this letter, each subsystem possesses its own work and rest time. We design an auxiliary timer for each subsystem to make a compromise between control work intervals and control rest intervals such that the derivative of constructed vertex-Lyapunov functions (ignoring the interconnections) is negative all the time. Then we use the graph-theoretic technique to deal with the cross terms. A novel sufficient criterion is obtained to guarantee the exponential stability of the closed-loop system. Simultaneously, the control gain matrices are also designed according to the feasibility of some linear matrix inequalities (LMIs). Finally, two examples of coupled inverted pendulums illustrate the validity of our results.

Global decentralized control for uncertain large-scale feedforward nonlinear time-delay systems via output feedback

Abstract In this paper, the problem of global decentralized output feedback control is addressed for a class of large-scale nonlinear systems with zero dynamics and unknown time-varying delay. System disturbance nonlinearities are subject to feedforward growth restrictions with unknown growth rate. In the spirit of dynamic scaling change technique, a novel pair of time-varying-gain observer and controller is proposed. Compared with the existing results, the controller proposed is capable of handling the difficulties caused by the zero-dynamics, the uncertainties and the unknown time-varying delay. With the help of the Razumikhin theorem, it is shown that the closed-loop states asymptotically converge to zero. Finally, the effectiveness of the proposed control scheme is illustrated by a numerical example.

Observer-based decentralized adaptive control for large-scale nonlinear systems with mixed dynamic interaction

The problem of decentralized adaptive control is investigated for a class of large-scale nonstrict-feedback nonlinear systems subject to dynamic interaction and unmeasurable states, where the dynamic interaction is related to both input and output items. First, the fuzzy logic system is utilized to tackle unknown nonlinear function with nonstrict-feedback structure. Then, by combining adaptive and backstepping technology, the proper output feedback controller is designed. Meanwhile, a fuzzy state observer is proposed to estimate the unmeasurable states. The proposed controller could guarantee that all the signals of the resulting closed-loop systems are bounded. Finally, the applicability of the proposed controller is well carried out by a simulation example.

Protocol-Based and Hybrid Access Control for the IoT: Approaches and Research Opportunities.

Internet of Things (IoT) applications and services are becoming more prevalent in our everyday life. However, such an interconnected network of intelligent physical entities needs appropriate security to sensitive information. That said, the need for proper authentication and authorization is paramount. Access control is in the front line of such mechanisms. Access control determines the use of resources only to the specified and authorized users based on appropriate policy enforcement. IoT demands more sophisticated access control in terms of its usability and efficiency in protecting sensitive information. This conveys the need for access control to serve system-specific requirements and be flexibly combined with other access control approaches. In this paper, we discuss the potential for employing protocol-based and hybrid access control for IoT systems and examine how that can overcome the limitations of traditional access control mechanisms. We also focus on the key benefits and constraints of this integration. Our work further enhances the need to build hierarchical access control for large-scale IoT systems (e.g., Industrial IoT (IIoT) settings) with protocol-based and hybrid access control approaches. We, moreover, list the associated open issues to make such approaches efficient for access control in large-scale IoT systems.

Guaranteed cost finite-time control of large-scale singular systems with interconnected delays

The guaranteed cost finite-time control problem of large-scale singular systems subjected to interconnected state delays is addressed in this article. A singular value decomposition approach combining with the Lyapunov function method is proposed to study the problem. Based on the method, delay-dependent sufficient conditions are established to design guaranteed cost controllers, which are presented in terms of tractable linear matrix inequalities. An example with simulation is given to demonstrate the validity and effectiveness of the theoretical results.

Design of Controller for Large-Scale Uncertain Systems Adopting Affine Arithmetic

In this article presents a new controller design technique for large scale uncertain systems. The new controller is designed through a reduced order system from certain high order system. In the projected reduction method, the numerator coefficients are obtained with γ-table while the denominator polynomial is achieved with δ - table. An optimised reduced order model is derived with minimum value of ISE. In this proposed method gives better stability of the reduced order model, if consider the original stable high order system. A PID controller is designed for the high order original systems through its proposed reducer order model. Consider several numerical examples are available in the literatureto illustrate the usefulness of the proposed method andit gives sufficientoutcome.