Control Of Large-scale Systems Research Articles

New Architecture Design of Magnetic Field Monitoring System Based on HIAF

The High-Intensity Heavy Ion Accelerator Facility (HIAF) is one of China's major scientific and technological infrastructure projects, aimed at providing an internationally leading research platform for nuclear physics, nuclear astrophysics, nuclear energy development, nuclear safety, and materials science. During the operation of HIAF, the high-precision measurement and control of magnetic fields are crucial for the stable operation of core equipment. To meet the high demands of HIAF for precision, real-time performance, and system autonomy in magnetic field measurement, this research designs and implements a high-precision magnetic field measurement system based on the LACCS (Large-scale Accelerator Cluster Control System) architecture. The system integrates advanced hardware and software designs, enabling it to efficiently and accurately complete distributed magnetic field measurement tasks, thus providing technical support for the smooth operation of HIAF.

Adaptive Fixed-time tracking control for large-scale nonlinear systems based on improved simplified optimized backstepping strategy.

This paper investigates the optimal fixed-time tracking control problem for a class of nonstrict-feedback large-scale nonlinear systems with prescribed performance. In the process of optimal control design, the new critic and actor neural network updating laws are proposed by adopting the fixed-time technique and the simplified reinforcement learning algorithm, which both guarantee the simplified optimal control algorithm and accelerate the convergence rate. Furthermore, the prescribed performance method is contemplated simultaneously, which ensures tracking errors can converge within the prescribed performance bounds in fixed time. The minimum parameter method is utilized to reduce the number of parameters designed in the adaptive laws for large-scale systems. Meanwhile, the proposed control strategy can guarantee that all closed-loop signals are bounded within a fixed time interval. Finally, simulation examples are provided to validate the effectiveness of the proposed control strategy.

Adaptive decentralized control for second-order large-scale nonlinear systems via fully actuated system approach

Adaptive decentralized control for second-order large-scale nonlinear systems via fully actuated system approach

Predefined-time adaptive neural network decentralized control for large-scale interconnected systems with input hysteresis.

This study endeavors to develop a predefined-time adaptive neural network decentralized controller for large-scale interconnected nonlinear systems with input hysteresis. Within the framework of the backstepping technique, the proposed control scheme guarantees that the tracking error converges to a small bounded set within a predefined settling time. The upper limit of this convergence time is determined by a single adjustable control parameter. Modified command filter not only tackles the inherent "complexity explosion" issue in traditional backstepping methods but also effectively avoids chattering phenomena possibly induced by sign function. An online approximator based on neural networks is utilized to address system uncertainties. Moreover, a novel predefined-time error compensation mechanism is constructed to compensate for the reduction in control accuracy caused by filtering errors. Two simulation case studies demonstrate the feasibility and effectiveness of the proposed control method.

Controllability of non-instantaneous impulsive large-scale neutral fractional stochastic systems with Poisson jumps

Controllability of non-instantaneous impulsive large-scale neutral fractional stochastic systems with Poisson jumps

Decentralized state feedback guaranteed cost control for conformable fractional-order nonlinear large-scale systems

Decentralized state feedback guaranteed cost control for conformable fractional-order nonlinear large-scale systems

Optimal control of electric vehicle by Short-term Costate Estimation method of Pontryagin’s minimum principle

Pontryagin’s minimum principle (PMP) is a foundational theory to obtain optimal control of dynamic systems by the change of their state and cost function over time. Compared to programming-based optimization methods, it offers the advantage of providing rapid computation and an algebraic comprehension of the optimal solution. However, particularly for large-scale systems, constructing their costate dynamics and ensuring numerical stability is quite a difficult task. This study introduces a novel method called as the Short-term Costate Estimation method based on PMP (SCEMP). It aims to efficiently obtain optimal control for large-scale systems by employing an iterative algorithm cored by the PMP. The objective is to minimize energy consumption while a car is running, achieved by regulating the distribution ratio of traction torque between the front and rear wheels. The key concept of SCEMP lies in obtaining a discrete optimal control through the sequential process of forward integration of the state equation and backward integration of the costate equation within a pre-defined near-future time frame. Costate dynamics is formulated with a cost function evaluated within a surrogate model, which not only offers a simplified representation of the plant dynamics but also serves as a reference model for controlling the plant model. Verification is conducted within a simulation environment. The SCEMP achieved results that approached the global optimum obtained from Dynamic Programming (DP) within a margin of up to 1%, while requiring approximately one-tenth of the computation time needed by DP.

Decentralized event-trigger-based predefined-time adaptive control of large-scale nonlinear systems

Decentralized event-trigger-based predefined-time adaptive control of large-scale nonlinear systems

Harnessing artificial intelligence for coherent beam combination

Coherent beam combining (CBC) is an effective scheme to surpass the physical power limits of single fiber lasers, achieving higher power and superior beam quality, with phase control being the critical factor. Active phase control compensates for phase noise-induced coherence degradation by directly or indirectly detecting phase differences among sub-beams. Traditional phase control algorithms face challenges in large-scale CBC systems due to low control bandwidth. With the rapid development of artificial intelligence (AI) technologies, integrating advanced intelligent algorithms into active phase control systems holds promise for enhancing the performance of CBC systems. This paper begins with a brief introduction to the principles of traditional phase control algorithms, such as Stochastic Parallel Gradient Descent (SPGD) and locking of optical coherence by single-detector electronic-frequency tagging (LOCSET), elucidating why AI can assist in active phase control systems. Subsequently, we review recent advancements in phase control based on deep learning and reinforcement learning, concluding with a summary and future outlook. As phase control technology advances, the integration of AI and traditional algorithms will play a pivotal role in achieving high-bandwidth and accurate phase control in large-scale CBC systems.

Optimization of chemical engineering control for large-scale alkaline electrolysis systems based on renewable energy sources

In the context of renewable energy, dynamic control is crucial for large-scale alkaline water electrolysis systems. Under traditional Proportional-Integral-Derivative (PID) control, the system's temperature and impurity levels may exceed safe limits during sudden load changes, compromising system safety. Therefore, this study proposes a control strategy based on mathematical models of system temperature and impurities, including Model Predictive Control (MPC) for temperature and Optimal Curve Tracking (OCT) for impurities. The MPC controller offsets the disturbances in temperature caused by current fluctuations through state prediction, while the OCT controller ensures that impurity levels remain within safe limits. Real-time application of these control strategies is achieved through MATLAB-PLC communication, validating controller performance. Experimental results show that the MPC controller exhibits excellent dynamic response and stability in controlling the stack temperature, with a maximum temperature peak deviation of only 2.9 K and a temperature fluctuation range of 5.9 K. This represents a reduction of 1.6 K in peak deviation compared to traditional PID controllers and significantly decreases the temperature fluctuation range. The OCT controller also demonstrates good safety and stability in controlling hydrogen to oxygen (HTO) impurity levels, consistently maintaining the HTO content at the set upper limit of 1.5%, thereby expanding the safe operational range of the system and reducing the need for frequent adjustments to the system setpoints.

Decentralized Event-Triggered Tracking Control for Unmatched Interconnected Systems via Particle Swarm Optimization-Based Adaptive Dynamic Programming.

The problem of the large-scale interconnected system (LSIS) control is prevalent in practical engineering and is becoming increasingly complex. In this article, we propose a novel decentralized event-triggered tracking control (ETTC) strategy for a class of continuous-time nonlinear LSIS with unmatched interconnected terms and asymmetric input constraints. First, auxiliary subsystems are established to address the unmatched cross-linking terms. Next, the dynamics states of the tracking error and the exosystem are combined to construct a nominal augmented subsystem. By employing a nonquadratic performance function, the input-constrained decentralized tracking control problem is transformed into an optimal control problem for the nominal augmented subsystem. A group of independent parameters and event-triggered conditions are designed to save communication bandwidth and computational resources. Subsequently, the critic-only adaptive dynamic programming (ADP) method is used to solve the Hamilton-Jacobi-Bellman equation (HJBE) associated with the optimal control problem. To improve training success rate, the weights of the critic neural network (NN) are updated by introducing a particle swarm optimization algorithm (PSOA). The tracking error and the NN weights are proved to be uniformly ultimately bounded (UUB) under the proposed ETTC by using the Lyapunov extension theorem. Finally, the simulation example of an unmatched interconnected system is provided to verify the validity of the proposed decentralized method.

Data-based distributed model predictive control for large-scale systems

Data-based distributed model predictive control for large-scale systems

Robust decentralized controller design for highly Maneuverable Aircraft

Large-scale control systems often encounter challenges due to their intricate control structures, leading to delays and uncertainties when traditional centralized control methods are implemented in engineering applications. In response to these challenges and with the aim of enhancing control efficiency, the exploration of decentralized control approaches has become increasingly prominent. This paper presents two decentralized control strategies: fixed structure control theory, which is built upon the foundational principles of traditional robust controller design, and the weighted model approximation method, which integrates weighted model reduction with optimization strategies. The fixed structure control theory predetermines architecture of the controllers, significantly alleviating the computational burden associated with the development of robust controllers. Conversely, the weighted model approximation method proposes a more intuitive and succinct pathway for controller design and engineering application. The efficacy of these strategies is evaluated through their application to the Highly Maneuverable Aircraft (HIMAT) system. The results demonstrate that, the fixed structure control theory generally exhibits good performance. However, in certain engineering scenarios, the conclusions derived from the weighted model approximation method for decentralized controller design are easier to comprehend and facilitate rapid application.

A learning optimization for resilience enhancement of risk-informed traffic control system with hazardous materials transportation under uncertainty

A learning optimization is proposed to enhance overall resilience of interdependent traffic systems with hazardous materials (hazmat) transportation under uncertainty. To this end, three interconnected systems are proposed against traffic dynamics and congestion in a complex environment of regular traffic and hazmat carriers incurred with time-varying travel cost. In order to increase control resilience against traffic instability, a reinforcement learning hazmat network is proposed. In order to efficiently improve computation resilience against disruption of risk uncertainty, a resilience-aware learning optimization is proposed to obtain optimal solutions. In order to demonstrate computational performance of proposed approach, numerical experiments are performed at a real-world city under various kinds of traffic conditions. Computational comparisons are numerically made with stochastic and robust optimization at large-scale traffic control systems. As it reported, the proposed learning-based optimization can better greatly enhance overall traffic system resilience, control resilience and computation resilience compared to other models under mixed risk of hazmat transportation and uncertain demand.

Prediction-Based Control of Large-Scale Networked Control Systems

Prediction-Based Control of Large-Scale Networked Control Systems

Delayed feedback implementation of decentralized derivative-dependent control of large-scale systems with input delays and disturbed measurements

We study decentralized derivative-dependent control of large-scale nth-order systems with input delays via delayed feedback implementation. The unavailable derivatives can be approximated by finite differences giving rise to a time-delayed feedback. In the centralized case, an efficient simple linear matrix inequalities (LMIs)-based method for designing of such static output-feedback and its sampled-data implementation was recently suggested. In the present paper, we extend this design to large-scale systems in the presence of input delays and disturbed measurements. Under the assumption of the stabilizability of the system with small enough input delays and small enough interactions by a state-feedback that depends on the output and its derivatives, a delayed static output-feedback that stabilizes the system is presented by using the current and past disturbed measurements. To compensate the errors due to the input delays, we add the appropriate terms to the corresponding Lyapunov–Krasovskii functional that lead to LMIs conditions. The efficient bounds on the delays preserving that the resulting system is input-to-state stable (ISS) are found by verifying the LMIs. In addition, we employ the vector Lyapunov functional method that may allow larger couplings compared with the existing method. Finally, the effectiveness of the proposed methods is illustrated by numerical examples.

Decentralized Cluster-Based Distributed Secondary Control of Large-Scale DC Microgrid Cluster System

Decentralized Cluster-Based Distributed Secondary Control of Large-Scale DC Microgrid Cluster System

A Semi-Global Finite-Time Decentralized Control Method for High-Order Large-Scale Nonlinear Systems

This study focuses on the decentralized stabilization issue of high-order large-scale nonlinear systems with unknown disturbances. A novel decentralized semi-global finite-time control approach is suggested by constructing a Lyapunov function with both quadratic and higher-order components and employing the method of homogeneous domination. Based on the designed Lyapunov function, a state-feedback controller is constructed for the nominal system. Subsequently, the scaling gain is flexibly introduced to enable semi-globally finite-time stabilization of the nonlinear system. Besides, the approach is extended to the problem of decentralized tracking control of high-order large-scale nonlinear systems. Finally, numerical and practical examples validate the effectiveness of the presented control strategy.

Towards intelligent emergency control for large-scale power systems: Convergence of learning, physics, computing and control

This paper has delved into the pressing need for intelligent emergency control in large-scale power systems, which are experiencing significant transformations and are operating closer to their limits with more uncertainties. Learning-based control methods are promising and have shown effectiveness for intelligent power system control. However, when they are applied to large-scale power systems, there are multifaceted challenges such as scalability, adaptiveness, and security posed by the complex power system landscape, which demand comprehensive solutions. The paper first proposes and instantiates a convergence framework for integrating power systems physics, machine learning, advanced computing, and grid control to realize intelligent grid control at a large scale. Our developed methods and platform based on the convergence framework have been applied to a large (more than 3000 buses) Texas power system, and tested with 56000 scenarios. Our work achieved a 26% reduction in load shedding on average and outperformed existing rule-based control in 99.7% of the test scenarios. The results demonstrated the potential of the proposed convergence framework and DRL-based intelligent control for the future grid.

Design of SIL-2 interlock function for tokamak fusion reactor

The ITER Gas Injection System (GIS) Regulates fueling and impurity gases during plasma discharges within the tokamak vacuum vessel. To implement ITER investment protection, the GIS requires capable of interlock function to shielding the gases from ITER vacuum chamber when Disruption Mitigation System (DMS) is triggered or an abnormality is detected, in order to prevent further damage caused by gas fueling. This paper presents the preliminary design of the interlock system for the ITER GIS per ITER plant interlock system design rules and guidelines. The design adopts a redundant and fault-tolerant architecture with fail-safe programmable logic controllers to achieve the required ITERs target Interlock Integrity Level of SIL-2. The solutions here facilitate the integration with ITER instrumentation and control system, also establishing reference models for other large-scale control system platforms.