Optimal Control Performance Research Articles

Optimal Algorithm for Shared Network Communication Bandwidth in IoT Applications

In recent years, a variety of wired and wireless network communication protocols in the field of industrial control have become increasingly mature. The purpose of this paper is to provide a Shared network communication bandwidth optimization management algorithm for large-scale industrial networked control systems in Internet of things applications. This algorithm is based on the generalized geometric convex optimization method and can realize the optimal allocation of Shared network communication bandwidth resources. L2 networked control systems is used in this paper for the establishment of various numerical relations between the control performance and the communication network parameters. Based on the generalized geometric convex optimization method for the numerical relationship between convex analysis and fitting, convexity, and with the convex analysis and the numerical relationship between convexity fitting as constraint conditions, the results of integrity for networked control systems with large-scale resource allocation target will share the optimal management of network resources as a generalized geometric convex optimization problem. Using convex optimization software package for optimizing the optimal global solution of management problem, i. e. the optimal allocation of resources, the algorithm realizes the stability of each networked control system and achieve optimal L2 control performance. It is concluded that the predetermined transmission rate between the network node one and network node two, the data flow information sent by the network node two to the network node one is read, the delay time and packet loss rate between the two nodes are determined, the delay time is reduced by about 8 seconds, and the packet loss rate is greatly reduced by 78%.

Multi-objective operation optimization of regional integrated energy system based on NSGA-II algorithm

With the deepening of China’s energy market reform and the promotion of integrated energy services, the regional integrated energy system becomes an important development direction of energy supply system. In order to maximize the economic efficiency and reduce the air pollutant emission of the regional integrated energy system, the distributed power generation module and the cooling-heat-power (CCHP) triple-supply module are formed into a model, and the power balance, equipment capacity and environmental factors of the system are constrained with the objective function of minimizing the daily operation cost of the system as well as minimizing the air pollutant emission. Based on the mathematical system framework model and the optimal operation control strategy, the NSGA-II algorithm is used to solve the multi-objective programming model to obtain the Pareto solution set, and the hourly output of the optimal operation of the system equipment with both economic and environmental benefits is obtained. The results show that the daily operating costs and pollutant emissions of the district energy system are significantly reduced compared with those without optimization, which effectively solves the problems of low operating efficiency and serious environmental pollution of the district energy system and achieves the optimal operation with both economic and environmental benefits.

Transferring Online Reinforcement Learning for Electric Motor Control From Simulation to Real-World Experiments

Reinforcement learning (RL) based methods are an upcoming approach for the control of power systems such as electric drives. These data-driven techniques do not need an explicit plant model like most common state-of-the-art approaches. Instead, the control policy is continuously improved solely based on measurement feedback pursuing optimal control performance through learning. While the general feasibility of RL-based drive control algorithms has already been proven in simulation, this work focuses on transferring the methodology to real-world experiments. In the case of electric motor control, a strict real-time requirement, safety constraints, system delays and the limitations of embedded hardware frameworks are hurdles to overcome. Hence, several modifications to the general RL training setup are introduced in order to enable RL in real-world electric drive control problems. In particular, a rapid control prototyping toolchain is introduced allowing fast and flexible testing of arbitrary RL algorithms. This simulation-to-experiment pipeline is considered an important intermediate step towards introducing RL in embedded control for power electronic systems. To highlight the potential of RL-based drive control, extensive experimental investigations addressing the current control of a permanent magnet synchronous motor utilizing a deep deterministic policy gradient algorithm have been conducted. Despite the early state of research in this domain, promising control performance could be achieved.

Economic Model Predictive Control for optimal struvite recovery

Resource recovery from municipal wastewater has been a prime focus for a decade. Although several recovery processes already exist in the market today, the high cost of material, inherent disturbance in the influent quality, lack of real time monitoring of critical parameters, and lack of a robust automation system may result in suboptimal performance. This work attempts to construct a model based predictive control for optimal operation of a struvite recovery unit in a full scale WRRF. A multi-parameter based predictive control has been developed by implementing an Economic Model Predictive Controller (EMPC) for optimal dosing of magnesium hydroxide in a struvite recovery unit. The EMPC used customized objective function for real-time optimization of performance and economical parameters of the crystallization unit. The effectiveness of the proposed EMPC controller is verified through tests conducted on the Benchmark Simulation Model No. 2 (BSM2d.). The results obtained from the simulator-based evaluation of EMPC demonstrate a significant improvement in resource recovery at reduced operational costs. The economic advantages of implementing an EMPC compared to proportional and constant magnesium dosage has also been enumerated.

Economic model predictive control for robust optimal operation of sparse storage networks

A tube-based robust economic model predictive control scheme is proposed for sparse, linear storage network systems under bounded disturbances. We present a modeling framework that reflects conservation principles, and for which a centralized controller optimizes a performance metric for the entire network over a finite time horizon. Our main result is a computationally efficient projection-based approach that exploits the sparse structure of the system dynamics for tightening state and input constraints, in the meantime ensuring that average performance, recursive feasibility, and asymptotic stability of the system are maintained. An inventory control problem under demand uncertainty is presented to illustrate the advantages of the proposed framework and benchmark against nominal economic model predictive control.

Dynamic Tuning of the Controller Parameters in a Two-Area Multi-Source Power System for Optimal Load Frequency Control Performance

Frequency control is becoming increasingly critical today due to the growing size and changing structure of complex interconnected power networks. Scaling up economic pressures for efficiency and reliability of the power system has necessitated a requirement for maintaining system frequency, and tie-line power flows as close as possible to scheduled values. High-frequency deviations may degrade load performance, damage equipment, resulting in overloading of transmission lines, which may interfere with system protection schemes, and, finally, may also result in an unstable condition of the power system. More so, Load Frequency Control ( LFC) plays a vital role in the modern power system as an auxiliary service to support power exchanges and, at the same time, to provide better conditions for the trading of electricity. Therefore, the tuning of the dynamic controller (i.e., net frequency and net power interchange errors) is a significant factor in achieving optimum LFC performance. Appropriate tuning of the controller parameters is required in order to achieve excellent control action. In view of this, this paper introduces the dynamic tuning of controller parameters in a two-area multi-source power system with an AC-DC parallel tie line for optimum load-frequency control performance. Matlab/Simulink software is used to realize the system simulation. System dynamic performance is observed for conventional PID tuning by the Ziegler Nichols method and the Kitamori method, fuzzy logic controllers, fuzzy-logic PID controllers, fuzzy PID controllers, and polar-Fuzzy controllers. Furthermore, the frequency and tie-line power response of the interconnected areas were compared based on the setting-time, peak-overshoot, and peak-undershoot. The simulation results show that the responses of the fuzzy-based controllers are faster than those of the classical controllers, resulting in minimized frequency and tie-line power deviations.

Predictive Control of Networked Nonlinear Multiagent Systems With Communication Constraints

This paper investigates the control problem of networked nonlinear multiagent systems with communication constraints and unknown dynamics, based on system input–output data. A cost function for the design of networked nonlinear multiagent control systems is presented, which considers not only the coordination performance between agents but also the performance of individual agents. Utilizing a data model to represent discrete-time nonlinear multiagent systems, a networked data-driven predictive control scheme is proposed to optimize control performance, compensate for communication constrains, and achieve both consensus and stability of networked nonlinear multiagent systems. An optimal control protocol is derived, which minimizes the cost function and makes compensation for communication constraints. The criteria of achieving both consensus and stability of networked nonlinear multiagent systems are provided. An example illustrates the performance of the proposed networked data-driven predictive control scheme.

低コスト版個別分散型空調システム最適制御手法の開発 （第２報）冬期における制御システムの実証試験結果の考察について

低コスト版個別分散型空調システム最適制御手法の開発 （第２報）冬期における制御システムの実証試験結果の考察について

Robust Optimal Control of High-Speed Permanent-Magnet Synchronous Motor Drives via Self-Constructing Fuzzy Wavelet Neural Network

This article presents the design of an adaptive backstepping robust optimal control (ABROC) approach for achieving performance with high dynamic of high-speed micro permanent-magnet synchronous motor (HS-MPMSM) drive. First, a backstepping controller is designed for stabilizing the HS-MPMSM drive. To enhance the performance of the control system against external disturbances and parameter variations, an adaptive backstepping robust controller (ABRC) is developed. The ABRC combines a backstepping controller, an adaptive self-constructing fuzzy wavelet neural network (SCFWNN) identifier, and a robust controller. The proposed identifier is developed to approximate the nonlinear functions online. Furthermore, the robust controller is designed to recover the SCFWNN approximation errors. As the online adaptive control laws are derived via Lyapunov theory, thus, the ABRC stability is assured. To attain the optimal control performance, an infinite horizon optimal controller using a critic neural-network (NN) is developed and combined with ABRC to construct the ABROC approach. The critic NN is developed to approximate the optimal value function of the Hamilton–Jacobi–Bellman equation, which is used to develop the optimal controller. The experimental results are presented to verify the effectiveness of the proposed approach. The results validate that the ABROC approach is robust against parameter uncertainties and external disturbances.

Control and soft sensing strategies for a wastewater treatment plant using a neuro-genetic approach

During the last years, machine learning-based control and optimization systems are playing an important role in the operation of wastewater treatment plants in terms of reduced operational costs and improved effluent quality. In this paper, a machine learning-based control strategy is proposed for optimizing both the consumption and the number of regulation violations of a biological wastewater treatment plant. The methodology proposed in this study uses neural networks as a soft-sensor for on-line prediction of the effluent quality and as an identification model of the plant dynamics, all under a neuro-genetic optimum model-based control approach. The complete scheme was tested on a simulation model of the activated sludge process of a large-scale municipal wastewater treatment plant running under the GPS-X simulation frame and validated with operational gathered data, showing optimal control performance by minimizing operational costs while satisfying the effluent requirements, thus reducing the investment in expensive sensor devices.

A memristor-based hybrid analog-digital computing platform for mobile robotics.

Algorithms for mobile robotic systems are generally implemented on purely digital computing platforms. Developing alternative computational platforms may lead to more energy-efficient and responsive mobile robotics. Here, we report a hybrid analog-digital computing platform enabled by memristors on a mobile inverted pendulum robot. Our mobile robotic system can tune the conductance states of memristors adaptively using a model-free optimization method to achieve optimal control performance. We implement sensor fusion and the motion control algorithms on our hybrid analog-digital computing platform and demonstrate more than one order of magnitude enhancement of speed and energy efficiency over traditional digital platforms.

Classification of operation cases in electric arc welding wachine by using deep convolutional neural networks

Electric arc welding machines are widely used in industry in metal technology. In parallel with the advancement of technology for the development and automation of electric arc welding machines, it is necessary to conduct scientific studies on the determination of optimal operation cases and control for optimal welding process. In this study, operating zones were classified and determined according to the measured welding current graph during the 5-s operation of the MAG electric arc welding machine. Five deep convolutional neural networks were used for this purpose. Four of these deep learning methods are pre-trained models. We used the concept of “ransfer learning” to use pre-trained models. According to the results we obtained from five different models, we were able to estimate the operating range of the electric arc welding machine, with 93.5% accuracy with the designed model and 95−100% accuracy with pre-trained models.

A Matlab/Simulink Simulation Small Signal Stability of Single-Machine Infinite Bus Using Optimal Control Based on Load Cluster Patterns

Matlab/Simulink is sophisticated software that has been facilitated by MathWorks Inc. This device is increasingly being used in various fields of research. Likewise, it has great potential in the field of power system simulation. This paper presents a simulation of the optimal performance of the power generation system due to changes in load consumption. Small signal stability due to changes in electrical power usage at the load center is overcome by applying a load cluster pattern. The main objective of this research is to achieve control in a power generation system that is responsive and able to maintain stability in all operating conditions at the load center. Simulation results show the performance of optimal control of the power generation system in each load cluster. Contributions to improve the stability of the power plant system performance by 28.03 percent for frequency (F), 23.03 percent for voltage (V), and 29.5 percent for electric power (P).

Exploring Edge Computing for Multitier Industrial Control

Industrial automation traditionally relies on local controllers implemented on microcontrollers or programmable logic controllers. With the emergence of edge computing, however, industrial automation evolves into a distributed two-tier computing architecture comprising local controllers and edge servers that communicate over wireless networks. Compared to local controllers, edge servers provide larger computing capacity at the cost of data loss over wireless networks. This article presents switching multitier control (SMC) to exploit edge computing for industrial control. SMC dynamically optimizes control performance by switching between local and edge controllers in response to changing network conditions. SMC employs a data-driven approach to derive switching policies based on classification models trained based on simulations while guaranteeing system stability based on an extended Simplex approach tailored for two-tier platforms. To evaluate the performance of industrial control over edge computing platforms, we have developed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">WCPS-EC</i> , a real-time hybrid simulator that integrates simulated plants, real computing platforms, and real or simulated wireless networks. In a case study of an industrial robotic control system, SMC significantly outperformed both a local controller and an edge controller in face of varying data loss in a wireless network.

Optimal operation and tracking control of vapor‐recompressed batch distillation

AbstractVapor recompressed batch distillation (VRBD) is an energy‐integrated configuration which works on the principle of a heat pump. Operation of such a column is challenging due to unsteady, nonlinear dynamics and strong interplay between separation and energy efficiency. In this paper, a two‐step approach is proposed for optimal operation and control of such a column. Initially, an openloop optimal operation policy is generated for maximization of an overall performance index using offline optimization. To this end, three performance indices are proposed to capture interplay between separation and energy efficiency. Subsequently, a model‐based output feedback controller is designed to track this optimal performance trajectory. The effectiveness of the proposed approach is demonstrated using a benzene‐toluene separation case study wherein it is shown that the proposed approach helps to achieve optimal operation in the presence of operational disturbances.

Distributed controller design and performance optimization for discrete‐time linear systems

SummaryThis article addresses the problem of distributed controller design for linear discrete‐time systems. The problem is posed using the classical framework of state feedback gain optimization over an infinite‐horizon quadratic cost, with an additional sparsity constraint on the gain matrix to model the distributed nature of the controller. An equivalent formulation is derived that consists in the optimization of the steady‐state solution of a matrix difference equation, and two algorithms for distributed gain computation are proposed based on it. The first method consists in a step‐by‐step optimization of said difference matrix equation, and allows for fast computation of stabilizing state feedback gains. The second algorithm optimizes the same matrix equation over a finite time window to approximate asymptotic behavior and thus minimize the infinite‐horizon quadratic cost. To assess the performance of the proposed solutions, simulation results are presented for the problem of distributed control of a quadruple‐tank process, as well as a version of that problem scaled up to 40 interconnected tanks.

Research on data-driven identification and prediction of heat response time of urban centralized heating system

Heat response time (HRT) is one of the key dynamic response characteristics of urban centralized heating system (UCHS). HRT is also critical to the operation dispatch of large-scale UCHS. This study proposes a data-driven approach to identify and predict HRT. Due to the diversity of operational data, this study applies correlation analysis and feature fusion (both linear and nonlinear) to generate feature sets. This study further develops the prediction models of HRT made up of three different feature sets and four machine learning models: Linear Regression (LR), Support Vector Machine Regression (SVR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost). A UCHS in Zhengzhou is selected as the demo site to show the effectiveness of this approach. HRT of investigated substations range from 1000 to 3000 s. The correlation analysis indicates that the heating area’s square root is most relevant to HRT (correlation coefficient close to 0.68) compared to other features. Feature fusion is critical in HRT analysis. The performances of all four prediction models are improved with fused features added to sets. XGBoost model outperforms other models in terms of model accuracy. Quantification of HRT could be useful in the optimized operation control of large-scale UCHS.

Advanced optimization of gas turbine aero-engine transient performance using linkage-learning genetic algorithm: Part II, optimization in flight mission and controller gains correlation development

Part Ⅰ has illustrated the procedures to apply the Linkage Learning Genetic Algorithm (LLGA) in Gas Turbine Engine (GTE) controller gains tuning and generated the optimization results for runway conditions from idle to takeoff. However, the total pressure and temperature of the engine inlet vary as the changing of altitude and Mach number, which would lead to the variation in fuel flow supply regulation. As a result, the optimized gains in runway might not be suitable for other flight conditions. In order to maintain the optimal control performance, the GTE controller gains should be adjusted according to the flight conditions. This paper extends the application of the LLGA method to other flight conditions and then simulates a complete flight mission with different gains and weather condition configurations. For this purpose, the control parameters in the Simulink model of the GTE controller are first corrected by the weather condition in altitude. Then, a typical flight mission is defined and divided into different flight segments based on the altitude and Mach number configuration. One representative point is selected from each segment as the datum point for optimization process. After this step, the LLGA method is used to find the best gains combinations for different flight conditions and the differences in optimization effects for different flight conditions are analyzed subsequently. The simulation results show that the optimization effect of the control performance of each flight condition is dependent on the value of √θδ and the optimal Kpla in some flight conditions is approximately equal to √θδ times of the Kpla value in sea level standard condition. Finally, the complete flight mission is simulated with different gains and weather condition configurations. The simulation results show that the engine performance has been greatly improved after optimization by LLGA in the transient state and the high altitude conditions. In other steady states, the optimization effect is not very obvious.

Control Performance Optimization for Application Integration on Automotive Architectures

Automotive software implements different functionalities as multiple control applications sharing common platform resources. Although such applications are often developed independently, the control performance of the resulting system depends on how these applications are integrated. A key integration challenge is to efficiently schedule these applications on shared resources with minimal control performance degradation. We formulate this problem as that of scheduling multiple distributed periodic control tasks that communicate via messages with non-zero jitter. The optimization criterion used is a piecewise linear representation of the control performance degradation as a function of the end-to-end latency of the application. The three main contributions of this article are: 1) a constraint programming (CP) formulation to solve this integration problem optimally on time-triggered architectures; 2) an efficient heuristic called Flexi; and 3) an experimental evaluation of the scalability and efficiency of the proposed approaches. In contrast to the CP formulation, which for many real-life problems might have unacceptably long running times, Flexi returns nearly optimal results (0.5 percent loss in control performance compared to optimal) for most problems with more acceptable running times.

Effective approach to distributed optimal operation control in rural low voltage microgrids

Effective approach to distributed optimal operation control in rural low voltage microgrids