Optimal Control Research Articles

Efficient Productivity-Aware Control Parameter Optimization in Cutter Suction Dredger Construction Using Machine Learning with Parallel Global Search

This paper proposes an efficient productivity-aware optimization framework that utilizes hybrid machine learning with parallel global search to timely and appropriately adjust the critical control parameters (CCPs) of a cutter suction dredger (CSD) during construction. This optimization framework consists of three main parts. First, a hybrid Jaya–multilayer perceptron (MLP) algorithm was developed to rapidly construct a model that captures the interaction between construction parameters and slurry concentration. Next, the preliminary coarse results for the CCPs are determined through multi-parameter sensitivity analysis. Finally, the proposed resilient-zone parallel global search algorithm was employed to further optimize the CCPs, yielding more precise optimization results. To validate the proposed optimization framework and implement the in-situ service, it is applied to a real-world case study involving “Tianda” CSD construction. The results demonstrated that the average optimization duration is 6.7 s, which is shorter than the data acquisition interval of 8 s. Our approach improves the computational efficiency by 9.4 times compared with traditional optimization control methods. Additionally, there is a significant increase in the slurry concentration, with the maximum growth rate reaching 81.64%.

Distributed Optimization Control for the System with Second-Order Dynamic

No matter whether with constraint or without constraint, most of the research about distributed optimization is studied for the kind of quadratic performance criteria that does not have an integrator; these optimization problems only concern the state value at the time of the final state, not the whole process of the system change. For this problem, this paper discusses second-order multi-agent systems with a discrete-time dynamic and a continuous-time dynamic, respectively, for distributed optimization control problems, and proposes sufficient conditions to ensure the quadratic performance criteria with an integrator is positive. Specifically, under sufficient conditions, we describe the multi-agent systems that are considered in this paper to be connected topology; all the agents can obtain the information from their neighbors. In addition, the structure of our controller only relies on the Laplace matrix of the system’s topology, and the reaction coefficients in the controller are the parameters in the performance criteria. Finally, the analysis of convergence is given and verified by numerical examples and simulations.

Research on the role of multi-sensor system information fusion in improving hardware control accuracy of intelligent system

Abstract Multi-sensor management and control technology generally constructs a reasonable objective function to solve the optimal control command set to control a limited number of sensors to obtain higher quality measurement information, thus obtaining better target tracking performance. In the process of multi-sensor information fusion, there is not only the problem of information redundancy but also obvious time delay. A sensor fusion algorithm combined with global optimization algorithm is innovatively proposed. According to the key frames saved in the previous steps, feature points in local maps, sensor information, and loop information, a global optimization algorithm based on graph optimization model is constructed to optimize the position and pose of intelligent hardware system and the position of spatial feature points. Moreover, this work studies and experiments on multi-sensor fusion simultaneous localization and mapping (SLAM) comprehensively and systematically, and the experimental results show that the algorithm proposed in this work is superior to common open-source SLAM algorithm in positioning accuracy and mapping effect under special circumstances. Therefore, the method proposed in this work can be applied to intelligent driving of vehicles, vision-assisted movement of robots and intelligent control of unmanned aerial vehicles, thus effectively improving the hardware control accuracy of intelligent systems.

Reinforcement Learning Model-Based and Model-Free Paradigms for Optimal Control Problems in Power Systems: Comprehensive Review and Future Directions

This paper reviews recent works related to applications of reinforcement learning in power system optimal control problems. Based on an extensive analysis of works in the recent literature, we attempt to better understand the gap between reinforcement learning methods that rely on complete or incomplete information about the model dynamics and data-driven reinforcement learning approaches. More specifically we ask how such models change based on the application or the algorithm, what the currently open theoretical and numerical challenges are in each of the leading applications, and which reinforcement-based control strategies will rise in the following years. The reviewed research works are divided into “model-based” methods and “model-free” methods in order to highlight the current developments and trends within each of these two groups. The optimal control problems reviewed are energy markets, grid stability and control, energy management in buildings, electrical vehicles, and energy storage.

Chance-constrained stochastic optimal control of epidemic models: A fourth moment method-based reformulation

This work proposes a methodology for the reformulation of chance-constrained stochastic optimal control problems that ensures reliable uncertainty management of epidemic outbreaks. Specifically, the chance constraints are reformulated in terms of the first four moments of the stochastic state variables through the so-called fourth moment method for reliability. Moreover, a spectral technique is employed to obtain surrogate models of the stochastic state variables, which enables the efficient computation of the required statistics. The practical implementation of the proposed approach is demonstrated via the optimal control of two different stochastic mathematical models of the COVID-19 transmission. The numerical experiments confirm that, unlike those reformulations based on the Chebyshev–Cantelli’s inequality, the proposed method does not exhibit the undesired outcomes that are typically observed when a higher precision is required for the risk level associated to the given chance constraints.

Simulation-based adaptive optimization for passenger flow control measures at metro stations

Effective passenger flow control measures are essential for the safe operation of metro stations. Existing in-station control measures include adjusting the operation mode of escalators and setting up temporary fences. However, in practice, metro operators often adopt fixed operation modes during fixed periods, indicating that the current passenger flow control measures at metro stations are overly rigidified. Therefore, developing an adaptive control strategy to constantly balance the wildly fluctuating passenger flow and optimize the operation performance is a key issue in current research. In this study, transportation efficiency and congestion risk are selected as evaluation objectives for passenger transportation risk, and passenger flow feature, station structure, and passenger flow control measures are considered key influential factors. Subsequently, an adaptive optimization method integrating simulation and data interpolation is proposed. The software Legion is used to conduct 150 orthogonal simulations, and prediction models for passenger transportation risk are obtained by performing data interpolation on the simulation results. Finally, taking a certain metro station as a case study, the optimal passenger flow control strategy under any passenger flow composition is obtained by scenario acquisition, risk identification, and adaptive decision-making. The results show that setting up temporary fences can reduce the passenger density near the fare gates, while adjusting the running direction of escalators can reduce overcrowding on the platform. Under varying passenger flow composition, the optimal strategy for the current scenario can be obtained, controlling passenger transportation risk within an acceptable range and providing assistance for metro operators in decision-making.

Multi-agent distributed control of integrated process networks using an adaptive community detection approach

This paper focuses on developing an adaptive system decomposition approach for multi-agent distributed model predictive control (DMPC) of integrated process networks. The proposed system decomposition employs a refined spectral community detection method to construct an optimal distributed control framework based on the weighted graph representation of the state space process model. The resulting distributed architecture assigns controlled outputs and manipulated inputs to controller agents and delineates their interactions. The decomposition evolves as the process network undergoes various operating conditions, enabling adjustments in the distributed architecture and DMPC design. This adaptive architecture enhances the closed-loop performance and robustness of DMPC systems. The effectiveness of the multi-agent distributed control approach is investigated for a benchmark benzene alkylation process under two distinct operating conditions characterized by medium and low recycle ratios. Simulation results demonstrate that adaptive decompositions derived through spectral community detection, utilizing weighted graph representations, outperform the commonly employed unweighted hierarchical community detection-based system decompositions in terms of closed-loop performance and computational efficiency.

Optimal Periodic Impulsive Control for Orbital Stabilization of Underactuated Systems

Optimal Periodic Impulsive Control for Orbital Stabilization of Underactuated Systems

Modeling optimal control strategies for HIV and gonorrhea co-infection: Incorporating screening along with treatment

Modeling optimal control strategies for HIV and gonorrhea co-infection: Incorporating screening along with treatment

Optimal control of Hydrocarbon Reducer (HC) injection based on Trust-Region-Reflective Algorithm (TRRA) and physical models for Diesel Oxidation Catalyst (DOC)

The aim of this paper is to control the DOC outlet gas temperature between 600 ± 15 °C by optimizing the hydrocarbon (HC) injection into the Diesel Oxidation Catalyst (DOC) for active regeneration of the downstream Diesel Particulate Filter (DPF). First, based on the physical model of the DOC thermal dynamics, the energy conservation equation for the gas phase temperature is simplified by using variable substitution, and the energy conservation equation for the solid phase temperature is simplified by considering only the heat generated by the HC injection. By solving the simplified model using the Trapezoidal Rule-Backward Differentiation Formula 2 (TR-BDF2) method and optimizing the model parameters in combination with the Gauss-Newton method, the computational efficiency and accuracy were significantly improved. Next, the DOC downstream temperature observer is designed by combining the solution characteristics of the DOC model with the unscented Kalman filter (UKF) algorithm to ensure that the catalyst operates within the optimal temperature range. Then, this paper verifies the accuracy of the model and observer through bench tests covering four steady-state operation conditions and World Harmonized Transient Cycle (WHTC) test cycle, and the results show that the model and observer exhibit high accuracy in both steady-state and transient operation conditions. Finally, the DOC temperature was successfully maintained between 600 ± 15 °C by optimizing the control of the HC injection rate, thus achieving the expected temperature control target. These research results provide theoretical and practical support for diesel engine emission control and DPF active regeneration.

Optimal shift control for new dual-clutch full-powershift transmission of heavy-duty tractor based on genetic algorithm–particle swarm optimization

The calculation of a linear quadratic regulator (LQR) control weighting matrix determines whether it can achieve optimal control. In order to solve the problem of complex calculation and low efficiency of this matrix, this paper proposed genetic algorithm–particle swarm optimizatio algorithm (GAPSO) combining genetic algorithm and particle swarm algorithm to solve the weighting matrix of LQR clutch oil pressure control strategy, so as to control the design efficiency and precision of the strategy. Subsequently, the shifting quality of a tractor was improved. A dynamic model of the shifting process was established using a comprehensive indicator that combined jerk and sliding friction work as a quadratic function. An LQR-weighted matrix calculated using GAPSO enabled optimal control strategy designing for clutch hydraulic pressure. Furthermore, the shift process from the 11th to 12th gear was analyzed via simulations during plowing operations. The simulation results show that the genetic operation of crossover and variation is introduced into PSO, and the optimization ability of PSO is improved effectively. Compared with the clutch oil pressure control strategy calculated by GAPSO with LQR weighted matrix and LQR clutch oil pressure control strategy calculated by PSO with LQR weighted matrix, the clutch heat load is slightly increased, the smoothness of shifting is greatly improved, and the shifting quality of tractors is improved. Overall, this study provides valuable insights for designing and computationally analyzing optimal clutch LQR control.

Implications of baseline glycemic control by plasma glycated hemoglobin A1c on adverse outcomes in patients with coronary heart disease and type 2 diabetes mellitus: Results from the PROMISE study

BackgroundThe optimal glycosylated hemoglobin (HbA1c) target in type 2 diabetes mellitus (T2DM) patients remains controversial, especially in patients with concomitant coronary heart disease (CHD). This study aimed to investigate the correlation between baseline HbA1c and long-term prognosis in CHD patients with T2DM. MethodsThe study enrolled 6,839 CHD patients with T2DM and measured HbA1c at admission in a multicenter prospective observational cohort. Patients were divided into two groups according to baseline HbA1c levels: optimal glycemic control group (HbA1c < 7.0 %, n = 3023) and poor glycemic control group (HbA1c ≥ 7.0 %, n = 3816). The study endpoints were all-cause death and major adverse cardiac and cerebrovascular events (MACCEs). ResultsThe median follow-up period was 2.1 years. During this period, 229 (3.3 %) all-cause deaths, 165 (2.4 %) cardiac deaths, and 759 (11.1 %) MACCEs occurred. Unadjusted Kaplan–Meier analysis showed that the incidences of all-cause death, cardiac death, non-fatal MI, unplanned revascularization, and MACCEs were significantly lower in the HbA1c < 7.0 % group than in the HbA1c ≥ 7.0 % group (P < 0.05). Multivariate Cox hazard analysis indicated that the incidences of all-cause death, cardiac death and MACCEs were significantly lower in the HbA1c < 7.0 % group compared to the HbA1c ≥ 7.0 % group [all-cause death: hazard ratio (HR) 1.969, 95 % confidence interval (CI) 1.421–2.729; cardiac death: HR 2.515, 95 % CI 1.647–3.839; MACCEs: HR 1.345, 95 % CI 1.150–1.573; P < 0.001]. ConclusionsBaseline HbA1c level was associated with all-cause death, cardiac death, and MACCEs in CHD patients with T2DM.

Operation Analysis of Power Systems with HVDC Interconnections Using a Transient Stability Aware OPF Method

High-voltage direct current (HVDC) transmission systems are widely used today due to their several advantages over high-voltage alternative current (HVAC) systems. They are primarily utilized in connecting different large grids or smaller independent networks, especially in submarine interconnections. Many studies have been conducted globally on the optimal planning, operation, and control of these power systems. Understanding the transient stability of these systems during significant disturbances is also of great importance. This paper specifically focuses on power systems with HVDC connections and high penetration of renewable energy sources. Analytical models were developed for power system components like HVDC converters, DC lines, and full converter wind generators. A simulation of a test-case power system was conducted, including various significant disturbances such as HVDC line trips, short circuits, power unit failures, and major changes in load. The voltage and frequency stability of the system under specific operational scenarios was also examined. The results obtained indicate technical limitations and operating guidelines to avoid undesirable conditions and system failure. In conclusion, adopting short-term stability tests the optimal operation point of the system, and the limitations in the penetration by RES units can be decided.

Optimal control of discrete event systems under uncertain environment based on supervisory control theory and reinforcement learning

Discrete event systems (DESs) are powerful abstract representations for large human-made physical systems in a wide variety of industries. Safety control issues on DESs have been extensively studied based on the logical specifications of the systems in various literature. However, when facing the DESs under uncertain environment which brings into the implicit specifications, the classical supervisory control approach may not be capable of achieving the performance. So in this research, we propose a new approach for optimal control of DESs under uncertain environment based on supervisory control theory (SCT) and reinforcement learning (RL). Firstly, we use SCT to gather deliberative planning algorithms with the aim to safe control. Then we convert the supervised system to Markov Decision Process simulation environments that is suitable for optimal algorithm training. Furthermore, a SCT-based RL algorithm is designed to maximize performance of the system based on the probabilistic attributes of the state transitions. Finally, a case study on the autonomous navigation task of a delivery robot is provided to corroborate the proposed method by multiple simulation experiments. The result shows the proposed approach owning 8.27%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document} performance improvement compared with the non-intelligent methods. This research will contribute to further studying the optimal control of human-made physical systems in a wide variety of industries.

Coronavirus Metamorphosis Optimization Algorithm and Collocation Method for Optimal Control Problem in COVID‐19 Vaccination Model

Coronavirus Metamorphosis Optimization Algorithm and Collocation Method for Optimal Control Problem in COVID‐19 Vaccination Model

Design of sliding mode controller for servo feed system based on generalized extended state observer with reinforcement learning

Nonlinear friction, system uncertainty, and external disturbances have a significant impact on the performance of high-precision servo feed systems. In order to achieve higher tracking accuracy, a sliding mode controller based on a generalized extended state observer with the double critic deep deterministic policy gradient algorithm is designed. Firstly, a flexible two mass drive model (FTMDM) is established for the two-axis differential micro-feed system (TDMS). Next, a generalized extended state observer (GESO) is designed to estimate matched interference and mismatched interference. And it is proved that the observation error of GESO is bounded. A sliding mode controller based on GESO is further proposed. The stability of the controller has been proven through Lyapunov theory, and the error is bounded and converges to zero in finite time. The tuning process of controller parameters is simplified by using quadratic optimal control principle. Furthermore, a double critic deep deterministic policy gradient algorithm (DCDDPG) is proposed to achieve dynamic optimization of parameters about GESO. The simulation results show that GESO with DCDDPG can reduce the observation error of step signal and sinusoidal signal, and improve the observation accuracy of nonlinear friction significantly. Finally, experimental results show that the proposed control method achieves more accurate position tracking performance on TDMS.

Theoretical Analysis on Active Polarization Control of Fiber Laser Based on Root Mean Square Propagation Algorithm

High-power linearly polarized fiber lasers are widely used in coherent beam combination, nonlinear frequency conversion, and gravitational wave detection. With the increase in output power, it is challenging for fiber lasers to maintain a high polarization extinction ratio (PER). Combined with intelligent techniques, active polarization control is a prospective method to obtain the laser output with high PER and high stability. We demonstrate a comprehensive model of an active polarization control system. The root mean square propagation (RMS-Prop) algorithm is used to control the non-polarization-maintaining (non-PM) fiber laser to generate linearly polarized laser. The parameters of the RMS-Prop algorithm are theoretically analyzed, including cost function, perturbation amplitude, and global learning rate. The simulation results show that PER is the optimal cost function. When the perturbation amplitude is 0.06 and the global learning rate is 0.6, the system can achieve the optimal control speed and accuracy. By comparison with the stochastic parallel gradient descent (SPGD) algorithm, the RMS-Prop algorithm has an advantage in obtaining higher PER.

Development and validation of a predictive scoring system for hypoglycaemic agents for optimal control of blood glucose during glucocorticoid therapy.

Glucocorticoid (GC) treatments are often used. There is limited information on the prediction of hyperglycaemia after GC administration. This study aimed to identify the risk factors for hyperglycaemia after glucocorticoid (GC) administration and the need for hypoglycaemic agents to correct it and to develop and validate a novel scoring system for predicting GC-induced hyperglycaemia. In a development set, 508 adults receiving prednisolone (PSL) for the first time were divided into two groups based on treatment with or without hypoglycaemic agents. Clinical and laboratory parameters were compared, and risk factors were identified using logistic regression analysis after performing univariate analyses between the two groups. A point-addition scoring system with several categories and coefficients for each risk factor was constructed to predict the need for hypoglycaemic agents. The scoring system was then applied and validated on two validation sets: A and B. Older age, higher glycated haemoglobin percentage, body mass index and initial PSL dosage were identified as risk factors. The sensitivity, specificity and accuracy of the scoring system were 70.6%, 81.9% and 77.1% in the development set; 75.8%, 78.4% and 77.4% in validation set A; and 79.4%, 73.9% and 75.3% in validation set B respectively. By fitting the total score in the development set and the probability of hyperglycaemia to a logistic curve, a figure was created to show the probability of GC-induced hyperglycaemia in patients scheduled to receive GC. This scoring system is a novel, valid and reliable tool for predicting GC-induced hyperglycaemia and the need for hypoglycaemic agents to correct it.

Learning protocols for the fast and efficient control of active matter

Exact analytic calculation shows that optimal control protocols for passive molecular systems often involve rapid variations and discontinuities. However, similar analytic baselines are not generally available for active-matter systems, because it is more difficult to treat active systems exactly. Here we use machine learning to derive efficient control protocols for active-matter systems, and find that they are characterized by sharp features similar to those seen in passive systems. We show that it is possible to learn protocols that effect fast and efficient state-to-state transformations in simulation models of active particles by encoding the protocol in the form of a neural network. We use evolutionary methods to identify protocols that take active particles from one steady state to another, as quickly as possible or with as little energy expended as possible. Our results show that protocols identified by a flexible neural-network ansatz, which allows the optimization of multiple control parameters and the emergence of sharp features, are more efficient than protocols derived recently by constrained analytical methods. Our learning scheme is straightforward to use in experiment, suggesting a way of designing protocols for the efficient manipulation of active matter in the laboratory.

Coordinative optimization for wind farms considering improved fatigue load index

In the wind farm(WF), there exists a serious uneven distribution of damage equivalent load(DEL) on the wind turbine(WT), which increases the maintenance cost of wind power generation. Previous researches focus on a single objective such as the total or the standard deviation of fatigue loads for optimal control. To address these, this paper proposes a coordinative optimization by considering an improved fatigue load index. The coordinative optimization is achieved on both WF level and WT level. For WF level, a state-space model is built for the active power dispatch by utilizing small signal linearization. Then, an improved sensitivity analysis is performed to evaluate the fatigue load on the component of the tower and shaft. Further, a coordinative multi-objective function is designed, which minimizes the total DEL of the WF, and balances the individual fatigue load distribution of the WT. For WT level, an adaptive internal model control is proposed to suppress both tower vibrations and torque fluctuation of the main shaft. Several experiments are performed to verify the significance of the proposed method with the weight analysis of power and load coefficient. The comparison results reveals the superiority of the coordinative optimization in restraining the total DEL of the WF, balancing WF fatigue distribution, decreasing the DEL of the single WT, and satisfying the power reference tracking simultaneously.