Multiple Control Research Articles

Efficient Multitask Reinforcement Learning Without Performance Loss.

We propose an iterative sparse Bayesian policy optimization (ISBPO) scheme as an efficient multitask reinforcement learning (RL) method for industrial control applications that require both high performance and cost-effective implementation. Under continual learning scenarios in which multiple control tasks are sequentially learned, the proposed ISBPO scheme preserves the previously learned knowledge without performance loss (PL), enables efficient resource use, and improves the sample efficiency of learning new tasks. Specifically, the proposed ISBPO scheme continually adds new tasks to a single policy neural network while completely preserving the control performance of previously learned tasks through an iterative pruning method. To create a free-weight space for adding new tasks, each task is learned through a pruning-aware policy optimization method called the sparse Bayesian policy optimization (SBPO), which ensures efficient allocation of limited policy network resources for multiple tasks. Furthermore, the weights allocated to the previous tasks are shared and reused in new task learning, thereby improving sample efficiency and the performance of new task learning. Simulations and practical experiments demonstrate that the proposed ISBPO scheme is highly suitable for sequentially learning multiple tasks in terms of performance conservation, efficient resource use, and sample efficiency.

Efficient Computation of Geodesics in Color Space.

Although scientists agree that a perceptual color space is not Euclidean and color difference measures, such as CIELAB's ∆E2000, model these aspects of color perception, colormaps are still mostly evaluated through piecewise linear interpolation in a Euclidean color space. In a non-Euclidean setting, the piecewise linear interpolation of a colormap through control points translates to finding shortest paths. Alternatively, a smooth interpolation can be generalized to finding the straightest path. Both approaches are difficult to solve and are compute intensive. We compare the 11 most promising optimization algorithms for the computation of a geodesic either as the shortest or as the straightest path to find the most efficient one to use for colormap interpolation in real-world applications. For two control points, the zero curvature algorithms excelled, especially the 2D relaxation method. For multiple control points, only the mimimal curvature algorithms can produce smooth curves, amongst which the 1D relaxation method performed best.

Adaptive Anti-Disturbance Control for a Class of Uncertain Nonlinear Systems With Composite Disturbances.

High-precision and safety control in face of disturbances and uncertainties is a challenging issue of both theoretical and practical importance. In this article, new adaptive anti-disturbance control schemes are proposed for a class of uncertain nonlinear systems with composite disturbances, including additive disturbances, multiplicative actuator faults, and implicit disturbances deeply coupled with system states. Both the cases with known and unknown control/fault directions are investigated. By properly fusing the techniques of disturbance observers and adaptive compensation, it is shown that all closed-loop signals are globally uniformly bounded and the tracking error converges to zero asymptotically, no matter the control/fault directions are known or not. In the case of known directions, the proposed control scheme, for the first time, guarantees asymptotic tracking and L∞ tracking performance simultaneously in face of disturbances and actuator faults. Moreover, novel Nussbaum functions and a contradiction argument are introduced, which allow the system to have multiple unknown nonidentical control directions and unknown time-varying fault direction. Simulation results illustrate the effectiveness of the proposed control schemes.

Cooperative Control and Performance Evaluation of a Linear MIMO Parabolic Spatiotemporal Dynamic System on Directed and Switching Topologies.

The current work concerns on cooperative control of exponential stabilization and control performance improvement in the spatial domain for a linear spatiotemporal dynamic system associated with multiple control actuators and multiple collaborative measurement sensors. By assuming that the system dynamics is modeled by a MIMO parabolic partial differential equation (PPDE) and each sensor can share measurement information with its topological neighbors in directed and switching topological networks, a cooperative control protocol is proposed to achieve the control aim of this article. With the help of a combination of multiagent consensus theory, Lyapunov's method, and integral inequality technique, sufficient conditions are presented for the closed-loop exponential stability of the PPDE in the norm |·|2 . Moreover, some performance indexes are defined to evaluate the closed-loop control performance improvement in the spatial domain. Extensive simulation results are finally presented for a simple numerical example and a practical heat treatment process to verify the effectiveness and performance improvement capability of the proposed cooperative control protocol.

Active Control of Longitudinal Combustion Instability in Bluff-Body Stabilized Premixed Flames with Multiple Neural Network Controller

ABSTRACT The suppression performance of multiple neural network controller on the longitudinal combustion instability is investigated in the Volvo bluff-body stabilized premixed flame. Two controlled plant models, e.g. one-dimensional (1D) reduced order acoustic network model and three-dimensional (3D) large eddy simulation model are adopted to verify this control strategy. In order to build the reduced order Volvo acoustic network model, large eddy simulation is adopted to extract the characteristics of system heat release under wide bandwidth and construct the flame transfer functions. For the 1D acoustic network model, this controller can completely eliminate the pressure oscillation using fuel valve as actuator. For the 3D large eddy simulation model that mimics the real-time oscillating characteristics, clustered prediction method and amplitude limiting method are adopted in multiple neural network control strategy to better account for the nonlinear characteristics of the 3D Volvo flame. This control strategy enables the modulation of the phase relationship between the heat release rate oscillation and pressure oscillation by introducing small perturbations to the inlet equivalence ratio, and attenuates the amplitude of the second harmonic of the unstable mode effectively.

Day-ahead dispatch with carbon trading for multi-regulation participation and emission reduction considering multi-type energy storage

In this work, a day-ahead dispatch optimization model with energy-type, power-type, and composite-type energy storage systems (ESSs) is established to participate in multiple frequency control ancillary services (FCASs), in wind-energy-integrated power systems. This model is designed to better handle the operational uncertainties of wind energy, mitigate operational risks and reduce carbon emissions. Leveraging the complementary features of different types of ESSs, the proposed operation strategies of regulation units can respond to multiple FCASs and achieve the optimal task allocation among different types of resources. Various uncertainty handling approaches are adopted to better cope with the randomness of step disturbances and wind power fluctuations. Furthermore, a new environmental factor is devised in the dispatch objective and the time-of-use carbon-price carbon emission trading (ToU-CET) and ladder-type CET (LT-CET) models are developed. They can incorporate dynamic carbon prices and leverage mechanisms to reduce carbon emissions during daily operations. The effectiveness of the proposed model is verified by case studies, and the decisions can help save energy and reduce carbon emissions in modern power systems with wind power generators with high security and reliability.

Research on Building Power Quality Assessment and Control Technology Based on Online Monitoring

This study is to improve the assessment accuracy and control efficiency of building power quality by adopting online monitoring and integrating multiple technical indicators and control methods. A whole set of power quality technical index system is developed in the study, involving voltage deviation, harmonic voltage/current limit, and so on. The power quality indicators are quantitatively evaluated through standardization and weight determination. The assessment results show that the measured power quality indicators vary within the interval of 0 to 9, which verifies the effectiveness of the assessment system. In terms of control technology, this paper discusses in depth the static reactive power compensation (SVC) device and its role in improving building power quality, and confirms its efficacy in optimizing power quality through simulation experiments. This work not only successfully evaluates the building power quality, but also proposes practical control strategies, which provide theoretical and practical guidance for the stable operation of power systems and the further improvement of power quality.

Metropolis criterion pigeon-inspired optimization for multi-UAV swarm controller

This paper presents a new multiple unmanned aerial vehicle swarm controller based on Metropolis criterion. This paper presents the design of a controller, utilizing the improved Metropolis criterion pigeon-inspired optimization (IMCPIO) and proportional-integrational-derivative (PID) algorithms, and conducts comparative experiments. Simulation outcomes demonstrate the enhanced performance of the multi-unmanned aerial vehicle formation controller, which is based on IMCPIO, when compared to the basic pigeon-inspired optimization (PIO) algorithm and the genetic algorithm. The IMCPIO algorithm for the energy difference discrimination makes it a faster convergence and more stable effective optimization. Hence, the controller introduced in this study proves to be both practical and resilient.

Photoimmuno-antimicrobial therapy for Staphylococcus aureus implant infection.

Implant infections caused by Staphylococcus aureus are responsible for high mortality and morbidity worldwide. Treatment of these infections can be difficult especially when bacterial biofilms are involved. In this study we investigate the potential of infrared photoimmunotherapy to eradicate staphylococcal infection in a mouse model. A monoclonal antibody that targets Wall Teichoic Acid surface components of both S. aureus and its biofilm (4497-IgG1) was conjugated to a photosensitizer (IRDye700DX) and used as photoimmunotherapy in vitro and in vivo in mice with a subcutaneous implant pre-colonized with biofilm of Staphylococcus aureus. A dose of 400 μg and 200 μg of antibody-photosensitizer conjugate 4497-IgG-IRDye700DXwas administered intravenously to two groups of 5 mice. In addition, multiple control groups (vancomycin treated, unconjugated IRDye700DX and IRDye700DX conjugated to a non-specific antibody) were used to verify anti-microbial effects. In vitro results of 4497-IgG-IRDye700DX on pre-colonized (biofilm) implants showed significant (p<0.01) colony-forming units (CFU) reduction at a concentration of 5 μg of the antibody-photosensitizer conjugate. In vivo, treatment with 4497-IgG-IRDye700DX showed no significant CFU reduction at the implant infection. However, tissue around the implant did show a significant CFU reduction with 400 μg 4497-IgG-IRDye700DX compared to control groups (p = 0.037). This study demonstrated the antimicrobial potential of photoimmunotherapy for selectively eliminating S. aureus in vivo. However, using a solid implant instead of a catheter could result in an increased bactericidal effect of 4497-IgG-IRDye700DX and administration locally around an implant (per operative) could become valuable applications in patients that are difficult to treat with conventional methods. We conclude that photoimmunotherapy could be a potential additional therapy in the treatment of implant related infections, but requires further improvement.

Belief in omens and superstitions among patients with chronic neurological disorders.

Chronic neurological disorders may affect various cognitive processes, including religiosity or superstitious belief. We investigated whether superstitious beliefs are equally prevalent in patients with Parkinson's disease (PD), people with epilepsy (PWE), patients with multiple sclerosis (MS) and healthy controls (HCs). From late 2014 to early 2023 we conducted a cross-sectional in-person anonymous paper-based survey at the tertiary clinic of Vilnius University Hospital Santaros Klinikos among outpatients and HCs by asking them to ascribe meaning or report belief for 27 culturally adapted statements (9 omens and 18 superstitions). The sum of items that a respondent believes in was labeled the superstition index (SI). The SI was compared between groups by means of the Kruskal-Wallis (H) test and negative binomial regression modeling. A two-step cluster analysis was performed to discern different subgroups based on answers to the items of the SI. There were 553 respondents who completed the questionnaire (183 PWE, 124 patients with PD, 133 with MS and 113 HCs). Complete SI scores were collected for 479 (86.6%) participants and they were lower in patients with PD (n = 96, Md = 1, IQR = 0-5.75) in comparison to those with epilepsy (n = 155, Md = 6, IQR = 1-14), MS (n = 120, Md = 4, IQR = 0-12) or HCs (n = 108, Md = 4.5, IQR = 1-10), H (3) = 26.780, p < 0.001. In a negative binomial regression model (n = 394, likelihood ratio χ2 = 35.178, p < 0.001), adjusted for sex, place of residence, income and education, female sex was the only characteristic associated with the SI (β = 0.423, OR = 1.526, 95% CI = 1.148 to 2.028). Both female sex (β = 0.422, OR = 1.525, 95% CI = 1.148 to 2.026) and Parkinson's disease (β = -0.428, OR = 0.652, 95% CI = 0.432 to 0.984) were significant predictors of the SI when age was removed from the model. Two-step cluster analysis resulted in individuals with PD being grouped into "extreme non-believer," "non-believer" and "believer" rather than "non-believer" and "believer" clusters characteristic for PWE, patients with MS and HCs. Our study suggests that individuals with PD believe in less superstitions than patients with MS, PWE or HCs. The results of this investigation should be independently confirmed after adjusting for PD-specific variables.

Control strategies for inverted pendulum: A comparative analysis of linear, nonlinear, and artificial intelligence approaches.

An inverted pendulum is a challenging underactuated system characterized by nonlinear behavior. Defining an effective control strategy for such a system is challenging. This paper presents an overview of the IP control system augmented by a comparative analysis of multiple control strategies. Linear techniques such as linear quadratic regulators (LQR) and progressing to nonlinear methods such as Sliding Mode Control (SMC) and back-stepping (BS), as well as artificial intelligence (AI) methods such as Fuzzy Logic Controllers (FLC) and SMC based Neural Networks (SMCNN). These strategies are studied and analyzed based on multiple parameters. Nonlinear techniques and AI-based approaches play key roles in mitigating IP nonlinearity and stabilizing its unbalanced form. The aforementioned algorithms are simulated and compared by conducting a comprehensive literature study. The results demonstrate that the SMCNN controller outperforms the LQR, SMC, FLC, and BS in terms of settling time, overshoot, and steady-state error. Furthermore, SMCNN exhibit superior performance for IP systems, albeit with a complexity trade-off compared to other techniques. This comparative analysis sheds light on the complexity involved in controlling the IP while also providing insights into the optimal performance achieved by the SMCNN controller and the potential of neural network for inverted pendulum stabilization.

Temperature-Automated Calibration Methods for a Large-Area Blackbody Radiation Source.

High-precision temperature control of large-area blackbodies has a pivotal role in temperature calibration and thermal imaging correction. Meanwhile, it is necessary to correct the temperature difference between the radiating (surface of use) and back surfaces (where the temperature sensor is installed) of the blackbody during the testing phase. Moreover, large-area blackbodies are usually composed of multiple temperature control channels, and manual correction in this scenario is error-prone and inefficient. At present, there is no method that can achieve temperature-automated calibration for a large-area blackbody radiation source. Therefore, this article is dedicated to achieving temperature-automated calibration for a large-area blackbody radiation source. First, utilizing two calibrated infrared thermometers, the optimal temperature measurement location was determined using a focusing algorithm. Then, a three-axis movement system was used to obtain the true temperature at the same measurement location on a large-area blackbody surface from different channels. This temperature was subtracted from the blackbody's back surface. The temperature difference was calculated employing a weighted algorithm to derive the parameters for calibration. Finally, regarding experimental verification, the consistency error of the temperature measurement point was reduced by 85.4%, the temperature uniformity of the surface source was improved by 40.4%, and the average temperature measurement deviation decreased by 43.8%. In addition, this system demonstrated the characteristics of strong environmental adaptability that was able to perform temperature calibration under the working conditions of a blackbody surface temperature from 100 K to 573 K, which decreased the calibration time by 9.82 times.

Global finite‐time control for high‐order stochastic nonlinear systems with multiple unknown control directions

AbstractIn this article, a class of global finite‐time stabilization problem for high‐order stochastic nonlinear systems with multiple unknown control directions is studied. In order to solve the difficulties caused by the unknown control directions and at the same time, the system is required to converge to the origin in a limited time. Therefore, a new switching mechanism based on the Lyapunov‐like function is proposed. By adding a power integral technique and utilizing sign function, a finite‐time switching controller is designed. The controller parameters need to be adjusted online according to the proposed switching logic. It is rigorously proved that the designed controller can make the system state converge probabilistically to the origin in a finite‐time. A simulation example shows the effectiveness of the proposed control algorithm.

Erosion Monitoring in Benggang Based on Control-Free Images and Nap-of-the-Object Photogrammetry Techniques

Erosion Monitoring in Benggang Based on Control-Free Images and Nap-of-the-Object Photogrammetry Techniques

Dual Volts-per-Hertz Control of Double-Inverter-Fed Wound Rotor Induction Machine

Double-inverter-fed wound rotor induction machine (DI-WRIM) is a two-port machine capable of maintaining rated torque even at twice the rated speed. Such machines are gaining popularity in high-speed and high-power applications for their versatility and degrees of freedom in control. The existing control methods for DI-WRIM are all based on the dynamic model of the machine and the associated controller structures are complex with multiple controllers. This article proposes a novel control scheme for DI-WRIM based on the steady-state model which requires only one controller. The proposed control scheme has the capability to operate the machine in both sub and super-synchronous modes in all the four quadrants in torque-speed plane. The simplicity of the proposed dual volts-per-hertz control method can be compared to the very popular scalar control used in cage-rotor induction machine drives. Mathematical derivation of the control law with appropriate approximations is discussed. Experimental results are presented from a 5-hp, 8-pole wound rotor induction machine drive.

Supplementary Control for Quantized Discrete-Time Nonlinear Systems Under Goal Representation Heuristic Dynamic Programming.

This article is concerned with supplementary control of discrete-time nonlinear systems with multiple controllers in the framework of goal representation heuristic dynamic programming (GrHDP), where a logarithmic quantizer is used to govern the network communication. For the addressed problem, a neural network (NN)-based observer is first proposed to estimate the unknown system state in the simultaneous presence of quantized influence. In light of the estimated states and the ideal control inputs via a zero-sum game, a GrHDP algorithm with a reinforced term is developed to implement the supplementary control task, where some novel weight updating rules are constructed by virtue of an additional tunable parameter to improve the system performance. Furthermore, a set of conditions about the stability of estimated error dynamics of both observer states and updated NNs' weights are derived by resorting to the Lyapunov stability theory. Finally, the effectiveness of the developed method is verified by a power system and a numerical experiment.

Safe Control for Navigation in Cluttered Space Using Multiple Lyapunov-Based Control Barrier Functions

Safe Control for Navigation in Cluttered Space Using Multiple Lyapunov-Based Control Barrier Functions

Advanced Control Strategies for Resilient Voltage and Frequency Regulation in Smart Grids

This study discusses advanced control strategies for voltage and frequency regulation in smart grids, particularly in the integration of renewable energy sources and electrification. These strategies, including Model Predictive Control (MPC), adaptive control, optimal control, robust control, and distributed control, aim to optimize control actions while adhering to system constraints. Case studies show their effectiveness in high photovoltaic penetration, wind power integration, and microgrid operation. However, challenges persist, such as managing uncertainties and coordinating multiple controllers in decentralized power systems. The study acknowledges ongoing research and development in this field, emphasizing the potential for enhancing voltage and frequency regulation in smart grids.

A Large-Scale Multi-Agent Deep Reinforcement Learning Method for Cooperative Output Voltage Control of PEMFCs

To increase the output voltage stability and improve the operating efficiency of proton exchange membrane fuel cells (PEMFCs), a data-driven cooperative method for controlling the PEMFC output voltage is proposed in this paper. The proposed method adapts centralized learning and decentralized implementation, which can nonlinearly and adaptively realize optimal coordinated control over the hydrogen valve and the DC/DC converter. Additionally, a champion multiagent double delay deep deterministic policy gradient (CMA-4DPG) algorithm is proposed in this method, the design of which incorporates the policies of the champion selection mechanism, cooperative exploration, imitation learning guidance and curriculum guidance to improve the robustness of the cooperative method. The method cooperates with multiple controller to prevent output voltage fluctuation. The experimental results show that by simultaneously regulating the hydrogen flow through the hydrogen valve and the duty ratio of the DC/DC converter, the proposed method achieves better robustness and can improve the tracking performance of the PEMFC output voltage.

On the uniqueness of a solution to the multiplicative control problem for the electron drift–diffusion model

The multiplicative control problem for a stationary diffusion-drift model of charging a polar dielectric is studied. The role of control is played by a leading coefficient in the model equation, which has the meaning of the electron diffusion coefficient. The global solvability of the boundary value problem and the local uniqueness of its solution, as well as the solvability of the extremum problem under consideration, have been proved in the previous papers of the authors. In this paper, an optimality system is derived for the control problem and local regularity conditions for the Lagrange multiplier are established. Based on the analysis of this system, the local uniqueness of the multiplicative control problem's solution for specific cost functionals is proved.