Parallel Control Method Research Articles

A Novel Parallel Control Method for Optimal Consensus of Nonlinear Multiagent Systems.

This work concentrates on the initial introduction of parallel control to investigate an optimal consensus control strategy for continuous-time nonlinear multiagent systems (MASs) via adaptive dynamic programming (ADP). First, the control input is integrated into the feedback system for parallel control, facilitating an augmented system's optimal consensus control with an appropriate augmented performance index function to be established, which is identical to the original system's suboptimal control with a conventional performance index. Second, the feasibility of the proposed control scheme is evaluated based on the policy iteration algorithm, and the convergence of the algorithm is demonstrated. Then, an online learning algorithm becomes available to implement the ADP-based optimal parallel consensus control protocol without prior knowledge of the system. The Lyapunov approach is employed to indicate that the signals are convergent. Ultimately, the experimental data support the theoretical results.

A Parallel Control Method For Zero-Sum Games With Unknown Time-varying System

In this paper, based on ACP approach, a parallel control method is proposed for zero-sum games of unknown time-varying systems. The process of constructing a sequence of artificial systems, implementing the computational experiments and conducting the parallel execution is presented. The artificial systems are constructed to model the real system. Computational experiments adopting adaptive dynamic programming (ADP) are shown to derive control laws for the a sequence of artificial systems. The purpose of the parallel execution step is to derive the control laws for the real system. Finally, simulation experiments are provided to show the effectiveness of the proposed method.

Research on parallel control strategy of power converters based on fuzzy neural network

As pure electric vehicles shift toward intelligent technology, the energy demand for onboard equipment is on the rise. In this study, a parallel control strategy for two 3-kW DC–DC power converters was proposed to meet the power requirements of pure electric vehicle loads in this paper. First, the operation mode of the resonant power converter was analyzed. The operation mode of the power converter adopted the advantageous frequency conversion–phase shift control mode. Second, a parallel control method for Takagi–Sugeno-type fuzzy neural network converters with four inputs and a single output first-order was designed to meet the power demand based on the advantages of fuzzy control and neural networks. The neural networks can be trained automatically based on the established requirements, and the fuzzy rules formulated through fuzzy neural networks were more detailed and accurate. Finally, the proposed control strategy was validated by experiments. The experimental results showed that the proposed control strategy can ensure the stable operation of the power converter during switching under the set load. The output power of the primary and sub converters varies linearly, which can meet the load’s demand for high power. There is no need to develop higher-power power converters. These results can provide a new idea for the research of high-power power converters and reduce development costs.

An Instantaneous Load-Sharing Parallel Control Method Using Improved PWM-Capture With DMA in a Power Conversion System

An Instantaneous Load-Sharing Parallel Control Method Using Improved PWM-Capture With DMA in a Power Conversion System

Adaptive droop control strategy of parallel LLC converters

AbstractWhen the resonant converters are connected in parallel, the output current is unbalanced without additional current‐sharing measures due to differences in resonant component. In the input‐parallel output‐parallel resonant converter system, the output voltage accuracy of the converter under the traditional droop control is analyzed when the load changes. In this paper, an adaptive droop control is proposed, in which the droop coefficient is adaptively adjusted as the load changes. The control method proposed in this paper realizes the current sharing between the converters and solves the shortcomings of the output voltage changing under different loads in the conventional droop control. Taking two parallel LLC converters as an example, the control block diagram of the parallel control method for adaptive adjustment of the droop coefficient proposed in this paper is given, and the stability of the control loop is analyzed. Finally, two parallel prototypes of LLC converters with input 200 V/output 24 V input are built by digital control, and the experiment shows that the proposed control method improves the shortcomings of excessive output voltage droop caused by traditional droop control during dynamic load switching, which verifies the effectiveness of the method.

Neural Predictor-Based Dynamic Surface Parallel Control for MIMO Uncertain Nonlinear Strict-Feedback Systems

We propose a neural predictor-based dynamic surface parallel control method for a class of uncertain nonlinear systems in this brief. The dynamics of the physical system is in the strict-feedback form and subject to multi-input, multi-output, and uncertain nonlinearities. The parallel control method is developed based on an ACP methodology, including three steps. Firstly, an artificial system to the physical system is developed using an echo state network-based neural predictor structure. Next, a high-order tuner-based computational experiment is developed to achieve online adaptive training of the echo state network. Finally, parallel execution is developed by using a second-order linear tracking differentiator-based dynamic surface control approach. The total closed-loop system can be proved to be input-to-state stable. The effectiveness of the proposed theoretical results is demonstrated by a simulation of trajectory tracking of an autonomous surface vehicle.

A Novel Parallel Control Method for Continuous-Time Linear Output Regulation With Disturbances.

In this article, a novel linear parallel control method is developed for output regulation problems with disturbances. The traditional feedback regulators are passive regulation methods. In order to solve this problem, the parallel controllers are presented, where the time variation of the control is constructed instead of the control value itself, to stabilize the output of the system. The main contributions of the developed method include two aspects: 1) a novel parallel regulator structure is presented, which can provide greater flexibility comparing with traditional methods and 2) the necessary and sufficient conditions for the existence of parallel regulators are analyzed. First, the structure of the linear parallel regulators is provided. Next, considering the situations that the full system information is obtained and the information of error is obtained, respectively, the properties of the parallel regulators are analyzed, and the regulator designs for the two situations are provided. Finally, numerical examples are provided to verify the correctness of the present method.

Parallel evolution and control method for predicting the effectiveness of non-pharmaceutical interventions in pandemics.

Nonpharmaceutical interventions (NPIs) are important strategies to utilize in reducing the negative systemic impact pandemic disasters have on human health. However, early on in the pandemic, the lack of prior knowledge and the rapidly changing nature of pandemics make it challenging to construct effective epidemiological models that can be used for anti-contagion decision-making. Based on the parallel control and management theory (PCM) and epidemiological models, we developed a Parallel Evolution and Control Framework for Epidemics (PECFE), which can optimize epidemiological models according to the dynamic information during the evolution of pandemics. The cross-application between PCM and epidemiological models enabled us to successfully construct an anti-contagion decision-making model for the early stages of COVID-19 in Wuhan, China. Using the model, we estimated the effects of gathering bans, intra-city traffic blockades, emergency hospitals, and disinfection, forecasted pandemic trends under different NPIs strategies, and analyzed specific strategies to prevent pandemic rebounds. The successful simulation and forecasting of the pandemic showed that the PECFE could be effective in constructing decision models during pandemic outbreaks, which is crucial for emergency management where every second counts. The online version contains supplementary material available at 10.1007/s10389-023-01843-2.

Parallel inverter control method based on adaptive parameters

AbstractIn the inverter parallel system, due to the uncertainty of the working environment, the output voltage cannot meet the requirements, and the power distribution accuracy by the traditional droop control method is low. To increase the control accuracy of the output voltage of the inverter parallel system and improve the power distribution accuracy between parallel modules, this paper proposes a parallel inverter control method based on adaptive parameters. This method is improved on the basis of traditional P‐V droop control, and an adaptive parameter control method is implemented by the Lyapunov function, which can effectively improve the control accuracy of the output voltage and ensure that the output voltage remains within a certain constraint range under any load conditions. In order to further improve the output voltage control accuracy, the adaptive parameter control method is adopted. The parameter optimization strategy was deduced, and the steady‐state and dynamic control performances were significantly improved. Simulation and experimental results verify the effectiveness of the proposed control method.

A Precession Effect Suppression Method for Active Magnetically Suspended Rotor

The attitude angular velocity measurement and output torque precision of magnetically suspended control and sensitive gyroscope (MSCSG) is seriously affected by the strong gyro effect (precession and nutation) of magnetically suspended rotor. This article addresses the two key challenges on the precession effect control of magnetically suspended rotor. The precise precession frequency of the magnetic suspension rotor was obtained by critical speed analysis and a novel parallel control method of PID and band stop filtering based on differential cross feedback is proposed. Based on the shortcomings of the existing control methods, the phase margin of the precession effect is improved by differential cross feedback. Then, the rotor precession effect is suppressed by the parallel control of PID and band-stop filter. By adjusting the cut-off frequency of the band stop filter, we can ensure stable operations over the entire speed ranges. The simulation results show that the proposed method can provide a greater phase lead and optimize the distribution of precession poles. The experimental results show that the power spectrum level of the precession pole using the method proposed is below −48.9 dB and the rotor tilt angle is less than 0.024°.

Event-triggered optimal control for discrete-time multi-player non-zero-sum games using parallel control

This paper presents a novel event-triggered optimal control (ETOC) method for discrete-time (DT) multi-player non-zero-sum games (NZSGs). First, a novel event-triggered algorithm is developed for DT multi-player NZSGs based on the time-triggered optimal value functions. Therefore, the developed event-triggered algorithm only needs to solve the time-triggered Hamilton–Jacobi-Bellman (HJB) equations. Then, the asymptotic stability of the closed-loop system is proved. Additionally, we show that an upper bound for the sum of the actual performance indices of all the players can be determined in advance. A key step in the implementation of the developed event-triggered algorithm is to obtain the next state of the actual system, which is difficult to implement on the actual system. Thus, a parallel control method is utilized to predict the next state by constructing the parallel system for the actual system and obtain the optimal value functions. The method that combines the developed event-triggered algorithm and parallel control is called the event-triggered optimal parallel control (ETOPC) method. The neural network (NN) technique and the iterative adaptive dynamic programming (IADP) technique are employed in parallel control. Moreover, the control stability is shown further in the consideration of the NN weight approximation errors. Finally, two simulations justify the theoretical conjectures.

Comparative study of the synchronous control for rigidly coupled electro-motors

Multiple electro-motors rigidly coupled by multi-points gear meshing is of a typical driving structure, and the synchronous control between parallel motors is the key issue about system stability and performance. Firstly, the drooping control is introduced to modify traditional speed parallel control (SPC) method; classical torque master-slave control (TMSC) using soft control parameter is also proposed as contrast. Subsequently, the basic principle of vector control with rotor flux orientation is described; the non-linear time-varying gear dynamics at the unbalanced pinion-gear meshing stage are also studied. Based on the mathematic analysis, simulation models under three different control methods are established in Simulink, and are comparatively studied against backlash, contact ratio and motor parameter deviations between multiple chains. Finally, SPC with drooping control (SPCD) and soft TMSC (STMSC) are applied on a test rig. The simulation and experimental results show that, SPC, SPCD and STMSC separately has advantages in speed tracking, system stability and torque synchronization, and should be used according to specific preference.

Multi-intelligent connected vehicle longitudinal collision avoidance control and exhaust emission evaluation based on parallel theory

With the increasing of vehicle volume and driving speed, traffic accidents and environmental safety have become social concerns. Vehicle traffic accidents, especially multi-vehicle chain accidents, cause damage to property and human lives. Meanwhile, traffic pollution will lead to continuous harm to living environment and health. This is a coupled human-vehicle-environment interaction system, which is difficult to model with traditional mathematical methods. Parallel theory is an effective method to solve such complex problems based on advanced artificial intelligence and computer technology. In this paper, a parallel system is built to analyze and control multi-intelligent connected vehicle based on parallel theory. The parallel system is also used to analyze and assess the exhaust emission of multi-intelligent connected vehicle. The parallel system is carried out with three steps: 1) modeling and representation of multi-intelligent connected vehicle system using artificial societies; 2) analysis and evaluation by computational experiments; 3) control, management and exhaust emission evaluation through parallel execution of real and artificial systems and big data. The parallel control methods, models and conclusions obtained from this paper can be used to enhance the experience of safety in multi-vehicle control under vehicle to everything environment and make the safety intervention measures more efficient.

Parallel Hierarchical Control-Based Efficiency Enhancement for Large-Scale Virtual Reality Traffic Simulation

Virtual reality (VR)-based traffic simulation plays an important role in the study of transportation systems, especially in the testing of connected and autonomous vehicles. However, a simulator that provides a large-scale traffic scene with high fidelity is still not available. To address this issue, the parallel hierarchical control method is proposed in this article, which presents a framework that enables the efficient simulation of a VR-based large-scale traffic scene. First, the original data of the basic traffic components are acquired through a modification and generation method. Then, based on the proposed method for spatial parallel slicing, the prepared data and simulation tasks are separated and distributed to subcontrollers considering the connected-vehicle environment. Meanwhile, a fidelity loss-based hierarchical control method is integrated to stratify the separate data into multiple levels. Finally, the experiments are carried out on a virtual driving platform that indicates that the proposed approach effectively ensures the fidelity of the visualized scenes and performs a better allocation of computational resources.

Parallel control for optimal tracking via adaptive dynamic programming

This paper studies the problem of optimal parallel tracking control for continuous-time general nonlinear systems. Unlike existing optimal state feedback control, the control input of the optimal parallel control is introduced into the feedback system. However, due to the introduction of control input into the feedback system, the optimal state feedback control methods can not be applied directly. To address this problem, an augmented system and an augmented performance index function are proposed firstly. Thus, the general nonlinear system is transformed into an affine nonlinear system. The difference between the optimal parallel control and the optimal state feedback control is analyzed theoretically. It is proven that the optimal parallel control with the augmented performance index function can be seen as the suboptimal state feedback control with the traditional performance index function. Moreover, an adaptive dynamic programming ( ADP ) technique is utilized to implement the optimal parallel tracking control using a critic neural network ( NN ) to approximate the value function online. The stability analysis of the closed-loop system is performed using the Lyapunov theory, and the tracking error and NN weights errors are uniformly ultimately bounded ( UUB ) . Also, the optimal parallel controller guarantees the continuity of the control input under the circumstance that there are finite jump discontinuities in the reference signals. Finally, the effectiveness of the developed optimal parallel control method is verified in two cases.

Parallel model predictive direct power control of DFIG for wind energy conversion

This paper presents a parallel model predictive power control (PMPPC) method of doubly-fed induction generator (DFIG) for variable-speed wind turbine applications (VSWT). The use of a hysteresis controller and a predefined switching table will inevitably lead to select a non-optimal voltage vector in terms of minimizing power errors, power ripple and THD currents. In this research paper, a new approach of direct power control (DPC) of DFIG based model predictive direct power control is developed by minimizing two cost functions to select the voltage vectors for stator active and reactive power in synchronously manner and consequently the optimal switching vector state of two-level voltage source inverter (2L-VSI). The proposed PMPPC control scheme takes full advantage and the power regulation objective of DFIG is confirmed by the simulation results compared to the DPC and PPC methods.

Discrete-Time Self-Learning Parallel Control

In this article, a new self-learning parallel control method, which is based on adaptive dynamic programming (ADP) technique, is developed for solving the optimal control problem of discrete- time time-varying nonlinear systems. It aims to obtain an approximate optimal control law sequence and simultaneously guarantees the convergence of the value function. Establishing the time-varying artificial system by neural networks in a certain time-horizon, a control-sequence-improvement ADP algorithm is developed to obtain the control law sequence. For the first time, the criteria of the parallel execution are presented, such that the value function is proven to converge to a finite neighborhood of the optimal performance index function. Finally, numerical results and analysis are presented to demonstrate the effectiveness of the parallel control method.

Parallel Control Method of Microgrid Inverter Based on Adaptive Droop Control

Energy is the driving force of social and economic development. With the gradual depletion of conventional energy and the increasingly prominent ecological and environmental problems, the power industry needs to find a sustainable development path. Parallel control of microgrid inverters based on adaptive droop control is an important part of future intelligent and sustainable power systems. However, with the continuous increase of power generation equipment capacity and the continuous increase of load power consumption, the form of power supply by a single inverter can no longer meet the requirements. Therefore, parallel connection of multiple inverters has been widely used in the microgrid. In this paper, a set of parallel control methods for microgrid inverters based on adaptive droop control is established. This paper is based on the inductance of the inverter’s terminal impedance, and the amplitude of the inverter terminal voltage is positively correlated with the output reactive power and has a strong correlation It is concluded that the microgrid based on adaptive droop control has the ability to automatically adjust the voltage and frequency of the microgrid. Properly reducing the droop coefficient can improve the primary frequency modulation capability of the microgrid. In this paper, an improved resistive control equation is used in a parallel system, a multi-loop control structure with a quasi-resonant controller is used for the inverter in the parallel system, and a virtual complex impedance is added to perform small signal analysis on the improved resistive control equation The overall performance of the parallel system is analyzed, and the effect of different control coefficients on the system performance is clarified. The experimental research results show that when analyzing the effect of the inductance coefficient value on the equivalent output impedance in the virtual complex impedance, the value of KL is 1 At this time, the equivalent output impedance becomes resistive again, and the property is stronger than when KL is 0.05.

Clinical Study on Different Types of Insomnia Based on Conducting Yin from Yang, Conducting Yang from Yin Method with Spleen and Stomach as Center

[Objective] On the basis of observing Jinding Decoction (Glycyrrhiza 6g Poria 9g Pinellia 3g Guizhi 9g Peony 9g Long Bone 6g Oyster 9g), clinical research on different types of insomnia based on the pulse condition. [Method] Using the randomized parallel control method, 150 patients with insomnia who were treated in the Treatment Center and Brain Disease Department of our hospital from November 2019 to June 2020 were divided into treatment group, TCM control group, Western medicine control group, and TCM control group Adopting traditional Chinese medicine to collect through observation, hearing, and inquiring. According to the symptoms, tongue condition and pulse condition, the syndromes were differentiated one by one, followed by TCM classification and treatment. The western medicine control group was treated with alprazolam tablets (Jiangsu Enhua Pharmaceutical Co., Ltd., Zhunzi H32020215). Medicine treatment. 0.4 mg daily, orally 30 minutes before bedtime. Continuous treatment for 10 days is a course of treatment, each course is separated by 1 day, a total of 3 courses of treatment, observation of long-term efficacy observation before and after treatment were performed 1 day before the beginning of the treatment, the second day after the end of the treatment, and one month after stopping the treatment. Patients in the group were evaluated for clinical efficacy, PSQI score, and (SLC-90) score. [Results] Jinding Decoction had statistical significance in the treatment of insomnia of various syndrome types (P<0.05). [Conclusion] Spleen and stomach harmony is the basis for the orderly rise and fall of yin and yang qi, the hidden primordial spirit, and the foundation of sleep soundness. Jinding Decoction is based on the basic theory of traditional Chinese medicine.

Research and Design of Multi-Joint Bionic Robot

According to the various movement patterns of snakes and caterpillars, the body structure of Multi-Joint Bionic Robot is designed and analyzed, and the Multi-Joint Bionic Robot model was printed by ABS material 3D.The multi-Joint Bionic Robot is designed to achieve winding forward, multi-limb reversal movement, turning, curled up and other movements, through the voice signal detection, ultrasonic distance detection to mimic the ears and eyes of the creature. At the same time, for the problem of controlling joints, a control method combining distributed control and centralized control is proposed, and the parallel control method of multi-joint joints is studied. Experiments were carried out on the multi-joint robot body to analyze the force of the Multi-Joint Bionic Robot during the movement, and the smoothness and fluency of the robot movement were realized.