Proportional Control Strategy Research Articles

Synchronization of complex networks with PID pinning controller

Recently, synchronization of complex networks has been studied deeply; however, most existing works take the standard proportional control strategy approach to pinning controllers, which produces a steady-state error and has a significant time of fluctuation. In order to solve this problem, we propose a new method using the Proportional-Integral-Differential (PID) control to design a pinning control method for synchronization of complex networks. Based on Lyapunov theory, the stability of the network is proved, and sufficient synchronization conditions are obtained. Finally, examples are given to evaluate the effectiveness of the proposed PID pinning method and compare it to the traditional proportional pinning controller and adaptive pinning controller.

IHUVS: Infinite Homography-Based Uncalibrated Methodology for Robotic Visual Servoing

The construction of task functions in robotic manipulation is of paramount importance for uncalibrated visual servoing. The existing methods generally use image information as control variables and estimate the image Jacobian matrix online, thus possessing issues relating to convergence, and image Jacobian matrix singularities. Therefore, this work proposes a novel methodology dubbed infinite homography-based uncalibrated visual servoing (IHUVS), in which the visual control of the robot end-effector pose is decomposed into its rotational and translational components. The corresponding rotational controller designs the visual servoing task function using the relationship between the infinite homography matrix and rotation matrix, and employs the Kronecker product to derive linear equations for rotational control, as well as to conduct the associated task error analysis. Meanwhile, the translational controller utilizes Kalman filtering for online estimation of the Jacobian matrix that is required by the proportional control scheme. The robot end-effector motion in Cartesian space is generated via the IHUVS method, without knowing the camera's intrinsic parameters and the robot hand-eye relationship. A simulation analysis is carried out to assess the algorithm's numerical performance, while robotic visual servoing experiments are also conducted to verify the accuracy and efficacy of the proposed IHUVS method.

Dynamic optimal control of coal tar chemical looping gasification based on process modelling and intelligent screening

Aiming to solve the problem of low utilization and poor economic efficiency of coal tar, this study proposes a new method to convert coal tar into syngas that can be used for coal-to-oil production through chemical looping gasification reactions, thus reducing the cost of coal-to-oil production. This proposal converts tar to syngas for Fischer-Tropsch reaction through chemical looping gasification cleaning process, which is realized by process simulation and artificial intelligence prediction to ensure the stable products quality under feed jump abnormal conditions. The proposed process model is first built and the optimal operating parameters are determined by sensitivity analysis after the optimization sequence is determined from correlation analysis. A dynamic model based on the optimal steady-state model is second generated to design an optimal control scheme to efficiently deal with the unusual conditions of the feed step. The data-driven deep learning model LSTM is used to intelligently screen the optimal proportional control scheme with the advantage of low time and cost consumption. Finally, the optimal control scheme is obtained by combining the prediction results of the LSTM model and the controller additions of other important parameters, and the control effect is tested by simulating the tar feed leap conditions proves that the control6 scheme is highly resistant to disturbances. The method achieves 84.1 % high carbon conversion rate of medium-low temperature tar and 88.14 % excellent selectivity of syngas. At the same time, the adjusting of feed step condition with long-term and short-term memory network promotes the development of process design with the low time and cost consumption.

Robust fault estimation and proportional derivative fault tolerant control for a class of singular systems with interval time‐varying delay and disturbance

SummaryThis paper deals with observer‐based fault estimation (FE) and proportional derivative state feedback fault tolerant control (FTC) schemes for singular systems affected by actuator fault, interval time‐varying delay and external disturbance. Firstly, the system transformation method of constructing the derivative matrix into a full‐rank matrix is adopted to transform the singular system into a normal system, which effectively eliminates the impulse characteristic of the singular system. Secondly, in the design of the dynamic observer, fault estimation utilizes the real‐time output error and its derivative simultaneously, which can achieve better estimation performance when the fault change frequently. Most importantly, the observer‐based FTC adopts a proportional derivative state feedback control, considers disturbance and interval time‐varying delay, and the introduction of state estimation derivative term can eliminate the impulse property of the closed‐loop system, which optimizes the design scheme of the fault‐tolerant controller. The obtained sufficient conditions for stability can be solved by the strict linear matrix inequality (LMI), and the superiority of the conclusions will be further verified by the simulations.

Master-slave Control Strategy of Surgical Robots for Gastrointestinal Surgery

The precise construction of the master-slave control strategy is a fundamental requirement for robot-assisted surgery. In this study, we propose a master-slave control strategy for gastrointestinal surgical robots based on the natural orifice transluminal endoscopic surgery surgical robot developed by Tianjin University. The forward and inverse kinematics of the continuum mechanism at the slave manipulator are investigated. A master-slave coordinate mapping system for surgical robots is established by analyzing the kinematic characteristics of the master and slave manipulators. Additionally, an intuitive mapping algorithm and a control flowchart for the robot are developed. The proportional control and incremental control strategies tailored for natural orifice transluminal endoscopic surgery procedures are proposed based on intuitive mapping control. Finally, a master-slave trajectory tracking experiment is performed to validate the feasibility of the proposed master-slave control strategy.

Three link flexible arm robust regulation via proportional retarded control scheme

In this paper, a delayed distributed proportional control is developed for a class of multiple-input-multiple-output systems in order to reduce the effects of external disturbances and uncertain dynamics. The system under consideration is a three degrees-of-freedom planar robot, consisting of an active joint driving the first link and passive flexible joints for the second and third links. The control challenge for this system is to drive the flexible arm around the desired setpoint or trajectory by controlling the first link. Actually, by using a specific Lyapunov-Krasovskii storage functional associated with a constrained optimization problem, the robustness of the proposed proportional retarded control action is achieved. This analysis, together with the attractive ellipsoid concept allows to conclude with Ultimately-Uniformly-Bounded stability. The closed-loop control algorithm is tested in numerical simulation and a comparative study with some existing recent control schemes based on the performance error is presented.

Intelligent Power Control of Inverter Air Conditioners in Power Systems: A Brain Emotional Learning-Based Approach

Inverter air-conditioning (IAC) units have been proved to be effective in frequency regulation by providing flexible capacities. This paper proposes a brain emotional learning (BEL)-based controller to provide the IACs with control signals to be efficiently involved in the frequency regulation in power systems. The BEL-based controller can learn quick-auto, making it appropriate in systems facing uncertainty. To assess the BEL controller performance in realistic conditions, the uncertainties as a consequence of variations in system parameters and load level are considered. The goal is to use the BEL controller to increase the IAC units' ability to track regulation signals accurately in uncertain circumstances. The controller is compared to a fuzzy-PI control, a proportional control scheme, a model predictive control and a linear quadratic regulator control. A delay-dependent stability criterion is used to calculate the highest time delay in the IACs response under which the system maintains stability. In addition, this paper presents an BEL-based coordinator to coordinate the IACs and traditional generation units for compensating considerable frequency variations caused by the time delays. Case studies are accomplished on a multi-area power system in MATLAB/Simulink environment. Eventually, real-time verifications by OPAL-RT real-time digital simulator on the simulated power system are executed to assess the control method.

Regenerative Braking Control Strategy Based on Pavement Recognition Controller for Electric Vehicle

Regenerative braking is a key technology for electric vehicles to improve energy efficiency and extend driving range. Considering the impact of road surface adhesion coefficient in the regenerative braking process of vehicles is a meaningful but challenging problem. To effectively utilize the pavement adhesion coefficient and improve the brake energy recovery effect, a pavement observer‐based regenerative braking control strategy for pure electric vehicles is proposed. The control strategy can identify the pavement based on correction factor by analogy idea according to the real‐time state information of vehicles. The optimal braking force distribution coefficient under different pavements is obtained by Fmincon function based on interior point algorithm. The effectiveness of the control strategy is verified by Simulink/CarSim software cosimulation and road test. The results show that the braking time of the proposed regenerative braking control strategy is reduced by 8.2% and the average actual feedback braking torque is increased by 17.6% in dry asphalt conditions compared with the fixed proportional distribution control strategy. Under the premise of effectively ensuring the braking stability and safety of the vehicle, more energy generated during braking can be recovered.

A novel PID type iterative learning controller optimized by two‐dimensional infinite horizon linear quadratic iterative learning control for batch processes

AbstractAlthough a proportional integral derivative type iterative learning control (PIDILC) scheme can achieve good performance, its application is limited by open‐loop structure, parameter tuning, and initial state. In this paper, a novel PIDILC optimized by two‐dimensional infinite horizon linear quadratic regulator (PIDILC‐2D‐IHLQR) is proposed. First, by synthesizing the advantage of the conventional PIDILC method and proportional integral derivative (PID) strategy, a novel closed‐loop PIDILC scheme is obtained. Then, a novel two‐dimensional infinite horizon linear quadratic regulator (2D‐IHLQR) is developed to optimize the parameters of the PIDILC strategy. The limitations of parameter tuning and initial state are solved by this PIDILC‐2D‐IHLQR method. Therefore, the proposed method not only solves the aforementioned limitations but also inherits the advantages of PIDILC algorithm, PID method, and the novel 2D‐IHLQR scheme. Moreover, a stability condition is given based on Lyapunov theory and it can help judge whether the selection of control parameters meets the stability condition. The effectiveness of the proposed method is demonstrated by the case study on an injection modelling process.

Simulation and optimization of hydrogen consumption in a fuel cell/battery hybrid vehicle

This paper presents a physics-based mathematical model for a fuel cell hybrid vehicle system. The performance design of fuel cell hybrid electric vehicles (FCHEVs) is an area of interest in transportation applications. FCHEV is a combination of a proton exchange membrane fuel cell (PEMFC), with a battery and associated DC/DC and DC/AC converters. Suitable batteries are used in FCHEV acts as a backup system with efficient energy management. The battery is designed for fast power transfer during transient response and constant performance without hydrogen. The power management schemes discussed are consistent with the most common modern power management techniques used in fuel cell vehicle applications, including: the state engine control strategies, rule-based fuzzy logic strategies, classical proportional integral control strategies, frequency fuzzy logic decoupling/control minimization strategies. Therefore, we tested and evaluated the reliability of the model using the MATLAB/Simulink environment, with mixed results.

On-Off and Proportional Closed-Loop Adaptive Deep Brain Stimulation Reduces Motor Symptoms in Freely Moving Hemiparkinsonian Rats

ObjectivesClosed-loop adaptive deep brain stimulation (aDBS) continuously adjusts stimulation parameters, with the potential to improve efficacy and reduce side effects of deep brain stimulation (DBS) for Parkinson’s disease (PD). Rodent models can provide an effective platform for testing aDBS algorithms and establishing efficacy before clinical investigation. In this study, we compare two aDBS algorithms, on-off and proportional modulation of DBS amplitude, with conventional DBS in hemiparkinsonian rats. Materials and MethodsDBS of the subthalamic nucleus (STN) was delivered wirelessly in freely moving male and female hemiparkinsonian (N = 7) and sham (N = 3) Wistar rats. On-off and proportional aDBS, based on STN local field potential beta power, were compared with conventional DBS and three control stimulation algorithms. Behavior was assessed during cylinder tests (CT) and stepping tests (ST). Successful model creation was confirmed via apomorphine-induced rotation test and Tyrosine Hydroxylase–immunocytochemistry. Electrode location was histologically confirmed. Data were analyzed using linear mixed models. ResultsContralateral paw use in parkinsonian rats was reduced to 20% and 25% in CT and ST, respectively. Conventional, on-off, and proportional aDBS significantly improved motor function, restoring contralateral paw use to approximately 45% in both tests. No improvement in motor behavior was observed with either randomly applied on-off or low-amplitude continuous stimulation. Relative STN beta power was suppressed during DBS. Relative power in the alpha and gamma bands decreased and increased, respectively. Therapeutically effective adaptive DBS used approximately 40% less energy than did conventional DBS. ConclusionsAdaptive DBS, using both on-off and proportional control schemes, is as effective as conventional DBS in reducing motor symptoms of PD in parkinsonian rats. Both aDBS algorithms yield substantial reductions in stimulation power. These findings support using hemiparkinsonian rats as a viable model for testing aDBS based on beta power and provide a path to investigate more complex closed-loop algorithms in freely behaving animals.

Bipartite consensus for networked Euler–Lagrange systems with cooperative–competitive interactions and time delays

AbstractThis paper investigates the coordinated behaviour of the multiple Euler–Lagrange systems under diverse interactions with time delays. Specially, in the case of undirected interconnection, the proportional plus damping control strategy is proposed and the sufficient conditions of bipartite consensus are derived via energy function based approach. Subsequently, a velocity‐free controller is further developed by introducing a novel second‐order auxiliary system to each agent. In the case of directed network, by fully investigating the property of the Laplacian matrix associated with the cooperative–competitive interactions, a bipartite consensus controller is devised and the input–output property analysis of the system transfer function in frequency domain is presented. Simulation results are provided to demonstrate the effectiveness of the presented algorithms.

Continuous Estimation of Lower Limb Joint Angles From Multi-Stream Signals Based on Knowledge Tracing

Multi-stream signals are increasingly being used in robot-assisted rehabilitation training, where the timely and accurate prediction of a patient's motor intentions is frequently required to provide simultaneous and proportional control strategies. However, existing methods for motion intent prediction typically isolate signals from different modalities, resulting in a loss of signal traceability and interpretability. Therefore, we propose a multi-stream signal-fusion strategy based on knowledge tracing (MSKT). First, we collected surface electromyography, force myography, and vibroarthrography signals from the lower extremity and used different encoders to map these signals into a problem component, concept component, and identity token component. The process of human learning was then simulated using a knowledge-tracing strategy to reconstruct these components into new features. Finally, the reconstructed set of depth features was mapped to joint angles. We validated the performance of the proposed method using motion data from eight participants in different locomotion modes and compared the results to those of a temporal convolution network and long short-term memory networks. The results indicate that the MSKT achieves the lowest root-mean-squared error. This study demonstrates that the proposed knowledge-tracing-based strategy enables accurate continuous estimation for lower-limb exoskeleton rehabilitation robots.

Active tuning of the vibration and wave propagation properties in electromechanical metamaterial beam

Locally resonant metamaterial beams made from flexible substrates with piezoelectric layers can exhibit bandgap and vibration attenuation properties. However, the bandgap properties of the electromechanical metamaterials are limited by the electromechanical coupling coefficient. In order to effectively overcome this limitation of the locally resonant bandgaps, a locally resonant electromechanical metamaterial beam with piezoelectric actuators and sensors is presented, and the piezoelectric shunting technique and negative proportional feedback control strategy are combined. In this design, both negative capacitance (NC) and inductance (L) are incorporated into the shunt circuits. Then, the classical root locus method is employed to obtain single/multiple bandgaps and particular structural response by arranging the poles and zeros. Finally, the influences of the feedback control gain, the shunt circuit type, and the damping ratio on the bandgap properties and wave propagation behaviors are analyzed. Numerical results demonstrate that the single/multiple bandgaps can be obviously broadened by properly increasing the control gain. Specifically, adding negative capacitance in series to pure inductive circuit can generate wider absolute bandgaps at lower frequencies. The comparison of the frequency response and the bandgap characteristics reveals a very good agreement. Summarily speaking, combining the piezoelectric shunting technique and negative proportional feedback control strategy can effectively tune the vibration and wave propagation behavior.

Global asymptotic stabilization of a pendulum using a single Lyapunov proportional bang-bang control strategy

The existence of a Lyapunov function is known to ensure either local or global stability of a system’s equilibrium state. Inspired by the control-Lyapunov method, we here construct a Lyapunov candidate function by analyzing a pendulum system’s total energy and then applying appropriate control actions such that the conditions of a Lyapunov function are met. More specifically, our controller evaluates the Lyapunov function’s time derivative at each time step, and applies control torque such as to ensure that the Lyapunov function decreases for each step toward the goal upright state. Unlike the control-Lyapunov method, which aims to select control input as to minimize the Lyapunov function’s time derivative, our method provides up front the satisfactory conditions that yield a globally stable controller by using a rigorously designed proportional bang-bang control strategy. We show how to derive the controllers evaluation function, and how the controller is implemented in code. We further demonstrate the effectiveness of our method through numerical simulations. The result of our approach is a globally stable upright pendulum using a single-controller strategy.

Research on magneto-hydraulic proportional control strategy for single degree of freedom supporting system of magnetic-liquid double suspension bearing.

Compared with the traditional active electromagnetic bearing, the additional hydrostatic supporting concept is imported to form magnetic-liquid double suspension bearing (MLDSB) without affecting the support performance. Therefore, the bearing carrying capacity and stiffness are greatly increased and the operational stability and reliability of MLDSB are also improved. The controlling strategy and the regulating mode have the major impact on the safe and stable operation of MLDSB, which directly determines the performance quality. MLDSB adopts the magnetic-liquid coupling supporting mode with electromagnetic suspension as the main and hydrostatic supporting as the supplement, which greatly increases the complexity and difficulty of MLDSB. Therefore, the proportional control strategy and the magneto-hydraulic coupled nonlinear controller of MLDSB are explored in this paper. First, the regulation mechanism of the single degree of freedom (DOF) supporting system is revealed, and the proportional-integral-differential controller of the electromagnetic and hydrostatic closed-loop is designed. Then, the fuzzy control idea is introduced into the single DOF supporting system to design a magnetic-liquid variable proportional fuzzy controller, and the difference between the proportional/variable proportional control modes is compared. Finally, the rotor displacement following characteristics and anti-interference characteristics in the proportional/variable proportional control mode are compared on a magnetic-liquid double suspension test bench. The results show that compared with the proportional control method, the variable proportional control method combines the advantages of fast regulation speed of the electromagnetic system and the stable and reliable advantages of the hydrostatic system. In the same step signal, the rotor can achieve unbiased following under two control modes of proportional/variable proportion, and the adjustment time of the variable proportional control mode is shorter. In two control modes, the rotor can be restored to the original equilibrium position. The maximum offset under the variable proportional control mode is smaller, and with the continuous increase of the displacement, the performance of the variable proportional control method is more obvious. The stability control of MLDSB is provided on the theoretical basis in this article.

Research on hybrid controller utilizing proportional and repetitive control scheme for Permanent Magnet Servo System

Due to the existence of high amount stator current harmonics caused by the inverter nonlinearity, the operation performance of Permanent Magnet Servo System (PMSS) used Proportional–Integral (PI) controller will be affected. To suppress the current harmonics in the PMSS, a hybrid controller utilizing proportional and repetitive control scheme for the current loop is proposed. Firstly, the phase-frequency characteristic and the magnitude–frequency characteristic for the hybrid controller bode diagrams are analysed. To improve the open-loop gain in the frequency band of high-proportion harmonics and compensate the phase lag, the hybrid controller parameter setting method is also studied. Furthermore, the stability and the anti-interference ability of the PMSS used hybrid controller are analysed through Lyapunov theory. Finally, the PMSS model is established through Matlab/Simulink software, and the operation performances of the PMSS utilizing different current loop controllers are studied. The simulation results show that the proposed hybrid controller can suppress the current harmonics and the output torque ripple effectively.

Event-Triggered H∞ Load Frequency Control for Multi-Area Nonlinear Power Systems Based on Non-Fragile Proportional Integral Control Strategy

In this article, a new event-triggered <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$H_{\infty }$ </tex-math></inline-formula> load frequency control (LFC) approach with dynamic triggered algorithm (DTA) for multi-area nonlinear power systems (NPSs) based on non-fragile proportional integral control (NPI-control) strategy is addressed. Firstly, different from the existing linear single-area LFC model for power systems, an improved nonlinear multi-area model with the performance of large-scale adjustment frequency fluctuation is constructed by considering the phenomenon of overshoots and long-term oscillations. Due to the existence of control uncertainty, it is the first time that the NPI-control scheme is applied to LFC approach for NPSs. Then, the DTA is proposed to adjust the dynamic event-triggered parameters, which reduces the occupation of communication bandwidth and the data computation of NPSs. Furthermore, a modified quadratic form with time-varying matrix and two-side closed functional method are adopted to construct the relaxed Lyapunov-Krasovskii functional, where some slack matrices are unnecessarily positive definite. Based on Lyapunov method, some less-conservatism stability criteria are derived. Utilizing the linear matrix inequality toolbox, the allowable upper bound of time-varying delays and the NPI-controller are obtained. Finally, a numerical example is presented to demonstrate the availability of the approach developed in this work.

An Efficient Energy Management Scheme for an Islanded DC Microgrid with Hybrid VRFB System

This paper presents an efficient energy management scheme for an islanded DC microgrid included with vanadium redox flow battery (VRFB) with supercapacitor based hybrid energy storage system for a 3-phase variable load profile. The analysed islanded DC microgrid system is composed with sources such as lithium-ion batteries, fuel cell, supercapacitors, and vanadium redox flow battery (VRFB), along with involved DC/DC (buck, boost, and bidirectional) and DC/DC converters. In this paper, an adaptive proportional plus integral (PI) control based energy management scheme is implemented. The main theme on which the performance of this system is analysed is the state of charge of energy storage devices (VRFB, supercapacitor, and Li-ion battery) and the overall efficiency of system. The voltage spikes due to sudden change in load have been regulated by using supercapacitor and Li-ion battery with VRFB. To minimize the fuel consumption, an adaptive proportional plus integral control strategy is being used. In this paper the modelling and simulations are carried out by using MATLAB/SIMULINK software to demonstrate minimizing the effect of load fluctuations with the help of supercapacitor and batteries by synchronizing it to the DC microgrid.

Modulation of Magnetorheological Fluid Flow in Soft Robots Using Electropermanent Magnets

Magnetorheological (MR) fluids, which stiffen in the presence of magnetic fields, have been shown to be an effective means for controlling the inflation of soft actuators. However, past efforts have largely focused on binary control. Proportional control schemes have faltered due to the difficulty in producing sufficient magnetic fields without requiring large amounts of electrical power. Electropermanent magnets (EPMs) offer one solution to this issue, since they can produce magnetic fields which are similar in magnitude to permanent magnets, they can be controlled electrically, but they do not require any power to hold their state. In this letter, we use EPMs to control the material properties of an MR fluid, allowing us to modulate the pressure within soft actuators. We demonstrate and quantify this behavior for several classes of soft actuators via bending and blocked force testing. We then demonstrate the ability to independently control the actuation of multiple-DoFs systems operating in both a binary and fully-modulated manner, thus providing an important step towards the development of reprogrammable, autonomous soft robots.