Proportional Controller Research Articles

Optimized fractional order PID controller with sensorless speed estimation for torque control in induction motor

With rising concern on environmental protection, the exploitation of EV has been widely focused. The IM is said to be the best in comparing varied electric motors for EV applications due to its lower price and robustness. In addition, sensor-less controlling schemes like DTC and FOC are found to be appropriate for crucial appliances owing to their energy savings, improved efficiency, and reliability. The “Optimized Fractional Order Proportional Integral Controller (FoPID)” with a sensor-less speed estimate mechanism is proposed in this work. Additionally, the parameters of FoPID are adjusted using a brand-new hybrid method called the Wolf based Elephant Herding method (W-EHA), which combines the ideas of Grey wolf optimization (GWO) model and Elephant herding optimization (EHO). The superiority of the recommended technique is finally shown in terms of controller performance and error. The RMSE obtained by the developed model at speed = 100 is 3.344, which is 53.89 %, 24.22 %, 1.09 %, and 38.96 % superior to existing approaches like P-controller, MFI-DA + FoPID, EHO-FoPID, and GWO-FoPID respectively.

Development of renewable energy fed three-level hybrid active filter for EV charging station load using Jaya grey wolf optimization

This work develops a hybrid active power filter (HAPF) in this article to operate in conjunction with the energy storage system (ESS), wind power generation system (WPGS), and solar energy system (SES). It employs three level shunt voltage source converters (VSC) connected to the DC-bus. Optimization of the gain values of the fractional-order proportional integral derivative controller (FOPIDC) and parameter values of the HAPF is achieved using the Jaya grey wolf hybrid algorithm (GWJA). The primary objectives of this study, aimed at enhancing power quality (PQ), include: (1) ensuring swift stabilization of DC link capacitor voltage (DCLCV); (2) reducing harmonics and improving power factor (PF); (3) maintaining satisfactory performance under different combinations of loads like EV charging load, non linear load and solar irradiation conditions. The proposed controller's performance is evaluated through three test scenarios featuring different load configurations and irradiation levels. Additionally, the HAPF is subjected to design using other optimization algorithms such as genetic algorithm (GA), particle swarm optimization (PSO), and ant colony optimization (ACO) to assess their respective contributions to PQ improvement.

Proportional control of a parallel robogami delta mechanism with embedded 2D printed angle sensor feedback

Robogami structures are employed for many purposes, including miniature parallel manipulators. Parallel manipulators require accurate joint position feedback, underlining the open problem in robogami structures for providing sophisticated feedback with 2D, fully embeddable, down-scalable, flexible sensors. Existing studies include only on-off control applications, excluding any parallel mechanism implementations and continuous proportional control signals. We implement silver inkjet printed angle sensors for the first time into a robogami structure and use this class of sensors for control. For the first time, a parallel manipulator is controlled with a 2D sensor, and printed sensors are tested for continuous control. Performances are analyzed with set-point and tracking control tasks using a paper Delta mechanism, compared with on-off control and encoder feedback control cases for better evaluation. A detailed comparison with the existing studies both for robogami parallel mechanisms and other sensor-embedded flexible hinges is provided. Encoder-like control performances are achieved in a wide range of the joint space and down to ~1 mm. RMS task space accuracy is recorded. Sources and potential improvements for the less accurate regions are stated. We conclude that printed sensors are promising for parallel mechanisms and continuous signal control applications.

Design of Active Disturbance Rejection Control for Rehabilitation-Assistant Device of Lower Limbs under Cross Coupling Effect

The robotic exoskeleton is a device which is designed to actuate the lower limbs of human leg for rehabilitation purposes. This device is used instead of physicians in rehabilitation clinics to recover the normal activities of dysfunctional parts. In this study, design of ADRC has been presented to control the motion of hip and knee joints for rehabilitated patients who wearing the exoskeleton device. The key of ADRC approach is to use extended state observer (ESO) for estimating the total uncertainties of the system and to compensate them in feedback-based control law. The proposed control scheme has been designed to reduce the coupling effect due to the cross coupling of upper and lower legs. The stability of ADRC-based system has been proven based on Lyapunov method. The effectiveness of proposed ADRC for hip and joint rehabilitated device has been verified via numerical simulations within MATLAB environment. The tests have been conducted including load and no-load exertion. As compared to proportional derivative controller, the numerical results revealed that the proposed ADRC gives better tracking performance and higher precision while maintaining appropriate control torque.

Tracking control for nonlinear high-order fully actuated system with state constraints: an explicit reference governor approach

This paper studies the tracking control problem of nonlinear high-order fully actuated system subject to state constraints. In order to guarantee the stability of the closed-loop system, a generalised nonlinear proportional differential controller is designed to configure the system into a desired linear constant system. Then, an explicit reference governor for high-order system is introduced to modify the reference signal such that the system state and the state derivatives of certain orders always remain within a prescribed constraint set. Furthermore, we prove that the modified reference will converge to the original reference as much as possible. Therefore, the system state can finally track to the original reference by tracking the modified reference. Two numerical examples demonstrate the validity of the proposed method in this paper.

Active control of thermally induced vibrations of temperature-dependent FGM circular plate with piezoelectric sensor/actuator layers

This study investigates the novel application of active control methods in mitigating thermally induced vibrations of functionally graded material (FGM) circular plates subjected to thermal shock. The top surface of the FGM circular plate is exposed to rapid surface heating, resulting in thermally induced vibrations. To counteract these deflections, two piezoelectric layers, one functioning as a sensor and the other as an actuator, are strategically integrated into the plate opposite sides. This layered configuration effectively distances the direct rapid surface heating from piezoelectric layers. All thermos-electro-mechanical properties of the used materials are considered temperature-dependent. The research employs the Generalized differential quadrature element (GDQE) method and the Crank-Nicolson time marching procedure to solve the nonlinear Fourier-type heat conduction equations for the layered structure. After obtaining the transient temperature profile, the study calculates thermal moments, forces, and pyroelectric terms. These thermally induced effects are then integrated into the motion and electrostatic equations, which inherently exhibit nonlinearity due to the von Kármán large deformations theory. The plate displacements are approximated using the first-order shear deformation theory (FSDT) along the plate thickness, while a quadrature distribution is considered for approximating the electric potential within the piezoelectric layers. The highly coupled electro-mechanical equations are solved using the GDQ method and the Newmark approach, with the Newton-Raphson iterative scheme utilized to address nonlinearity. A significant contribution of this research lies in the exploration of three distinct active control strategies: proportional control, derivative control, and proportional-derivative control. The study thoroughly analyzes the impact of each control type on the plate response, considering various boundary conditions.

Output reachable set synthesis for singular Markovian jump systems with time-varying delay: a PD bumpless transfer control scheme

PurposeThis paper aims to study the issues of output reachable set estimation for the linear singular Markovian jump systems (SMJSs) with time-varying delay based on a proportional plus derivative (PD) bumpless transfer (BT) output feedback (OF) control scheme.Design/methodology/approachTo begin with, a sufficient criterion is given in the form of a linear matrix inequality based on the Lyapunov stability theory. Then, a PD-BT OF controller is designed to keep all the output signs of the system are maintain within a predetermined ellipsoid. Finally, numerical and practical examples are used to demonstrate the efficiency of the approach.FindingsBased on PD control and BT control method, an OF control strategy for the linear SMJSs with time-varying delay is proposed.Originality/valueThe output reachable set synthesis of linear SMJSs with time-varying delay can be solved by using the proposed approach. Besides, to obtain more general results, the restrictive assumptions of some parameters are removed. Furthermore, a sufficiently small ellipsoid can be obtained by the design scheme adopted in this paper, which reduces the conservatism of the existing results.

Lineage motifs as developmental modules for control of cell type proportions

In multicellular organisms, cell types must be produced and maintained in appropriate proportions. One way this is achieved is through committed progenitor cells or extrinsic interactions that produce specific patterns of descendant cell types on lineage trees. However, cell fate commitment is probabilistic in most contexts, making it difficult to infer these dynamics and understand how they establish overall cell type proportions. Here, we introduce Lineage Motif Analysis (LMA), a method that recursively identifies statistically overrepresented patterns of cell fates on lineage trees as potential signatures of committed progenitor states or extrinsic interactions. Applying LMA to published datasets reveals spatial and temporal organization of cell fate commitment in zebrafish and rat retina and early mouse embryonic development. Comparative analysis of vertebrate species suggests that lineage motifs facilitate adaptive evolutionary variation of retinal cell type proportions. LMA thus provides insight into complex developmental processes by decomposing them into simpler underlying modules.

Sliding mode control for gasifier reactor temperature control

This paper presents a sliding mode control (SMC) design for gasifier reactor temperature control. Temperature control is a critical aspect of biomass gasification for quality syngas production. However, accurate modeling and effective control method are the major challenges of gasifier reactor temperature control. The gasification system processes are not yet well understood to model from the first principle due to the complex and nonlinear nature of the process. Also, conventional control methods such as proportional, integral, and derivative control have not been effective in controlling gasification systems. Hence, this work has used experimental data from a 500 kVA updraft gasifier reactor to develop a data-driven mathematical model. The system identification result predicts 86.36% goodness of fit on the model data and 88.26% on the validation data. Discrete time sliding mode control has been designed from the model and implemented in Simulink to investigate the performance of the control method on the gasifier. The result of the system time response shows that the controller effectively drives the temperature monotonically from 25°C to 700 °C in finite time (11.37 minutes). It also establishes a quasi-sliding motion with an ultimate band of ±1℃ for the remainder of the simulation time. Hence, the control technique guarantees optimal temperature control for any feedstock type which will result in higher conversion efficiency, more syngas yield, and improve syngas quality and durability of the gasifier reactor among others.

Improved Compound Vibration Suppression Control for Magnetic Levitation Motor

The magnetic levitation motor is high efficiency, energy saving and environmental protection, which integrates magnetic levitation bearing and permanent magnet motor into the traditional motor. Two key technologies of its stable operation are vibration suppression control of magnetic bearing and speed sensorless chattering suppression control of motor. The innovative research has been studied from the perspective of control systems. First, in order to form the vibration suppression controller, the traditional proportional integral differential control has been improved by adding incomplete differential time constant. In addition, the method using linear variable structure sliding mode observer (SMO) is proposed to achieve speed identification, which can solve the chattering problem caused by the discontinuous high frequency switching signal compared to the traditional SMO. Finally, the experimental platform is built to verify the correctness of the proposed control methods. When the motor operates in speed sensorless mode, the rotor vibration is within the effective range. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

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.

Exponential super-twisting control for nonlinear systems with unknown polynomial perturbations

The study focuses on the control of nonlinear dynamic systems in the presence of parameter uncertainties, unmodeled dynamics, and external disturbances. The lumped perturbation is assumed to be bounded within a polynomial in the system state with the polynomial parameters and degrees unknown a priori such that it accommodates a quite wider range dynamic systems. Based on the studies in recent super-twisting algorithm designs and the idea from adaptive sliding mode control for nonlinear systems with uncertainties, we propose a novel adaptive super-twisting algorithm with exponential reaching law, or exponential super-twisting algorithm (ESTA), for the high-stability and acceptable accuracy control of the aimed nonlinear dynamics. The stability analysis and practical finite-time (PFT) convergence are proven using Lyapunov theory and an intuitive analysis of the control behaviour. Simulations are performed to compare the proposed ESTA with the existing super-twisting method and the traditional proportional integral differential control. The simulation results demonstrate the effectiveness of the proposed ESTA in terms of the fastest settling time and the smallest overshoot.

Optimal state filters for networked iterative learning control systems with data losses and noises

SummaryData losses and noises in both forward and feedback channels significantly impact the convergence of networked iterative learning control (ILC) systems. To address this issue, this article considers a class of linear time‐invariant objects controlled by proportional ILC controllers, an optimal state filter is then designed at the ILC controller side that aims to guarantee the convergence of the input transmitted by ILC controllers. First, two data transmission processes are introduced to account for the effects of data losses and noises. Second, a filtering model is established utilizing only the object information and the aforementioned data transmission processes. Third, the optimal state filter is designed on the basis of the orthogonal projection principle. This filtered state facilitates the acquisition of actual output errors, thus improving the convergence of the input transmitted by ILC controllers. Simulation results demonstrate the effectiveness of the proposed state filters.

Research on unmanned navigation and PID control method for aircraft tractor

Unmanned navigation and control systems are gaining traction in various industries, including aviation. The related issues of control systems are also hot topics in research. This paper comprehensively studies the application of unmanned navigation and the implementation of proportional integral differential (PID) control methods for aircraft tractors. Aiming at the characteristics of large deviation of turning trajectory angle error and long lag time in the process of an unmanned tractor, the dynamic characteristics and state equation of the unmanned tractor are established by the PID method, and the motion equation is established by the Cartesian coordinate system. Finally, the simulation results show that the proposed method has good dynamic stability and rapidity. Compared with proportional control or integral control alone, the PID control system has better stability and a faster frequency response, which can be obtained that the PID control algorithm can effectively improve the work efficiency of the unmanned tractor and reduce its instability and slow response.

Fractional Order Predictive Proportional Integral Control of pH in Effluents of Industrial Plants

Abstract Aim: A robust and advanced controller for pH monitoring and control is necessary in industrial processes inorder to treat the effluents to protect the flora and fauna in the environment. Advanced controllers such as fractional controllers could be used for effective control with increased accuracy and reliability. Materials and Methods: This study includes a comparison of conventional controllers with advanced fractional order controllers to improve the performance of pH control in effluents from the industrial plants. Results: A fractional order predictive proportional integral (FOPPI) controller for effective control of pH was designed and simulated. This controller includes the advantages of a smith predictor for dead time compensation and the robustness of a fractional order controller. The presented method shows an improvement in control performance in terms of rise time (32 s), settling time (140 s), lesser oscillations (2%), and lesser integral of the absolute error of 171. Conclusion: FOPPI provides efficient control of pH in all regions of the titration curve and can be used for the control of pH in industrial waste water.

Design of dual loop controller for boost converter based on PI controller

Boost converters are widely used in industry for many applications, such as electrical vehicles, wind energy systems, and photovoltaic energy systems, to step up the low voltages. Using the topology structure of the DC–DC boost circuit, this paper studied and designed a dual loop control method based on proportional integral controllers for improving the converter efficiency. The inner loop and outer loop controls of the traditional boost circuit are adopted in MATLAB/Simulink software to make the output of the system more stable. The input voltage is set to 24 V DC, and the desired output voltage varies from 36 to 48 V. Through simulation verification, the influence of a 1 kW sudden load connection by using a switch at a nominal output voltage of 48 V DC is studied, and the results show that it reduces the transient output voltage dips during the sudden load connection. Simulation analysis verifies the design scheme of the system, reduces the fluctuation in output voltage and power, reduces the output current ripple, minimizes the dip in voltage to a minimum possible value, and improves the dynamic characteristics and overall efficiency of the converter.

High Performance Wearable Ultrasound as a Human-Machine Interface for Wrist and Hand Kinematic Tracking.

Non-invasive human machine interfaces (HMIs) have high potential in medical, entertainment, and industrial applications. Traditionally, surface electromyography (sEMG) has been used to track muscular activity and infer motor intention. Ultrasound (US) has received increasing attention as an alternative to sEMG-based HMIs. Here, we developed a portable US armband system with 24 channels and a multiple receiver approach, and compared it with existing sEMG- and US-based HMIs on movement intention decoding. US and motion capture data was recorded while participants performed wrist and hand movements of four degrees of freedom (DoFs) and their combinations. A linear regression model was used to offline predict hand kinematics from the US (or sEMG, for comparison) features. The method was further validated in real-time for a 3-DoF target reaching task. In the offline analysis, the wearable US system achieved an average [Formula: see text] of 0.94 in the prediction of four DoFs of the wrist and hand while sEMG reached a performance of [Formula: see text]= 0.60. In online control, the participants achieved an average 93% completion rate of the targets. When tailored for HMIs, the proposed US A-mode system and processing pipeline can successfully regress hand kinematics both in offline and online settings with performances comparable or superior to previously published interfaces. Wearable US technology may provide a new generation of HMIs that use muscular deformation to estimate limb movements. The wearable US system allowed for robust proportional and simultaneous control over multiple DoFs in both offline and online settings.

The research of multi-dimensional flow mapping for proportional flow control valve based on long short-term memory network

The multidimensional flow mapping illustrates the numerical correlation between the flow rate and three pertinent factors, namely spool displacement, valve port pressure difference, and oil temperature. This representation signifies a mapping of three-dimensional inputs to a one-dimensional output. Accurate mapping relationship lays solid foundation for the feasible flow control of the flow control proportional valve. Since the proportional flow control valve is a complex system with high nonlinearity, it is challenging to treat the flow mapping relationship as a simple parameter estimation problem. In this regard, this paper combines the relevant theories of signal processing and deep learning (DL) to propose a novel four-dimensional input-output mapping relationship learning method. This method adopts the long short-term memory (LSTM) network to model the multi-dimensional flow mapping relationship. And for better quality of the data, the initial measurement datasets, contaminated by environmental factors, are processed using a finite impulse response (FIR) filter to reduce noise before training the data. Moreover, the trained model is validated on test datasets. The experimental results shows that the mentioned method can accurately estimate the multidimensional mapping relationship of the proportional flow control valve.

Controlling temperature using proportional integral and derivative control algorithm for hybrid forced convection solar dryer

Drying is one of the crucial processes in agricultural production, especially in grain processing. The drying process can improve grain quality and affect the grain content. However, maintaining the temperature is a challenge in the drying process. Because it can influence the drying performance and produce a low-efficiency reduction of water content, in this study, the hybrid drying system is proposed to improve the performance of the forced convection dryer system. The proposed system used a proportional integral and derivative (PID) control system to obtain the optimal temperature. The proposed system was compared with natural drying and forced convection methods. The experimental result showed that the proposed system performed excellently for three performance evaluations. The average temperature was obtained as the highest of the other methods, with 54.68 °C and 54.55 °C for coffee and cocoa beans. The water content can be reduced by an average of 27.38% and 42.67% for coffee and cocoa beans. Then, the proposed system also had the highest reduction efficiency of water content than the other methods, with 62.71% and 36.94% reductions for coffee and cocoa beans, respectively. The results indicate that the proposed hybrid system performs better than the other methods.

Intelligent residual film recovery machine monitoring and control system

Domestic residue recovery machines in China still follow the traditional towed mechanical model, lacking automation and intelligent features. Under the key R&D project of Xinjiang Autonomous Region, our team has developed integrated intelligent agricultural machinery for cotton straw crushing and residue recovery. This research covers the monitoring and control system, including PLC control and communication, working parameter monitoring and adjustment, algorithmic control of electro-hydraulic actuators, and human-machine interface display. Compared to traditional models, this machine replaces the conventional chain-driven mechanical structure and diesel tractor traction power with a fully hydraulic integrated suspension drive and power system. Sensors, PLC, and a touch screen are utilized for real-time monitoring and display of parameters. Hardware components like electromagnetic switch valves, electro-hydraulic proportional valves, hydraulic cylinders, and software elements such as PLC programs and intelligent control algorithms are employed for electro-hydraulic switching and proportional control of certain working parameters and mechanisms. All parameter information is consolidated in the PLC host and expansion modules and displayed and controlled on the human-machine interface. The system was installed on the first-generation machine for indoor testing, revealing that the monitoring and control system used on the first-generation machine achieves parameter detection and adjustment of the actuating mechanisms. It incorporates more advanced functions compared to traditional residue recovery machines. However, there is still ample room for improvement in the overall intelligence of the machine. The control system’s structure requires adjustment, and certain functionalities of the overall mechanical structure and working components need optimization or upgrading. Detailed enhancements to the entire system will be made in the next-generation machine.