Proportional-integral Algorithm Research Articles

A novel approach to the FPIBSC strategy for an electric power steering system

The electric power steering system is used to improve comfort and steering feel for the user. In this article, we propose to use an integrated nonlinear control strategy for the electric steering system, which is described based on a complicated dynamic model. This work provides two new contributions. First, this control algorithm combines backstepping and proportional–integral (PI) techniques with parameters adjusted by a fuzzy algorithm, so it is called Fuzzy proportional–integral backstepping control (FPIBSC). The output of the PI algorithm is the input of the backstepping technique, while system stability is evaluated based on the Lyapunov function with virtual control variables. Second, road reaction torque is calculated based on a spatial dynamic model, which considers the influence of many other factors. Numerical simulation methods are used to evaluate the performance of the system. According to research findings, the value of the steering motor angle (controlled object) continuously tracks the desired value with negligible error. Under some specific conditions, the error between signals can be reduced to zero. In addition, other outputs that are obtained from the FPIBSC algorithm also tend to follow the reference signal with high accuracy. The phase difference phenomenon only occurs when using the conventional backstepping algorithm instead of FPIBSC. The assisted torque increases as speed decreases or the driver torque increases. In general, the system’s stability is always guaranteed under many different simulation conditions.

Method of test for CO2-based demand control ventilation systems: Benchmarking the state-of-the-art and the undervalued potential of proportional-integral control

Carbon dioxide (CO2) based demand control ventilation (DCV) adjusts a building’s outdoor air ventilation rate in response to indoor CO2 concentration to save energy while maintaining indoor air quality. Packaged heating, ventilation, and air-conditioning systems often contain DCV controllers with embedded proprietary algorithms that lack transparent performance data. A test method was developed to assess the ability of a DCV controller to maintain the indoor CO2 concentration at a setpoint in response to a series of CO2 generation functions that represent three different building occupancy densities and two occupancy schedules. Six commercially available controllers were tested to demonstrate the method and provide directly comparable results. The performance (in terms of CO2 control and damper movement) of each controller tested was compared to the performance of an ideal controller which knows the CO2 generation function. Finally, the performance of a proportional-integral (PI) controller with preset gains was developed and tested to determine the potential maximum performance achievable with this control strategy. The best performing commercially available controller achieved CO2 control (within 75 ppm of the setpoint) approximately 80 % of the time with damper movement slightly less than an ideal controller. However, most of the commercially available controllers had marginal or poor performance for CO2 control and damper movement. Two controllers had damper movement more than three times the ideal controller. Notably, a PI algorithm configured and tested by the research team achieved superior performance with CO2 control 92 % of the time and damper movement 1.5 times the ideal controller.

A novel approach with a fuzzy sliding mode proportional integral control algorithm tuned by fuzzy method (FSMPIF)

An automobile's vibration can be caused by stimulation from the road's surface. The change in displacement and acceleration values of the sprung mass is used to evaluate the automobile's vibration. Utilizing an active suspension system is recommended in order to attain an increased level of ride comfort. This article presents a novel strategy for regulating the operation of an active suspension system that has been put up for consideration. The PI (Proportional Integral) algorithm, the SMC (Sliding Mode Control) algorithm, and the Fuzzy algorithm served as the basis for developing the FSMPIF algorithm. The signal generated by the SMC algorithm is what is used as the input for the Fuzzy algorithm. In addition, the settings of the PI controller are modified with the help of yet another Fuzzy algorithm. These two Fuzzy methods operate independently from one another and in a setting that is wholly distinct from one another. This algorithm was created in a wholly original and novel way. Using a numerical modelling technique, the vibration of automobiles is investigated with a particular emphasis on two distinct usage situations. In each case, a comparison is made between four different circumstances. Once the FSMPIF method is implemented, the results of the simulation process have demonstrated that the values of displacement and acceleration of the sprung mass are significantly decreased. This was determined by looking at the values before and after implementing the new algorithm. In the first case, these figures do not surpass a difference of 2.55% compared to automobiles that use passive suspension systems. The second case sees these figures falling short of 12.59% in total. As a direct result, the automobile's steadiness and level of comfort have been significantly improved.

Non-integer Order Control Scheme for Pressurized Water Reactor Core Power

Tracking load changes in a pressurized water reactor (PWR) with the help of an efficient core power control scheme in a nuclear power station is very important. The reason is that it is challenging to maintain a stable core power according to the reference value within an acceptable tolerance for the safety of PWR. To overcome the uncertainties, a non-integer-based fractional order control method is demonstrated to control the core power of PWR. The available dynamic model of the reactor core is used in this analysis. Core power is controlled using a modified state feedback approach with a non-integer integral scheme through two different approximations, CRONE (Commande Robuste d'Ordre Non Entier, meaning Non-integer order Robust Control) and FOMCON (non-integer order modeling and control). Simulation results are produced using MATLAB® program. Both non-integer results are compared with an integer order PI (Proportional Integral) algorithm to justify the effectiveness of the proposed scheme. Sate-space model Core power control Non-integer control Pressurized water reactor PI controller CRONE FOMCON.

A Real-Time Hardware Experiment Platform for Closed-Loop Electrophysiology.

Targeted stimulation of nervous system has become an increasingly important research tool as well as therapeutic modality, and the stimulation signal acquisition based on the expected signal needs a closed-loop system. Due to the difficulty of biological experiments, the real-time simulation of neural activity is of great significance for the mechanism analysis and the performance improvement of neuromodulation techniques. This paper proposes a real-time hardware experimental platform for closed-loop electrophysiology. The platform integrates a neural computing module and a real-time control module on TMS320F28377D digital signal processors (DSP), and it reserves a programmable interface for users to call the required modules and set module parameters simultaneously. The platform has high compatibility and can be used for closed-loop electrophysiological experiments with different models, different control algorithms and different clamps. We implement the thalamocortical relay neural computing model and iteration improves proportional-integral algorithm on the platform for experimental verification in this paper. The neuron firing waveforms of the DSP platform and the MATLAB R2020b simulation waveforms are consistent. Under the same physiological time, the simulation speed of DSP platform is 3 times faster than that of the Intel Core i5-8400 CPU computer, and the neural firing rate of DSP platform is due to the real-time. This platform can be used as a tool to explore the working mechanism of the nervous system. It may promote the development of neuroscience, especially the field of closed-loop neuroscience.

PI control based on parameter space approach supported metaheuristic optimization algorithms and ANFIS in a natural gas combined cycle power plant

AbstractIn this article, proportional‐integral (PI) control to ensure stable operation of a steam turbine in a natural gas combined cycle power plant is investigated, since active power control is very important due to the constantly changing power flow differences between supply and demand in power systems. For this purpose, an approach combining stability and optimization in PI control of a steam turbine in a natural gas combined cycle power plant is proposed. First, the regions of the PI controller, which will stabilize this power plant system in closed loop, are obtained by parameter space approach method. In the next step of this article, it is aimed to find the best parameter values of the PI controller, which stabilizes the system in the parameter space, with artificial intelligence‐based control and metaheuristic optimization. Through parameter space approach, the proposed optimization algorithms limit the search space to a stable region. The controller parameters are examined with Particle Swarm Optimization based PI, artificial bee colony based PI, genetic algorithm based PI, gray wolf optimization based PI, equilibrium optimization based PI, atom search optimization based PI, coronavirus herd immunity optimization based PI, and adaptive neuro‐fuzzy inference system based PI (ANFIS‐PI) algorithms. The optimized PI controller parameters are applied to the system model, and the transient responses performances of the system output signals are compared. Comparison results of all these methods based on parameter space approach that guarantee stability for this power plant system are presented. According to the results, ANFIS‐ PI controller is better than other methods.

Distributed Robust Secondary Control of Islanded Microgrids: Voltage, Frequency, and Power Sharing

In this paper, a novel distributed robust secondary control is proposed to restore both voltage and frequency in islanded inverter-interfaced microgrids (μGs). The proposed method is superior particularly in the disaster ex-post resilient islanded operation of μGs where various unknown disruptions and uncertainties exacerbate the successful operation of conventional control schemes. Accordingly, the proposed control scheme is devised so as to accommodate the nonlinearity of μG and non-ideality of communication networks such as time-varying delays, data packet losses, and link failures. For μG voltage control, a distributed robust algorithm based on a model predictive control is developed. To do so, the Lyapunov-Razumikhin functional is exploited to analyze the stability of the proposed method, and sufficient conditions are then speculated by the linear matrix inequalities technique. For μG frequency restoration and achieving accurate active power sharing, a finite-time distributed robust proportional-integral algorithm is presented. The effectiveness of the proposed method is verified by inclusive simulation studies of an islanded μG in MATLAB/ Simulink.

Improved Event-Based Pi Controller for Limit Cycles Avoidance

One of the issues in designing event-based proportional-integral (PI) controller using aggressive tuning rules is the possible occurrence of limit cycles. To date, there is no adequate simple event-based PI controller technique able to explicitly use aggressive tuning rule. In this paper an improved simple event-based PI controller is proposed to address this issue. By analysing the discrete PI algorithm, an improved triggering condition is introduced. To test the effectiveness of the approach, extensive simulations are carried out by introducing the proposed method to process control under various sampling period, triggering limit, and different tuning rules namely, AMIGO, SIMC and One-Third tuning rules. The performances are evaluated based on two standard criteria: ability to imitate the time-triggered system and computation load reduction. The results show that the performance of the proposed method able to surpass others simple event-based PI controller approaches by giving a closest response to the time-triggered system, lowest computational load and able to avoid the limit cycles occurrences. It is envisaged that the proposed method can be useful in designing a simple event-based PI controller that compatible with any type of tuning rules.

Design and Verification of the LQR Controller Based on Fuzzy Logic for Large Wind Turbine

In this study, a linear quadratic regulator based on the fuzzy logic (LQRF) control algorithm for a variable-speed variable-pitch wind turbine was designed. In addition, to verify the optimum performance of the controller, simulations and wind tunnel tests were conducted. In the simulation, the performances of the proportional-integral (PI) and LQRF algorithms were compared in the transition region and the rated power region. In the wind tunnel test, the applicability of the LQRF algorithm was verified by comparing it with the conventional PI algorithms. The results showed that when compared with the PI control, the proposed LQRF control reduced the tower vibration by up to 12.50% depending on the operating region. Furthermore, the power deviation was reduced by 38.93%. These tests confirmed that the proposed LQRF control increases the power performance and structural stability of wind turbines compared with conventional PI controls.

A Self-Tuning Congestion Tracking Control for TCP/AQM Network for Single and Multiple Bottleneck Topology

In this work a self-tuning rate and queue based proportional and integral controller called SRQ-PI is proposed to efficiently control the queue length with small overshoot and faster settling time. SRQ-PI proposes a new control tracking function that maps level of congestion to the packet drop probability dynamically. In SRQ-PI, the incoming traffic rate is estimated and used with the proportional and integral controller. The SRQ-PI tunes itself and stabilizes the system with internal feedback without requiring any external feedback. Furthermore, the stability of the SRQ-PI is analyzed using control theory and presents systematic guidelines to select the control gain parameters. NS2 is used to carry out the simulation work. The simulation result demonstrates that SRQ-PI is stable and gets faster transient response due to lower average delay jitter and robust against dynamic network parameters. The SRQ-PI outperforms proportional integral (PI), Intelligent adaptive PI (IAPI) and Random exponential marking (REM) algorithm.

Proportional–integral‐like fuzzy controller of a small‐scale linear fresnel reflector solar plant

This study reports on the development and implementation of the fuzzy incremental controller on a small-scale linear fresnel reflector solar plant. The control problem is concerned with forcing the output temperature to follow the reference, despite the existence of disturbances. Owing to the non-linear effects of solar plants, the capability of fixed parameters of the proportional–integral controller to cope with the control problem is limited and leads to unsatisfactory control performance. The proposed controller is a modification to a conventional proportional–integral algorithm with better tuning flexibility. Firstly, the ant colony optimisation algorithm is used to define the optimal parameter of proportional–integral plus series feed-forward controller while guaranteeing a satisfactory performance of the plant. Secondly, the resulting controller is replaced with an equivalent fuzzy knowledge-based controller with the error and change of error of the plant as the input variables and flow rate as an output variable. The proposed controller is tested on a quasi-dynamic model of linear fresnel reflector plant using conventional and renewable energy optimisation toolbox in the environment of MATLAB/Simulink. The controller's performance is compared to the proportional–integral controller, where its effectiveness is evaluated under nominal conditions, measurement delay, noise, and presence of disturbance.

Research on brushless DC motor control system based on fuzzy parameter adaptive PI algorithm

As a complex system with multiple variables, nonlinearity, and strong coupling, the BLDCM (Brushless Direct Current Motor) has many problems, such as bad parameter tuning, poor adaptability, low control accuracy, and weak anti-interference ability by using the double closed loop traditional PI (Proportional Integral) control algorithm. In order to obtain good control performance, a fuzzy parameter adaptive PI algorithm based on speed loop was designed by combining fuzzy control with traditional PI control. This paper analyzes the mathematical model and operating characteristics of the BLDCM and designs a fuzzy system that takes the deviation e and deviation change rate ec of the reference speed and feedback speed as input and takes the corresponding PI adjustment parameters as output. The step response of the BLDCM at different reference speeds is analyzed. The variable speed response with the initial speed of 4000 r/min under different control algorithms and the changes in the three-phase current, back electromotive force, and electromagnetic torque in this state are compared. The results show that the designed fuzzy parameter adaptive PI algorithm based on the speed loop can make the motor have a faster response time, a smaller overshoot, and a steady-state error when the motor achieves the stable operation. The proposed algorithm also has better control effect, robustness, and stable operation under variable speed conditions.

Energy-efficient decentralized control method with enhanced robustness for multi-evaporator air conditioning systems

Multi-evaporator air conditioning system has found increasing applications in buildings. Energy-efficient control for this kind of system presents significant challenge because of strong coupling effects and various operation modes. Existing control methods are most likely to encounter uncontrollable problems and lack robustness under uncertain operation conditions. This paper proposes an energy-efficient decentralized control method with enhanced robustness for multi-evaporator air conditioning system. The proposed method is featured by a self-optimizing control strategy and a decentralized proportional-integral (PI) control algorithm based on the effective open-loop transfer function (EOTF). Different from most of previous studies that regulated compressor speed to control the evaporating pressure by using a supervisory optimizer, the proposed self-optimizing control strategy regulates compressor speed to maintain constant suction superheat and achieves energy-efficient operation in a simple and robust manner. Meanwhile, the EOTF-based decentralized PI algorithm overcomes the weakness of decentralized controllers in dealing with coupling effects, while retaining robustness against model errors and component faults. Control performance of the proposed method is tested using a validated system dynamic model. Through extensive controllability tests, it is found that the proposed control method significantly improves temperature control performance of the conventional strategy. Up to 8.6% energy savings are achieved with the proposed control strategy. Moreover, the proposed EOTF-based PI controller demonstrates enhanced robustness in comparison with an advanced model predictive controller.

基于模糊自整定比例积分算法的电动汽车巡航控制

基于模糊自整定比例积分算法的电动汽车巡航控制

Estimation of deformation for a glass sheet on a noncontact air conveyor with film pressure feedback

Recently, air film conveyors equipped with porous pads have been developed to transport large, thin glass substrates in a contactless state. The deformation of the glass substrates should be monitored for the inspection process on the production line. Although laser sensors can be applied to detect the deformation along a given path, there are some problems such as poor efficiency and low real-time performance. For the purpose of estimating the deformation of the floating glass sheet indirectly, in this paper a film pressure feedback method is proposed and studied via theoretical analysis and experimental verifications. First, a theoretical model including the characteristics of the porous media and gap flow is established, and the model is solved by the finite volume method to obtain the film pressure distribution. Then, an experimental apparatus is built to measure the film pressure with different degrees of deformation of the glass sheet. Comparisons of the calculated film pressure with the measured data indicate that deviations gradually appear for the outer units as the deformation degree enlarges. Thus, we use four inner pressure points as the feedback and apply a proportional–integral algorithm to adjust the deformation shape in terms of a quadratic curve. Furthermore, we verify the method via a deformation measurement experiment. We find that the estimation method can afford an accuracy of 3% in cases where the glass sheet is symmetrically distributed, or asymmetrically distributed but with an asymmetry coefficient of less than 0.2. Finally, we find that the realistic boundary conditions, i.e. the film pressure in the groove and the edge deflection, greatly affect the estimation results, and an allowable measurement error to ensure adequate accuracy of the estimation is provided.

Adaptive Current Controller Based on Neural Network and Double Phase Compensator for a Stepper Motor

In this paper, we propose an advanced approach to improve the accuracy of stepper motors based on an effective phase compensator. The proposed approach includes an adaptive current controller and an adaptive position controller. The proposed adaptive feed-forward proportional-resonant (AFPR) current control technique with a neural network (NN) is an improvement over the conventional proportional-integral algorithm. In which, the NN algorithm is applied to obtain the optimal parameters for the AFPR controller. This results in an enhancement of the stepper motor current tracking. In addition, the advanced position controller is a combination of a damping and a phase-compensated (D&PC) method to reduce steady-state error and variations during low-speed operation. The position tracking performance is further appreciated by applying an advanced proportional integral derivative (PID) controller during high-speed operation. A bandpass filter is designed to switch the motor between low- and high-speed operational regimes. The proposed approach does not require any changes in the structure of the hardware. Therefore, it can be widely applied to a diverse set of industrial applications. The performance of the proposed approach was validated by conducting experiments under practical conditions.

Application of fuzzy logic in sensorless induction motor drive with PWM-DTC

The paper deals with the application of fuzzy logic in the current-based model reference adaptive system (CB-MRAS) for sensorless induction motor drives using direct torque control with pulse-width modulation. The stability analysis of the selected observer is described in the first part. Fuzzy logic is used to adjust the parameters of proportional–integral (PI) adaption algorithm of the CB-MRAS observer. The description of a laboratory workplace with an induction motor drive and load system is presented in the third part. Comparisons of conventional PI and fuzzy logic-based adaptation algorithms are shown in the paper. The theoretical assumptions of the sensorless induction motor drive are confirmed by the simulation and experimental results.

Design and Simulation of an LQR-PI Control Algorithm for Medium Wind Turbine

In this paper, a new linear quadratic regulator (LQR) and proportional integral (PI) hybrid control algorithm for a permanent-magnet synchronous-generator (PMSG) horizontal-axis wind turbine was developed and simulated. The new algorithm incorporates LQR control into existing PI control structures as a feed-forward term to improve the performance of a conventional PI control. A numerical model based on MATLAB/Simulink and a commercial aero-elastic code were constructed for the target wind turbine, and the new control technique was applied to the numerical model to verify the effect through simulation. For the simulation, the performance data were compared after applying the PI, LQR, and LQR-PI control algorithms to the same wind speed conditions with and without noise in the generator speed. Also, the simulations were performed in both the transition region and the rated power region. The LQR-PI algorithm was found to reduce the standard deviation of the generator speed by more than 20% in all cases regardless of the noise compared with the PI algorithm. As a result, the proposed LQR-PI control increased the stability of the wind turbine in comparison with the conventional PI control.

Power Allocation Based on Proportional-Integral Controller in Femtocell Networks With Consideration of Maximum Power Constraint

In this paper, a two-tier uplink femtocell network operating in orthogonal frequency-division multiple access is investigated. Femtocell base stations endowed with cognitive capacity can sense the void spectrum of macrocell. We aim at an energy-saving scheme of power allocation for femtocell users. The new idea that performance of Femtocell networks can be analyzed using control theory is proposed in this paper. A simple and novel power control algorithm is designed based on the virtual Proportional-Integral (PI) controller. When considering the maximum power constraints, different from the proof of the convergence via standard interference functions, we establish a linear system with state saturation (maximum power constraint) for femtocell networks, and the power's convergence is verified in theory with iterative linear matrix inequality algorithm. Two introduced control parameters not only guarantee the stability of power control algorithm, but also can adjust the transient response and save energy. Numerical results illustrate the convergence performance and advantages of the PI algorithm.

Distributed Model Predictive Secondary Voltage Control of Islanded Microgrids With Feedback Linearization

This paper presents a novel distributed secondary control method for both voltage and frequency regulation in islanded microgrids. Firstly, the large-signal dynamic model of inverter-interfaced distributed generation (DG) is formulated in the form of a multi-input multi-output nonlinear system, which can be converted to a partly linear one using input–output feedback linearization. Then, the linear-distributed model predictive controller is designated in each DG to realize the secondary voltage control by incorporating the forecasted behaviors of the local and neighboring DG units. Through the receding optimization index of every update process, the implementation of optimal control action accelerates the convergence rate for voltage magnitudes to the reference value. Following, after transforming the nonlinear DG dynamics into a first-order linear system, a distributed proportional integral algorithm is introduced in the frequency restoration while maintaining the accurate active power sharing. Our approach utilizes the distributed architecture, which indicates superior reliability and flexibility compared to the centralized manner; moreover, it can accommodate diverse uncertainties in communication links, model parameters, and time delays. Simulation results are provided to verify the effectiveness of the proposed control methodology.