Proportional Integral Differential Controller Research Articles

Microwave Passive Direction-Finding Method Based on NV Color Center in Diamond

In this study, we established a passive direction-finding scheme based on microwave power measurement: Microwave intensity was detected using microwave-frequency proportion integration differentiation control and coherent population oscillation effect converting the change in microwave resonance peak intensity into a shift of the microwave frequency spectrum, for which the minimum microwave intensity resolution was −20 dBm. The direction angle of the microwave source was calculated using the weighted global least squares method of microwave field distribution. This lay in the 12~26 dBm microwave emission intensity range, and the measurement position was in the range of (−15°~15°). The average angle error of the angle measurement was 0.24°, and the maximum angle error was 0.48°. In this study, we established a microwave passive direction-finding scheme based on quantum precision sensing, which measures the microwave frequency, intensity, and angle in a small space and has a simple system structure, small equipment size, and low system power consumption. In this study, we provide a basis for the future application of quantum sensors in microwave direction measurements.

Self-organizing modeling and control of activated sludge process based on fuzzy neural network

The wastewater treatment process contains multiple complex biochemical reactions featured by strong nonlinear and time-varying dynamics due to the built-in discontinuity and uncertainty. Herein, a self-organizing fuzzy neural network with an efficient scheme for parsimonious (SOFNN-ESP) was orchestrated to improve the self-organizing modeling of municipal wastewater treatment process by combing predictive algorithms to deal with the complex water treatment procedure. The SOFNN-ESP algorithm could identify sewage treatment plants by a high-throughput parameter screening system and recursive least square method in real-time, which provided dynamic setting feedback and promoted water quality. The integration of the SOFNN-ESP algorithm and a model predictive control (MPC) further improved the accuracy in water quality controlling via immediately adjusting weight parameters of the network. This gradient algorithm also realized the online dynamic tracking of dissolved oxygen and nitrate nitrogen level by simultaneous tracking of multiple performance indicators and optimizing setting values of the control variable. SOFNN-ESP-MPC gave an error of <5 % when the peak error of proportional integral differential controller was >10 %. Concerning the benchmark simulation model No.1 of municipal wastewater treatment, the SOFNN-ESP-MPC method exhibited a compact network structure and outstanding generalization performance. The self-organizing modeling and predictive control strategy proposed in this study could effectively improve the prediction accuracy and control efficiency of the activated sludge process model, which is of great significance for the efficiency improvement of sewage treatment process.

Active Disturbance Rejection-Based Performance Optimization and Control Strategy for Proton-Exchange Membrane Fuel Cell System

In this paper, to maximize the net output power and realize better performance optimization and control of the oxygen excess ratio, a complete dynamic model of the proton-exchange membrane fuel cell system is developed and an active disturbance rejection control strategy is proposed. The active disturbance rejection control drives the uncertainties and perturbations of the system to an extended state, which is predicted and eliminated by real-time input–output data. The simulation results indicate that, compared with the proportion–integral–differential and fuzzy proportion–integral–differential control, the active disturbance rejection control strategy can effectively improve the control performance with a lower control cost and less wear on the compressor, and the integral absolute error of the oxygen excess ratio control is reduced by up to 50%. In addition, the output voltage is improved and the power generation efficiency of the proton-exchange membrane fuel cell under the active disturbance rejection-based oxygen excess ratio control is 1.84% and 0.95% higher than that of the proportion–integral–differential and fuzzy proportion–integral–differential control, respectively. Moreover, the proposed optimal-reference control strategy increases the net power by up to 1.85% compared with the fixed-reference control strategy.

Deep reinforcement learning method for turbofan engine acceleration optimization problem within full flight envelope

In order to solve the multidimensional restricted optimization problem of aeroengine acceleration process and improve the acceleration performance of full envelope, a design method of aeroengine full envelope acceleration controller based on deep reinforcement learning is proposed. Firstly, the thermo-aerodynamic principle of the engine acceleration process is analyzed, and the mathematical analytical model of the multidimensional restricted optimization problem of the turbofan engine acceleration process is established. On this basis, the design method of acceleration controller based on twin delayed deep deterministic policy gradient is studied. The proportional integral controller is designed to pursue the asymptotic stability at the acceleration destination and stable control is achieved through switching. Additionally, the flight envelope is divided into regions by clustering to reduce the span of data changes, so as to enhance the network convergence ability of deep reinforcement learning. Finally, based on the similarity conversion method of equal engine inlet total temperature, the acceleration controller of full flight envelope is realized. The digital simulation results demonstrate that compared with the traditional proportional integral differential controller, the acceleration controller based on twin delayed deep deterministic policy gradient shortens the acceleration time up to 48.33%, and the control effect in a large flight envelope (H=0∼10 km, Ma=0∼1.6) has been verified.

Isokinetic Muscle Strength Training Strategy of an Ankle Rehabilitation Robot Based on Adaptive Gain and Cascade PID Control

Isokinetic muscle strength training refers to the mode of movement based on constant speed and variable resistance, which can guarantee the maximum resistance and force–distance output of each muscle in different angles of exercise. In this article, we develop an isokinetic muscle strength training strategy based on the adaptive gain and cascade proportion integration differentiation (PID) controller for ankle rehabilitation on our newly developed ankle robotic system. First, a heuristic threshold intention recognition method based on time-series integration was proposed to precisely recognize the motion intention, thus inducing the motion in this direction. Then, an adaptive gain algorithm was developed to provide speed gain for the isokinetic training, to avoid jitter during speed switching. Finally, the isokinetic characteristic was realized by the cascade PID controller, which can control the motion velocity in a given range with fast response speed. Experiments with healthy subjects showed good performance in the smoothness of the control system, the accuracy, and the real-time performance of velocity tracking. By introducing the isokinetic characteristic in ankle rehabilitation, the ankle robot can provide resistance training at a constant speed no matter how much force the patient uses, which is a very functional supplement and improvement for ankle rehabilitation.

Intelligent controller for unmanned surface vehicles by deep reinforcement learning

With the development of the applications of unmanned surface vehicles (USVs), USV automation technologies are attracting increasing attention. In the industry, through the subtask division, it is generally believed that course-keeping is a critical basic sub-system in a series of complex automation systems and affects USV automation performance to a great extent. By course-keeping, we mean USV adjusts its angle to the desired angle and keeps it. In recent decades, course-keeping has been mainly achieved through classical first principles technologies, such as proportion–integral–differential (PID) controllers, leading to extremely laborious parameter tuning, especially in changeable wave environments. With the emergence and extensive application of data-driven technologies, deep reinforcement learning is conspicuous in sequential decision-making tasks, but it introduces a lack of explainability and physical meaning. To take full advantage of the data-driven and first principles paradigm and easily extend to the industry, in this paper, we propose an intelligent adaptive PID controller enhanced by proximal policy optimization (PPO) to achieve USV high-level automation. We then further verify its performance in path-following tasks compared with the PID controller. The results demonstrate that the proposed controller inherits the merits of explainability from PID and excellent sequential decision making from PPO and possesses excellent disturbance rejection performance when facing the disturbance of a changeable wave environment.

Design of Plc-based Pipe Pile Residual Slurry Recovery Equipment Control System

To solve the cost loss caused by the low recovery efficiency of pipe pile residual slurry, as well as the pollution of the environment during discharge, a control system of pipe pile residual slurry recovery equipment is designed. The equipment is controlled by PLC (Programmable Logic Controller), aiming at the problem that the traditional PID (Proportion Integration Differentiation) cannot modify the parameters in real time to meet the accuracy requirements of the residual slurry density. The fuzzy PID controller algorithm is proposed, and the simulation analysis is carried out by MATLAB at the same time. Through PLC selection, PLC I/O allocation, electronic control system design, program writing, and HMI (Human Machine Interface) establishment, the information interaction is achieved between equipment and people while monitoring the production process. Through on-site production tests, the equipment is proved to be reliable and efficient, which can make the residual slurry recovery utilization rate reach more than 90%, and reduce the production cost of pipe piles.

Fuzzy gain scheduling PID control of a hybrid robot based on dynamic characteristics

In this paper, a fuzzy gain scheduling proportional-integral-differential (PID) controller based on dynamic characteristics of a hybrid robot named TriMule is presented. Since the equivalent inertia and gravity imposed on motor shafts vary with the robot configuration, it is difficult to obtain satisfactory control performance with a linear, fixed gain PID controller. After analyzing the effect mechanism of dynamic characteristics on control performance, the clustering analysis algorithm and fuzzy logic are combined to deduce the control parameters online according to the command acceleration, equivalent inertia and gravity. The experimental results on a prototype machine show that, compared with the conventional PID and fuzzy gain scheduling PID controller, the proposed method can significantly reduce the effect of dynamic characteristics on control system, thus ensuring satisfactory control performance under any configuration in the task workspace.

Active tension control of the conical winding system based on the neural network control algorithm of radial-basis functions

A conical winding formation and tension control system was proposed in the doubling operation based on the yarn guide mode of a single spindle in this study. Conical winding formation realized the radial unwinding of wound package. An overfeed mechanism was introduced to achieve closed-loop control of yarn tension. The overfeed wheel was driven by a brushless Direct Current motor. The tension control system combined a Proportion Integration Differentiation controller with a radial-basis-function neural network, whose purpose was to meet the control requirements of the brushless DC motor. This system consisted of three main steps: Firstly, the radial-basis-function neural network was used to identify the system online. Secondly, the gradient descent method was used to adjust the node weight, center vector, and baseband width. Finally, incremental PID parameters online were adjusted according to the identified Jacobian information. A mathematical model of a control system was established in Matrix Laboratory. An experimental platform was designed for doubling winder to compare the control effects of Radial-basis-function-PID with traditional PID. The simulation results showed that the RBF-PID had a smaller overshoot of yarn tension, shorter adjustment time, and smaller steady-state error compared with the traditional PID controller in doubling operation by simulating the mathematical model. Experimental results showed the RBF-PID controller had good performance and stability and could be applied to yarns with different average linear velocity, yarn counts and strands . The yarn tension fluctuation will not exceed ±3% of the target value when the experimental materials and the cone angles are unchanged.

Design and Experiments of a Two-Stage Fuzzy Controller for the Off-Center Steer-by-Wire System of an Agricultural Mobile Robot

This paper focuses on the steering motion control of an in-wheel motor-drive robot. The influence of the pulse-width modulation (PWM) duty cycle on steering motion and the steering control method have not yet been proved. Thus, this study aimed to design a steering controller for the off-center steer-by-wire system of a robot. The influence of the PWM duty cycle on the steering motion under different conditions is firstly tested on a test bench. Based on the optimal duty cycles of different cases found in the test, a two-stage fuzzy controller of the duty cycle is designed for the steering system. The first stage of the controller is used to dynamically adjust the PWM duty cycle of the electromagnetic friction lock (EFL). The second stage is designed to realize the self-tuning of the fuzzy controller’s quantization factor and the scale factor. Through two-stage control, the motion of the in-wheel motor and the EFL can be coordinated to realize stable and rapid steering. Considering the robots’ primary application in field roads at present, road tests were ultimately conducted to verify the proposed method. The test results show that the angle response rate of the steering arm is elevated with the increase in the steering angle signal. The proposed controller can sensitively track the target angles with smaller overshoot, yaw rate and lateral acceleration, and better steering accuracy than the PID (proportional–integral–differential) controller under different working conditions.

Research on Structure Design and Control Method of Magnetorheological Suspension Based on Improved Fruit Fly Optimization Algorithm

To reduce engine vibration, a semi-active controlled magnetorheological suspension is designed. First, the magnetic circuit of the new magnetorheological suspension is simulated. Furthermore, the non-parametric forward and inverse dynamic models of the magnetorheological suspension with the Elman neural network are established, where the modeling results and accuracy are analyzed. Second, according to the characteristics of semi-active vibration control of engine suspension, an improved fruit fly optimized proportional-integral-differential (PID) controller is proposed. Compared with the original fruit fly optimized PID controller and particle swarm optimization PID controller, the control strategies based on the proposed methods are simulated by MATLAB. Then, a vibration control experiment is carried out under different control strategies. The simulation and experiment results show that the PID controller based on the improved fruit fly optimization algorithm has the advantages of short adjustment time and fast dynamic response.

Adaptive swing reduction control of double-pendulum tower crane time-varying system based on real-time update mutation factor

As representative underactuated and nonlinear systems, tower crane involves the multi-direction movement of the payload and even shows a more complex double pendulum in many actual situations, which makes the control problem more complicated. Most anti-swing controllers are mainly based on the linear design of tower crane dynamics at present and are insensitive to the position far from the equilibrium point. The time-varying cable length and double-pendulum effect are not taken into account simultaneously. Aiming at the problem of anti-swing control, a dynamic model of time-varying cable length tower crane with double-pendulum effect is established and an adaptive control method is proposed, which used the adaptive mutation strategy to update the mutation factor in real-time, and the parameters of the controller are tuned by an improved differential evolutionary algorithm. The simulations and experiments are carried out to verify the better optimization ability and faster convergence speed of the improved evolutionary algorithm in the process of parameter tuning and the effectiveness and superiority of the proposed control method are verified by comparing it with PID (proportion integration differentiation) and LQR (linear quadratic regulator) controllers.

Curvature Tracking of a Two-Segmented Soft Finger Using an Adaptive Sliding-Mode Controller

The emergence of soft robotics in the last decade has brought about the exponential usage of elastomer bending robots in numerous fields of expertise from prosthetic hands to industrial grippers. While considerable attention has been drawn to static grasp, a substantial gap can be recognized in the development of soft grippers and anthropomorphic hands with the ability to manipulate objects dynamically, an ability that is referred to as in-hand object manipulation. In order to fill this gap, efficient and practical approaches should be investigated for the fabrication, modeling, and control of soft robots that are computationally efficient and capable of yielding repeatable results. Accordingly, in this article, qualified approaches for the dynamic modeling and control of a soft bending finger are introduced that can be efficiently implemented for planar in-hand manipulation applications in soft robotics. In this regard, a novel 3-D-printed mold is designed to cast a uniformly fabricated soft finger consisting of two independently actuated bending segments. After deriving the dynamic model of the soft finger and identifying its dynamic parameters, an adaptive sliding-mode controller is proposed to guarantee the convergence of the bending angles to desired trajectories under dynamic uncertainties and external disturbances. In the experimental validations, the root-mean-square errors of the proposed controller are 0.0323 and 0.0303 rad/m in free motion for each segment, while these numbers are 0.0328 and 0.0226 rad/m for constrained motion. For a comparative evaluation of the proposed controller, the results of the proportional–integral–differential controller are demonstrated under similar circumstances.

On disturbance rejection proportional–integral–differential control with model-free adaptation

On disturbance rejection proportional–integral–differential control with model-free adaptation

Optimal design and stability control of an automotive magnetorheological brake considering the temperature effect

Magnetorheological brake (MRB) provide a potential alternative to traditional mechanical friction brakes in automobile applications owing to their technical advantages in terms of being compact yet powerful, having superior control performance, and wire-control features. However, the temperature effect has been an important issue that should be considered in the design and precise control of MRB. This paper presents the multi-objective optimal design and stability control of an automotive MRB considering the temperature effect. First, a description of the configuration design, magnetostatic field simulation, and mathematical modeling of the automotive MRB is presented in detail. Subsequently, design optimization of the MRB is carried out. The design of experiment method was adopted to screen out the major design variables, and the optimal solution was obtained using a multi-objective genetic algorithm (GA) non-dominated sorting genetic algorithm (NSGA-II). Thereafter, the torque output and response performance, as well as the temperature characteristics of the optimal MRB prototype are experimentally evaluated. The results indicate that the optimal design of the MRB was reasonable and effective. Finally, a GA-based back propagation neural network proportion integration differentiation controller is proposed for the stability control of the MRB during automobile braking. Its performance was verified to be satisfactory through both simulations and experiments.

Magnetic Levitation Actuation and Motion Control System with Active Levitation Mode Based on Force Imbalance

Accurate large-displacement magnetic levitation actuation and its stability remain difficult in non-liquid environments. A magnetic levitation actuation and motion control system with active levitation mode is proposed in this paper. The actuating force of the system is generated by the external magnetic field. A neural network proportion-integration-differentiation (PID) controller is designed for active actuation, and a force imbalance principle is built for the step motion mode. Dual electromagnetic actuators are configured to generate a superimposed magnetic field, ensuring that the electromagnetic force on the ball is more uniform and stable than single actuators. Dual-hall-structure sensors are used to measure displacement, thereby reducing overshoot and ensuring stability whilst motivating the ball. Due to the high adaptability of the neural network to complex systems with nonlinear and ambiguous models, the PID controller composed of neurons has stronger adaptability through tuning the PID controller parameters automatically. Furthermore, the proposed controller can solve the shortcoming that the deviation between the controlled object and the steady-state operating point increases and the tracking performance deteriorates rapidly. The strong robustness and stability in active levitation and motion control is achieved during both ascending and descending processes.

Improved active disturbance rejection controller for rotor system of magnetic levitation turbomachinery

&lt;abstract&gt; &lt;p&gt;The rotor of the magnetic suspension turbomachinery is supported by the magnetic suspension bearing without contact and mechanical friction, which directly drives the high-efficiency fluid impeller. It has the advantages of high efficiency, low noise, less fault and no lubrication. However, the system often has some unknown mutation, time variation, load perturbation and other un-certainties when working, and the traditional Proportion Integration Differentiation (PID) control strategy has great limitations to overcome the above disturbances. Therefore, this paper firstly establishes a mathematical model of the rotor of magnetic levitation turbomachinery. Then, a linear active disturbance rejection controller (LADRC) is presented, which can not only improve the above problems of PID control, but also avoid the complex parameter tuning process of traditional nonlinear active disturbance rejection control (ADRC). However, LADRC is easy to induce the overshoot of the system and cannot filter the given signal. On this basis, an improved LADRC with a fast-tracking differentiator (FTD) is proposed to arrange the transition process of input signals. The simulation results show that compared with the traditional PID controller and single LADRC, the improved linear active disturbance rejection control method with fast tracking differentiator (FTD-LADRC) can better suppress some unknown abrupt changes, time variation and other uncertainties of the electromagnetic bearing-rotor system. At the same time, the overshoot of the system is smaller, and the parameters are easy to be set, which is convenient for engineering application.&lt;/p&gt; &lt;/abstract&gt;

Mode Switching Control for Drag-Free Satellite Based on Region of Attraction

This paper aims to propose a switching rule to improve the efficiency and stability of the mode switching process for the drag-free satellite. The switching rules will ensure the stability of the different controllers during the switching process. Unlike traditional satellite switching control, the inner loop and the outer loop of the drag-free satellite are strongly coupled. The drag-free satellite not only needs to consider the controller design in the inner loop but also the controller design of the outer loop. In the outer loop, a Proportion Integration Differentiation control method is adopted to design the controller. In the inner loop, considering the release error effect of the test mass, a nonlinear sliding-mode control is employed as a controller before the mode switch. The H∞ mixed-sensitivity controller, to improve the robustness of the system and solve the problem of controller saturation, is designed after the mode switch. In the stability analysis of the switching system, the piecewise continuous Lyapunov function method is adopted. The region of attraction, which is used as the switching rule, is calculated based on the sum of squares. The obtained results demonstrate that the proposed switching rule satisfies the control accuracy and system stability.

Research on the electro-hydraulic servo system of picking manipulator

To meet the demand for high-altitude fruit picking, a luffing mechanism system is designed and used to control the hydraulic cylinder. Combined with the performance index of the servo system, the fuzzy proportional–integral–differential (PID) is adaptive and strong, and the robustness of the picking robot is simulated. Using the MATLAB/Simulink module, the fuzzy PID controller model of the electro-hydraulic servo system for picking the robot arm is established. The simulation results show that the fuzzy PID controller model can effectively reduce the error and meet the requirements. Through the simulation of the picking robot arm, the results show that the fuzzy PID control method can not only improve the performance of the picking robot arm but also provide a reference value for future optimization control.

Thermal Management of Fuel Cells Based on Diploid Genetic Algorithm and Fuzzy PID

The operation of a proton exchange membrane fuel cell (PEMFC) is greatly affected by temperature. Reliable thermal management of fuel cells can improve the life, efficiency, and power output of fuel cells. The model established in this paper is based on the inner layer of the fuel cell, and through the analysis of the heat change and material flow between layers, the simulink model can reflect the temperature change of the end plate, the bipolar plate, and the membrane electrode assembly (MEA) plate. In terms of the thermal management control strategy, the deviation and deviation rate between the MEA plate’s temperature and the target temperature are taken as input, and the fuzzy PID (proportional integral differential) controller is used to control the cooling water flow, to achieve a cooling effect. Due to the low efficiency and instability of a haploid genetic algorithm (GA) in solving dynamic optimization problems, a diploid genetic algorithm to optimize the membership function of the controller, and improve the adaptability of the control system, was designed. The simulation results show that compared with the haploid genetic algorithm, the optimal results of 100 iterations of the fuzzy PID control strategy reduce by 27.9%. Compared with the haploid genetic algorithm and fuzzy PID control, the MEA layer temperature, under the control of a diploid genetic algorithm, is reduced by 18% and 28%, respectively, and the minimum temperature difference of the reactor is 2.28 K.