Traditional Proportional Integral Derivative Controller Research Articles

Adaptive control of track tension estimation using radial basis function neural network

Track tension is a major factor influencing the reliability of a track. In order to reduce the risk of track peel-off, it is necessary to keep track tension constant. However, it is difficult to measure the dynamic tension during off-road operation. Based on the analysis of the relation and external forces depending on free body diagrams of the idler, idler arm, road wheel and road arm, a theoretical estimation model of track tension is built. Comparing estimation results with multibody dynamics simulation results, the rationality of track tension monitor is validated. By the aid of this monitor, a track tension control system is designed, which includes a self-tuning proportional-integral-derivative (PID) controller based on radial basis function neural network, an electro-hydraulic servo system and an idler arm. The tightness of track can be adjusted by turning the idler arm. Simulation results of the vehicle starting process indicate that the controller can reach different expected tensions quickly and accurately. Compared with a traditional PID controller, the proposed controller has a stronger anti-disturbance ability by amending control parameters online.

Shaking Table Substructure Testing Based on Three-Variable Control Method with Velocity Positive Feedback

To improve the experimental accuracy and stability of shaking table substructure testing (STST), an explicit central difference method (CDM) and a three-variable control method (TVCM) with velocity positive feedback (VPF) are proposed in this study. First, the explicit CDM is presented for obtaining an improved control accuracy of the boundary conditions between the numerical and experimental substructures of STST. Compared with the traditional CDM, the proposed method can provide explicit control targets for displacement, velocity, and acceleration. Furthermore, a TVCM-VPF is proposed to improve the control stability and accuracy for loading the explicit control targets of displacement, velocity, and acceleration. The effectiveness of the proposed methods is validated by experiments on a three-story frame structure with a tuned liquid damper loaded on an old shaking table originally designed with the traditional displacement control mode. The experimental results show that the proposed explicit CDM works well, and the response rate and control accuracy of the shaking table are significantly improved with the contribution of the TVCM-VPF compared with those of the traditional proportional integral derivative (PID) controller. This indicates the advantage of the proposed TVCM-VPF over the traditional PID for STST. A comparison between the traditional shaking table test and STST shows that when the latter is based on the TVCM-VPF, it exhibits an excellent performance in terms of the stability and accuracy of displacement and an acceptable performance in terms of the acceleration accuracy.

Control of superheat of organic Rankine cycle under transient heat source based on deep reinforcement learning

The organic Rankine cycle (ORC) is a promising technology for engine waste heat recovery. During real-world operation, the engine working condition varies frequently to satisfy the power demand; thus, the transient nature of engine waste heat presents significant control challenges for the ORC. To control the superheat of the ORC precisely under a transient heat source, several optimal control methods have been used such as model predictive control and dynamic programing. However, most of them depend strongly on the accurate prediction of future disturbances. Deep reinforcement learning (DRL) is an artificial-intelligence algorithm that can overcome the aforementioned disadvantage, but the potential of DRL in control of thermodynamic systems has not yet been investigated. Thus, this paper proposes two DRL-based control methods for controlling the superheat of ORC under a transient heat source. One directly uses the DRL agent to learn the control strategy (DRL control), and the other uses the DRL agent to optimize the parameters of the proportional–integral–derivative (PID) controller (DRL-based PID control). Additionally, a switching mechanism between different DRL controllers is proposed for improving the training efficiency and enlarging the operation range of the controller. The results of this study indicate that the DRL agent can satisfactorily perform the control task and optimize the traditional controller under the trained and untrained transient heat source. Specifically, the DRL control can track the reference superheat with an average error of only 0.19 K, whereas that of the traditional PID control is 2.16 K. Furthermore, the proposed switching DRL control exhibits excellent tracking performance with an average error of only 0.21 K and robustness over a wide range of operation conditions. The successful application of DRL demonstrates its considerable potential for the control of thermodynamic systems, providing a useful reference and motivation for the application to other thermodynamic systems.

A transformed proportional-integral-derivative controller for a multi-vectored propeller aerostat with independent actuator magnitude and rate saturations.

The problem of designing a controller for a multi-vectored propeller airship with independent amplitude and rate saturations is addressed. First, a linear Proportional-Integral-Derivative (PID) controller is introduced for position control without considering the input saturations. Then, two design methods are applied to the traditional PID control output to satisfy the independent amplitude and rate constraints: the nested saturated PID controller (N-PID) and the transformed PID controller (T-PID). The bounded magnitudes and rate outputs of the modified controllers are given. Simulation results showed both controllers have good tracking performance while satisfying independent amplitude and rate saturations. However, the transformed PID controller has the advantage of expressing explicitly the relationship of the actuator magnitude and rate saturations with the parameters of the transformed function such that the actuator saturations are suppressed by calculation but not by trial and error.

State feedback-based impedance control for legged robot hydraulic drive unit via full-dimensional state observer

In this article, the hydraulic drive unit that drives the movement of the joint of a legged robot was researched, aiming at improving the control accuracy of the position-based impedance control system with dual input and single output. The state feedback control containing more state information of the system was adopted to replace the traditional output feedback and proportional-integral-derivative (PID) control, through the real-time calculation of the time-varying parameters of the servo valve flow, the nonlinear state space expression of the system was derived, the state feedback matrix and the input transformation amplifier were obtained and the full-dimensional state observer was designed. The experimental results show that the designed full-dimensional state observer can track the state variables of the actual system better. Compared with the traditional output feedback and PID control, state feedback control performs better in aspects of accuracy and system dynamics for the position-based impedance control system, which can further improve the compliance performance of the robot.

A GPC-based Multi-variable PID Control Algorithm and Its Application in Anti-swing Control and Accurate Positioning Control for Bridge Cranes

It is one of the key tasks for the bridge crane to achieve anti-swing control of the hook and the accurate positioning of the body to work efficiently, safely and automatically. Based on Lagrange equation, this paper is to propose a dynamic model of the crane motion system for designing controller. In the controller design, Proportional-Integral-Derivative (PID), the most widely used controller in engineering, is adopted and a new parameter tuning algorithm for a multi-variable PID controller based on generalized predictive control (GPC) is given. It is found that the multi-variable PID controller shares the same structural mathematical expressions with the GPC law, which makes for the transfer and calculation of the three parameters P, I and D, and that the new algorithm enables the traditional PID controller to perform as brilliantly as the GPC. The results of both the simulation and real-time control experiments show that the newly-proposed PID controller can effectively eliminate the swing of the hook and control the bridge cranes moving position accurately.

A Novel Fuzzy PID Congestion Control Model Based on Cuckoo Search in WSNs.

Wireless Sensor Networks (WSNs) consist of multiple sensor nodes, each of which has the ability to collect, receive and send data. However, irregular data sources can lead to severe network congestion. To solve this problem, the Proportional Integral Derivative (PID) controller is introduced into the congestion control mechanism to control the queue length of messages in nodes. By running the PID algorithm on cluster head nodes, the effective collection of sensor data is realized. In addition, a fuzzy control algorithm is proposed to solve the problems of slow parameter optimization, limited adaptive ability and poor optimization precision of traditional PID controller. However, the parameter selection of the fuzzy control algorithm relies too much on expert experience and has certain limitations. Therefore, this manuscript proposes the Cuckoo Fuzzy-PID Controller (CFPID), whose core idea is to apply the cuckoo search algorithm to optimize the fuzzy PID controller’s quantization factor and PID parameter increment. Simulation results show that in comparison with the existing methods, the instantaneous queue length and real-time packet loss rate of CFPID are better.

Design and implementation of hybrid force/position control for robot automation grinding aviation blade based on fuzzy PID

The hybrid force/position control base on fuzzy proportional-integral-derivative (PID) is proposed to improve the quality of robotic automatic grinding aviation blades. First, the perception for the contact force/torque is discussed. A multi-source parameters gravity compensation matrix is established to identify the parameters through matrix reorganization. The contact force/torque is perceived according to the gravity compensation result. Then, the hybrid force/position control base on fuzzy PID is designed to realize active force control. Nevertheless, the sharp edge phenomenon occurs although the force control algorithm, which seriously affects the grinding quality of blades. Finally, the fusion control of force and torque is proposed to weaken the sharp edge phenomenon. The experiment proves that the introduction of torque control avoids effectively the sharp edge phenomenon. Meanwhile, comparing the proposed control algorithm with the traditional PID control, the results show that the proposed hybrid force/position control based on fuzzy PID can ensure the stability of the contact force and improve the quality of the aviation blades.

Development of a DSP Microcontroller-Based Fuzzy Logic Controller for Heliostat Orientation Control

This paper describes the design and implementation of a heliostat orientation control system based on a low-cost microcontroller. The proposed system uses a fuzzy logic controller (FLC) with the Center of Sums defuzzification method embedded on a dsPIC33EP256MU806 Digital Signal Processor (DSP), in order to modify the orientation of a heliostat by controlling the angular position of two DC motors connected to the axes of the heliostat. The FLC is compared to a traditional Proportional-Integral-Derivative (PID) controller to evaluate the performance of the system. Both the FLC and PID controller were designed for the position control of the heliostat DC motors at no load, and then they were implemented in the orientation control of the heliostat using the same controller parameters. The experimental results show that the FLC has a better performance and flexibility than a traditional PID controller in the orientation control of a heliostat.

Designing real time model mobile monitoring system for model predictive control in a nonlinear continuous stirred tank reactor

AbstractA continuous stirred tank reactor (CSTR) plays a major role in many process industries, and its control strategy could be very critical in achieving desirable yields, conversion, rates, and minimizing the formation of side products. CSTR control over a wide operating range has been a major issue to control engineers as it exhibits a highly nonlinear behaviour and time varying behaviour. A traditional control mechanism like a proportional–integral–derivative (PID) controller although still widely used, may not be sufficient in achieving optimum performance and robustness. In this study, model predictive control (MPC) strategy is developed for a nonlinear dynamic CSTR process, and the performance was compared with the traditional PID controller. The result shows that for a small set point change, MPC has similar performance to the PID controller. MPC performed much better when subjected to a much larger set point change in which the response time is 12 times faster and is able to reduce up to 60% of the loop interaction. The second part of this study is to design a real‐time mobile monitoring system (proof of concept). The overall design allows for real‐time updates on the performance of CSTR remotely. Engineers will be notified when the equipment fails to run at its full performance and generate critical analysis based on the historical data of the equipment. Lastly, a real‐time safety alert allows accidents to be predicted ahead before it happens and allows workers to be notified in real time.

Experimental Validation of Nonlinear Model Predictive Control for a Heavy-Duty Diesel Engine Waste Heat Recovery System

Abstract This paper discusses an experimental validation of a real-time augmented control scheme for an organic Rankine cycle (ORC) waste heat recovery (WHR) system. A nonlinear model predictive control (NMPC) is designed to regulate the working fluid vapor temperature after the evaporator. The NMPC utilizes a six-state reduced order moving boundary (MB) evaporator model. The state estimator is constructed using an extended Kalman filter (EKF) given the working fluid outlet vapor temperature and exhaust gas outlet temperature as measurements. Working fluid evaporation pressure is controlled by an external proportional-integral-derivative (PID) control loop. The experimental validation first compares the performance of the augmented control scheme with that of a traditional multiple loop PID control with a feedforward term over an engine transient. The experimental study shows that the augmented control scheme outperforms the baseline multiloop PID control in both terms tracking error and settling time during transient engine operation. The performance of the augmented control scheme is further validated over three additional transient conditions with alterations to both the engine transient and the working fluid reference temperature. The NMPC validation shows that the working fluid vapor temperature can be controlled within 1% error margin relative to the targeted reference.

Research on temperature control of heating furnace with intelligent proportional integral derivate control algorithm

In view of the problems of large overshoot and large oscillation frequency in cur?rent furnace temperature control, based on the development of intelligent control theory, expert control, fuzzy control, and neural network control in intelligent control theory are combined with proportional integral derivative (PID) control. The intelligent PID control algorithm is used to carry out numerical simulation and experimental research on these several control algorithms. The results show that the adjustment effect of the intelligent PID control algorithm is significantly better than the traditional PID control algorithm. Among them, the fuzzy self-tuning PID control algorithm and the fuzzy immune PID control algorithm are feasible in the application of furnace temperature control. The neural network PID control algorithm It also has good development and application potential.

Fractional-order internal model control algorithm based on the force/position control structure of redundant actuation parallel robot

This article proposes a new control structure for the complex redundant actuation parallel robot based on force/position hybrid control structure. The traditional proportional–integral–derivative control method, integer-order internal model control method and fractional-filter internal model control–proportional–derivative control method are used in the position structure of force/position hybrid control. The fractional-filter internal model control–proportional–derivative control method is used in the position loop of the permanent magnet synchronous motor to reduce the position error. A fractional-order theory with the internal model control method is used in redundant actuation force control structure, which can improve the control precision of the driving force of the parallel robot. The Admas/Matlab simulation results show that the proposed method outperforms other methods and can obtain good robustness and tracking performance.

Dynamic Loading System of an Agricultural Vehicle Power Wagon Based on Complex Loading

A dynamic loading system of a power wagon is studied to solve the problems of low loading precision, slow response speed, and poor adaptive capability of a power wagon in an agricultural vehicle traction test. The load feature of an engine is analyzed under different gear locations, governor handle positions, and vehicle speeds. Meanwhile, the load feature of an eddy current retarder is examined based on a backpropagation (BP) neural network proportional-integral-derivative (PID) control algorithm. The mathematical model for the loading system is established. The time domain model for traction is refactored by a white noise filter according to the power spectral density function. To improve the dynamic loading performance of the eddy current retarder, the BP neural network PID control algorithm is used to change its exciting voltage to achieve an accurate simulation of the actual working load. The effectiveness and practicability of this control algorithm are validated by comparing its system output response dynamic characteristics with those of the traditional PID control algorithm. Simulation results show that the output error is less than 2.8% and the maximum delay is 56.5 ms when the BP neural network PID control is used. On the basis of this result, the traction property of a tractor with several wheels is tested on the road. The test and analysis results indicate that the output loads of the loading system accurately simulate the field actual load of the tractor. The maximum output load value of the system is 42-48 kN and the applicable speed range is 0-25 km/h. The output error is less than 3.5% and the maximum delay is 85.6 ms.

A new control method based on fuzzy controller, time delay estimation, deep learning, and non-dominated sorting genetic algorithm-III for powertrain mount system

This article presents a new control method based on fuzzy controller, time delay estimation, deep learning, and non-dominated sorting genetic algorithm-III for the nonlinear active mount systems. The proposed method, intelligent adapter fractions proportional–integral–derivative controller, is a smart combination of the time delay estimation control and intelligent fractions proportional–integral–derivative with adaptive control parameters following the speed range of engine rotation via the deep neural network with the optimal non-dominated sorting genetic algorithm-III deep learning algorithm. Besides, we proposed optimal fuzzy logic controller with optimal parameters via particle swarm optimization algorithm to control reciprocal compensation to eliminate errors for intelligent adapter fractions proportional–integral–derivative controller. The control objective is to deal with the classical conflict between minimizing engine vibration impacts on the chassis to increase the ride comfort and keeping the dynamic wheel load small to ensure the ride safety. The results of this control method are compared with that of traditional proportional–integral–derivative controller systems, optimal proportional–integral–derivative controller parameter adjustment using genetic algorithms, linear–quadratic regulator control algorithms, and passive drive system mounts. The results are tested in both time and frequency domains to verify the success of the proposed optimal fuzzy logic controller–intelligent adapter fractions proportional–integral–derivative control system. The results show that the proposed optimal fuzzy logic controller–intelligent adapter fractions proportional–integral–derivative control system of the active engine mount system gives very good results in comfort and softness when riding compared with other controllers.

Characteristics Analysis and Fuzzy Fractional-Order PID Parameter Optimization for Primary Frequency Modulation of a Pumped Storage Unit Based on a Multi-Objective Gravitational Search Algorithm

Compared with conventional hydropower units, the pumped storage unit has the characteristics of diverse working conditions and frequent switching. Therefore, the stability and regulation quality of the primary frequency modulation transition process of the regulating system is very important. Due to the “S” characteristics of the pumped storage unit (PSU), the pumped storage unit regulating system has a strong nonlinearity, and the conventional proportional-integral-derivative (PID) controller cannot provide high-quality control under low water head conditions. In this paper, the nonlinear PSU model with an elastic water hammer effect is studied, and the fuzzy fractional-order PID (FFOPID) controller is designed to improve the stability of the system. The membership function and the control parameters of the fractional-order PID are optimized based on the multi-objective gravitational search algorithm (MOGSA). The experimental results show that the optimized design of the FFOPID controller has better control quality than the traditional PID controller, the fractional-order PID (FOPID) controller, and the fuzzy PID controller (FPID) when the system is disturbed by the rotating speed under low water head.

High order differential feedback controller design and implementation for a Stewart platform

Stewart platform or other parallel manipulators with a Stewart structure are commonly used in flight simulators, surgical operations, medical rehabilitation processes, machine tools, industrial applications, etc. Therefore, researchers have paid attention to position control of these manipulators in addition to their design and development process. In this study, a developed Stewart platform and its inverse kinematic analysis are presented first. Then, a model-free control scheme called a high order differential feedback controller scheme is designed for the Stewart platform in order to improve its trajectory tracking performance and robustness against to different reference trajectories. Real-time trajectory tracking experiments with varied reference trajectories are carried out to show the robustness and effectiveness of the high order differential feedback controller scheme compared to the traditional proportional–integral–derivative controller of which the parameters are optimally tuned. The obtained visual trajectory tracking results and numerical performance results based on error-based performance measurement metrics such as integral of absolute error, integral of squared error, and integral of time-weighted absolute error are provided for both the proposed high order differential feedback controller scheme and the optimal tuned proportional–integral–derivative controller. Experimental results show that the proposed high order differential feedback controller scheme is more robust than the proportional–integral–derivative controller. Furthermore, the high order differential feedback controller scheme has superiority in both transient and steady-state responses and even the parameters of the proportional–integral–derivative controller are optimally tuned.

Simulation and adaptive control of back propagation neural network proportional–integral–derivative for special launcher using new version of transfer matrix method for multibody systems

Rocket launcher system, as a special launcher placed on tactical vehicles, is a very complex mechanical system with characteristics of strong shock and vibration. In order to improve position accuracy, as well as reduce vibration, this paper creates a nonlinear dynamics model of the launcher system by using a new version of the transfer matrix method for multibody systems. The overall transfer equation of the nonlinear model is deduced. Combining with general kinematics equations of the rocket, the system launch dynamics are simulated and compared with experiments to verify the correctness of the model. On this basis, a backpropagation neural network proportional–integral–derivative adaptive control system is designed to improve servo control of the launcher. Then, the effectiveness of this method is verified by comparing with the traditional proportional–integral–derivative control method. Simulated results show that the backpropagation neural network proportional–integral–derivative control system makes the azimuth and elevation angles reach the target values smoothly and quickly, with higher accuracy. The results prove that the proposed method prominently reduces vibrations of the launcher, by adjusting the control parameters online according to the operation state of the system, presenting a better stability and robustness.

An optimal interval type-2 fuzzy logic control based closed-loop drug administration to regulate the mean arterial blood pressure

Background and ObjectiveThe main aim of this work is to present an optimal and robust controller design in order to improve the drug infusion to the automatic control of mean arterial blood pressure in conditions like critically-ill or post-operative or anaesthesia administration. The physiological systems also have uncertainty issues such as parameter variations with time or external disturbances and noise. Therefore, a controlled drug administration is necessary to regulate the mean arterial blood pressure of a person during surgery/observation. Over the years, the proportional-integral-derivative (PID) controller is the most commonly used controller in industries due to its easy structure and simplicity. However, this controller does not meet the desired performance with the complex and uncertain plants. Therefore, a robust controller is required to regulate the physiological variables that are uncertain in nature and can affect the human life. MethodsIn this work, a hybrid control scheme consisting of an interval type-2-fuzzy logic controller which acts as pre-compensator to the traditional PID controller is presented, to regulate the mean arterial blood pressure of a patient by administering the drug sodium nitroprusside in a controlled manner. An effective and well-established nature-inspired optimization technique namely cuckoo search algorithm is employed for obtaining the optimal parameters for the presented scheme. ResultsSimulation results are presented to show the effectiveness and robustness of proposed interval type-2-fuzzy logic controller based PID controller scheme, for maintaining the mean arterial pressure to 100 mmHg within considerable limit through SNP infusion. The results are further compared with other two controllers namely type-1 fuzzy logic based PID and traditional PID controllers for the parameter variations and external noise. ConclusionIn this study, the proposed interval type-2-fuzzy logic controller pre-compensator based PID controller provides an effective control than traditional type-1 fuzzy logic based control scheme and PID controller in terms of overshoot, settling-time and error which are the prime performance objectives of the closed-loop controlled drug delivery of human blood pressure. The presented study provides a firm base for initial design considerations for development of a low-cost closed-loop drug delivery system for blood pressure regulation.

Multiple-model switching control for vibration suppression of planar membrane structures

Membrane space structures have received widespread attention because of their small packaging volume and low mass. However, because membranes are very flexible and lightly damped, vibration suppression in membrane structures is very difficult. The objective of this study was to solve this problem. The first part of this article describes the influence of wrinkling in a membrane structure on the structure’s vibration characteristics. On this basis, the vibration deformations of a wrinkled square membrane structure were derived from the dynamic equations, and the correctness of this vibration model was verified by numerical simulation and experiment. A multi-model system is proposed to simulate the dynamic response of a membrane structure under different boundary conditions. In combination with the drive system, a multi-model switching control method based on adaptive and proportional–integral–derivative control is proposed. Under the initial disturbance, when the vibration amplitude dropped below 0.01 mm, the vibration duration was reduced to 2.96 s, compared with the duration of 12.37 s without control. The duration was shortened by approximately 39.7%, compared with the duration of 4.91 s achieved by the traditional proportional–integral–derivative control method, and by approximately 15.9% compared with the 3.52 s achieved by the out-plane control method. When there were multiple disturbances and the proposed method was used, the boundary displacement loadings did not increase when a certain value was exceeded. This prevented the breaking of the membrane by overstretching and provides a theoretical foundation for setting the initial pre-stress values.