Classical Proportional Integral Derivative Research Articles

Active Queue Management Supporting TCP Flows Using Disturbance Observer and Smith Predictor

Active queue management (AQM) is a technique to avoid serious congestion of the transmission control protocol (TCP) flows at a router. AQM based on control theory, which utilizes congestion controllers such as proportional-derivative (PD) controller or proportional-integral-derivative (PID) controller, has been previously proposed. In addition, disturbance observer (DOB) has been utilized to compensate for modeling error of a TCP/AQM congestion control system. However, the DOB-based controllers cannot cope with a large time delay in TCP/AQM networks. Although one of the effective time delay compensators is Smith predictor (SP), the implementation of the DOB and SP in an integrated manner has not been accomplished, because of saturation due to the input limit of packet drop probability. In this paper, a novel TCP/AQM congestion control system with the DOB and SP considering the saturation function is proposed to compensate for the modeling error and time delay simultaneously. Simulation results show that the proposed controller provides better throughput and goodput than conventional controllers. One of the simulations assume that the propagation delay and bottleneck link capacity are set to 100 ms and 100 Mbps, respectively. Under this assumption, it is confirmed that the proposed controller achieves a goodput of 99.55 Mbps whereas the classical PID controller and PD controller with DOB achieve 99.23 Mbps and 99.24 Mbps, respectively.

Comparison of MATLAB Simulink application with PLC application of real-time classical PID controllers in laboratory

Process control is a common area of study for different engineering disciplines. Controllers working ‎with ‎the ‎proportional–integral–derivative (PID) method are generally used in process control. ‎Parameters like ‎level, ‎flow, position, pressure, and temperature are controlled in-process control. PID ‎algorithm can be ‎formed ‎with different microprocessor-based devices. Process control is ‎implemented by using analog input ‎and ‎outputs terminals of PLCs (Programmable logic controller) ‎which is recently the most commonly used ‎in ‎industries. Also, a real-time PID method can be ‎implemented by using a computer-based ‎MATLAB ‎ ‎program in the laboratory.‎ The liquid level and flow control in the laboratory has been done by using PID algorithms and ‎PLC ‎device ‎and MATLAB real-time interface by using an experimental set. Necessary connections ‎and ‎configurations have ‎been prepared to perform process control. (SIMATIC Manager Step7) is ‎used for ‎programming in PLC. ‎Besides, the MATLAB simulation program is configured to implement ‎real-time ‎control to be ‎compatible with the MF624 data acquisition card. Liquid level and fluid flow ‎control ‎experiments were ‎performed separately for the same PID algorithms formed on two different ‎devices, then ‎liquid level ‎detection and liquid flow control results have been collected and ‎investigated. Based on these ‎results, a total ‎of four experiments were compared with both devices ‎considering the ease and difficulties of ‎using PLC and ‎MATLAB.‎

Gray based Fuzzy Gain-Scheduling PID Controller Design for Air-Fuel System Under Variable Engine Operating Conditions

In this study, the problem of regulation of air-fuel ratio (AFR) in gasoline engines under different engine operating conditions is discussed. Firstly, the mean value mathematical model of the AFR system has been created. Then, two different approaches named with classical proportional-integral-derivative (PID) and a fuzzy logic gain scheduling PID controller combined with gray system modelling approach (Gray GS-PID)have been used to improve the performance of the engine to monitor stoichiometric conditions. The parameters of classical PID parameters are determined by the pattern search algorithm. The design procedures for both controllers have been presented in detail. In order to evaluate the performance analysis for both of the proposed controllers, variable conditions were established based on engine speed and throttle opening ratios in the US06 and UDDS driving conditions and validated by simulation results. According to the results, Gray GS_PID is more powerful than optimally adjusted PID in terms of reducing the amount of deviation of AFR from stoichiometric value under variable engine operating conditions. The most important contribution of this study is that, unlike conventional AFR regulation, the prediction of future error value relative to the previous AFR error values ​​using the gray prediction algorithm, and the design of the control algorithm that determines the control action for the next step depending on the predicted error value before the error occurs and sets the gain parameters.

Hierarchical MPC scheme for the speed governing of PSU with complex conduit system

Pumped-storage unit (PSU) often acts as the peak and frequency modulation sources in modern power systems. The frequent operation condition conversions and tight hydraulic couplings with conduit system make its governing control a tough work in practise. To overcome the difficulty in the proportional–integral–derivative (PID) parameter tuning during transient processes, a hierarchical model predictive control (MPC) scheme, in which constrained generalised predictive control (CGPC) cooperates with classic PID is proposed. In the upper hierarchy of the MPC controller, CGPC acts as an auxiliary optimiser for the optimal PID parameters through online optimisation, whereas in the lower hierarchy, PID directly steers the movements of servomotor to control the guide vane opening; thus, to match the existing governing equipment in engineering practise. Also, typical constraints on the input/output signals and value ranges of PID parameters are discussed. Simulation experiments are conducted on a non-linear distributed-parameter simulation platform. It can precisely reflect hydraulic and mechanical dynamic behaviours of PSU and its conduit system. The results indicate that the proposed hierarchical MPC scheme not only outperforms in speed tracking and oscillation damping, but also attenuate instability phenomena caused by the negative ‘S’ character in pump turbine.

Reinforcement Learning Controllers for Enhancement of Low Voltage Ride Through Capability in Hybrid Power Systems

The present study applies reinforcement learning strategy for controller design to improve the low voltage ride through (LVRT) capability of a hybrid power system through convertible static compensator (CSC). This article considers different configurations of CSC such as static synchronous series compensator (SSSC), one static synchronous compensator (STATCOM), two STATCOMs, and unified power flow controller (UPFC). Both Q-learning and dynamic fuzzy Q-learning (DFQL)-based controllers are designed and their performances were compared with classical proportional-integral derivative (PID) controller considering a 3-machine system consisting of 2-synchronous and 1-wind energy systems. The results of simulation show that the performance of DFQL-based controller is better compared to other 2-controllers in improving the LVRT capability. Further, it is shown that the UPFC and two STATCOMs configurations of CSC provide higher voltage support compared to other configurations.

Control of Position, Velocity and Acceleration of Actuators Using Optimal State Estimation

Abstract In order to improve performance of closed loop control and achieve highly accurate reference trajectory tracking, motion controllers have to utilize the advanced control and system state estimation algorithms. Classical PID (Proportional-Integral-Derivative) controller includes calculation of derivation of a process value in order to achieve system desired performance of a system. This paper presents Kalman filter as an optimal estimation algorithm on the model of a hydraulic actuator which is equally applicable to any other application that involves calculating derivations of noisy measurements. Alternative form of Kalman filter, suitable for implementation on a PLC (Programmable Logic Controller) or any other embedded system is also presented.

Position Tracking of ML System using CSA Based PID Controller

The classical proportional integral derivative (PID) controllers are still use in various applications in industry. Magnetic levitation (ML) systems are rigidly nonlinear and sometimes unstable systems. Due to inbuilt nonlinearities of ML systems, tracking of position of ML Systems is still difficult. For the tracking purpose of position, PID controller parameters are found by choosing Cuckoo Search Algorithm (CSA) of optimization. The ranges of parameters are customized by z-n method of parameters. Simulation results show the tracking of position of ML systems using conventional and optimized parameters obtained with the CSA based controller.

Adaptive neural tracking control for automotive engine idle speed regulation using extreme learning machine

The automotive engine idle speed control problem is a compromise among low engine speed for fuel saving, minimum emissions and disturbance rejection ability to prevent engine stall. However, idle speed regulation is very challenging due to the presence of high nonlinearity and aging-caused uncertainties in the engine dynamics. Therefore, the engine idle speed system is a typical uncertain nonlinear system. To address the problems of inherent nonlinearity and uncertainties in idle speed regulation, an extreme learning machine (ELM)-based adaptive neural control algorithm is proposed for tracking the target idle speed adaptively. The purpose of ELM is to rapidly deal with the uncertain nonlinear engine system. Since the original ELM is not designed for adaptive control, a new adaptation law is designed to update the weights of ELM in the sense of Lyapunov stability. Experiment is conducted to validate the performance of the proposed control method. Experimental result indicates that the ELM-based adaptive neural control outperforms the classical proportional–integral–derivative (PID), fuzzy-PID and back-propagation-neural-network-based controllers in terms of tracking performance under the variation of engine load.

Optimized Proportional-Integral-Derivative Controller for Upper Limb Rehabilitation Robot

This paper proposes a nature inspired, meta-heuristic optimization technique to tune a proportional-integral-derivative (PID) controller for a robotic arm exoskeleton RAX-1. The RAX-1 is a two-degrees-of-freedom (2-DOFs) upper limb rehabilitation robotic system comprising two joints to facilitate shoulder joint movements. The conventional tuning of PID controllers using Ziegler-Nichols produces large overshoots which is not desirable for rehabilitation applications. To address this issue, nature inspired algorithms have recently been proposed to improve the performance of PID controllers. In this study, a 2-DOF PID control system is optimized offline using particle swarm optimization (PSO) and artificial bee colony (ABC). To validate the effectiveness of the proposed ABC-PID method, several simulations were carried out comparing the ABC-PID controller with the PSO-PID and a classical PID controller tuned using the Zeigler-Nichols method. Various investigations, such as determining system performance with respect to maximum overshoot, rise and settling time and using maximum sensitivity function under disturbance, were carried out. The results of the investigations show that the ABC-PID is more robust and outperforms other tuning techniques, and demonstrate the effective response of the proposed technique for a robotic manipulator. Furthermore, the ABC-PID controller is implemented on the hardware setup of RAX-1 and the response during exercise showed minute overshoot with lower rise and settling times compared to PSO and Zeigler-Nichols-based controllers.

Simulation Tool for Tuning and Performance Analysis of Robust, Tracking, Disturbance Rejection and Aggressiveness Controller

The RTD-A (robust, tracking, disturbance rejection and aggressiveness) controller is a novel control scheme that substitutes the classical proportional integral derivative (PID) controller. This novel controller’s performance depends on the four controller tuning parameters (θR, θT, θD and θA). The tuning of RTD-A controller is more transparent than classic PID controllers. The RTD-A tuning parameters values lies between ZERO and ONE. Availability of a tool to design optimal parameters for this controller and evaluating the performance on a given system is necessary for the researchers. In this paper, the new simulation tool is presented to deal with the RTD-A control scheme. There are four graphical user interface tools included in the proposed tool and working of each tool is explained in detail. To demonstrate the proposed tool, two examples, which involve a liquid level control application and an air pressure control application, are presented in this work. The performance of the RTD-A controller is compared with PID controller. RTD-A controllers are tuned using optimization algorithms and their performances are observed and analyzed in both cases under deterministic and uncertain conditions.

Back to Basics: Meaning of the Parameters of Fractional Order PID Controllers

The beauty of the proportional-integral-derivative (PID) algorithm for feedback control is its simplicity and efficiency. Those are the main reasons why PID controller is the most common form of feedback. PID combines the three natural ways of taking into account the error: the actual (proportional), the accumulated (integral), and the predicted (derivative) values; the three gains depend on the magnitude of the error, the time required to eliminate the accumulated error, and the prediction horizon of the error. This paper explores the new meaning of integral and derivative actions, and gains, derived by the consideration of non-integer integration and differentiation orders, i.e., for fractional order PID controllers. The integral term responds with selective memory to the error because of its non-integer order λ , and corresponds to the area of the projection of the error curve onto a plane (it is not the classical area under the error curve). Moreover, for a fractional proportional-integral (PI) controller scheme with automatic reset, both the velocity and the shape of reset can be modified with λ . For its part, the derivative action refers to the predicted future values of the error, but based on different prediction horizons (actually, linear and non-linear extrapolations) depending on the value of the differentiation order, μ . Likewise, in case of a proportional-derivative (PD) structure with a noise filter, the value of μ allows different filtering effects on the error signal to be attained. Similarities and differences between classical and fractional PIDs, as well as illustrative control examples, are given for a best understanding of new possibilities of control with the latter. Examples are given for illustration purposes.

Development of Operator Theory in the Capacity Adjustment of Job Shop Manufacturing Systems

With the development of industrial manufacture in the context of Industry 4.0, various advanced technologies have been designed, such as reconfigurable machine tools (RMT). However, the potential of the latter still needs to be developed. In this paper, the integration of RMTs was investigated in the capacity adjustment of job shop manufacturing systems, which offer high flexibility to produce a variety of products with small lot sizes. In order to assist manufacturers in dealing with demand fluctuations and ensure the work-in-process (WIP) of each workstation is on a predefined level, an operator-based robust right coprime factorization (RRCF) approach is proposed to improve the capacity adjustment process. Moreover, numerical simulation results of a four-workstation three-product job shop system are presented, where the classical proportional–integral–derivative (PID) control method is considered as a benchmark to evaluate the effectiveness of RRCF in the simulation. The simulation results present the practical stability and robustness of these two control systems for various reconfiguration and transportation delays and disturbances. This indicates that the proposed capacity control approach by integrating RMTs with RRCF is effective in dealing with bottlenecks and volatile customer demands.

Design and development of a Stewart platform assisted and navigated transsphenoidal surgery

In this study, technical details of a Stewart platform (SP) based robotic system as an endoscope positioner and holder for endoscopic transsphenoidal surgery are presented. Inverse and forward kinematics, full dynamics, and the Jacobian matrix of the robotic system are derived and simulated in MATLAB/Simulink. The required control structure for the trajectory and position control of the SP is developed and verified by several experiments. The robotic system can be navigated using a six degrees of freedom (DOF) joystick and a haptic device with force feedback. Position and trajectory control of the SP in the joint space is achieved using a new model-free intelligent PI (iPI) controller and it is compared with the classical PID (proportional-integral-derivative) controller. Trajectory tracking experimental results showed that the tracking performance of iPI is better than that of PID and the total RMSE of the trajectory tracking is decreased by 17.64% using the iPI controller. The validity of the robotic system is proven in the endoscopic transsphenoidal surgery performed on a realistic head model in the laboratory and on a cadaver in the Institute of Forensic Medicine. The key feature of the system developed here is to operate the endoscope via the joystick or haptic device with force feedback under iPI control. Usage of this system helps surgeons in long, fatiguing, and complex operations. This system can generate new possibilities for transsphenoidal surgery such as fully automated robotic surgery systems.

Real-Time Implementation of Sliding Mode Control Technique for Two-DOF Industrial Robotic Arm

This study presents a real-time implementation of the angle trajectory tracking control for the twoDOF industrial robotic manipulators. For real-time trajectory tracking control, the classical proportional–integral–derivative (PID) control and the nonlinear Sliding Mode Control techniques have been considered. The SMCtechnique takes into account the complete dynamic model of the two DOF robot to increase the trajectory trackingperformance of the manipulator. The experimental results demonstrate that the nonlinear SMC method has improvedthe trajectory tracking performance compared with the PID method.

How nonlinear control can enhance the automobile efficiency and reduce harmful emissions: China case study

China transport energy consumption grows greatly along with dramatically escalated carbon dioxide emissions. Saving energy in transportation is requested for both reducing energy pressure intensity and decreasing relevant emissions. Large scale energy conservation and emission reduction are Chinese automotive industrys’ fundamental objectives. This paper addresses a nonlinear hybrid fuzzy based proportional integral derivative (PID) control methodology to reduce the automobile fuel over-consumption and gases over-emission. After introducing the mathematical model of the automobile Pierburg mechatronic actuator of nonlinear character with parameter uncertainties, its angular displacement is controlled via the proposed PID-fuzzy logic control (FLC) approach. The outputs of the PID controller are the entries of the series-connected FLC. The hybrid topology merges the simplicity and robustness advantages of both the classical PID and the FLC methods respectively. The feedback controller, based on FLC technique, is designed to desirably maintain the angular displacement towards their desired reference values with possible least steady state errors. The proper steady state error elimination will lead to considerable gas emission reduction together with improved energy efficiency. Through Matlab/Simulink tools, the numerical simulations significantly illustrate that the hybrid PID-FLC technique can contribute efficiently in ameliorating the system dynamic behavior in presence of parameter uncertainties. The overall system behavioral analysis enhancement and the proposed controller robustness are experimentally validated.

Real-Time Implementation of Continuous Model Based Sliding Mode Control Technique for Trajectory Tracking Control of Mobile Robot

In this study, real-time trajectory tracking control of an autonomous mobile robot, actuated by two DC motors, has been designed, analyzed and studied. Two different control approaches such as model based sliding mode (SMC) and the classical proportional–integral–derivative (PID) control are employed to increase the tracking performance of the mobile robot. A model based SMC technique has been presented in order to consider the complete dynamic model of the robot and in order to increase trajectory tracking performance of the system. The experimental outcomes strongly verified that the proposed controller gives a quite well trajectory tracking response and smaller magnitude overshot compared with the classical PID controller.

Design of Magnetic Bearing Control System Based on Active Disturbance Rejection Theory

At present, most of the magnetic bearing system adopts the classical proportional–integral–derivative (PID) control strategy. However, the external disturbances, system parameter perturbations, and many other uncertain disturbances result in PID controller difficult to achieve high performance. To solve this problem, a linear active disturbance rejection controller (LADRC) based on active disturbance rejection controller (ADRC) theory was designed for magnetic bearing. According to the actual prototype parameters, the simulation model was built in matlab/simulink. The step and sinusoidal disturbances with PID and LADRC control strategies were simulated and compared. Then, the experiments of step and sinusoidal disturbances were performed. When control parameters are consistent, the experiment showed that the rotor displacement fluctuation decreased by 28.6% using the LADRC than PID control under step disturbances and decreased by around 25.8% under sinusoidal disturbances. When the rotor is running at 24,000 r/min and 27,000 r/min, the displacement of rotor is reduced by around 15% and 13.7%, respectively. Rotate the rotor with step disturbances and sinusoidal disturbances. It can also be seen that LADRC has the advantages of fast response time and good anti-interference. The experiments indicate that the LADRC has better anti-interference performance compared with PID controller.

Application of Model Predictive Control to a Continuous Multiple‐Effect Crystallizer

AbstractAn infinite horizon model predictive controller (IHMPC) with zone control is applied to a continuous five‐effect evaporative sodium chloride crystallizer. Firstly, a phenomenological dynamic model of the process is developed considering mass, energy, and moment balances coupled to crystallization kinetics. The developed model plays the role of the real system in order to study the proposed optimization/control strategy. The proposed approach is compared to a classical proportional integral derivative (PID) control system. The control strategy based on the prediction of the future state of the plant provides a faster response, a better stability to the process, and a reduction in energy consumption.

Data-Driven PID Controller and Its Application to Pulp Neutralization Process

The pulp neutralization process is a complex industrial process with strong nonlinearity and time-varying dynamics. In general, this kind of process is hardly controlled by using traditional control techniques such as the proportional-integral-derivative (PID) controller. To resolve this problem, a novel PID control scheme is proposed by integrating a data-driven compensation of unmodeled dynamics and a multistep ahead of optimal control strategy. Essential difference from the classical PID controller lies in the use of unmodeled dynamics, the measured data from the pulp neutralization process is used directly for building the nonlinear PID controller, which plays an important role in control system design. Based on multistep ahead prediction optimal control theory, the controller parameters can be obtained through optimizing a cost function. Some theoretical results on the stability and convergence of the closed-loop system are established. To demonstrate the effectiveness of the proposed control techniques, a simulation model and a cobalt nickel hydroxide pulp neutralization process are employed. Simulations were carried out using data extracted from an actual pulp neutralization process, followed by some industrial experiments. Results from both simulations and experiments indicate that our proposed controller can effectively control the pH value of the pulp neutralization process with promising control performance.

Frequency stabilization of hydro–hydro power system using hybrid bacteria foraging PSO with UPFC and HAE

This paper presents the design of proportional integral derivative (PID) based load frequency control (LFC) scheme effectively optimized through novel combination of bacteria foraging oriented particle swarm optimization (BFO-PSO) technique for a hydro dominating energy system model. The design control are implemented for 1% load disturbance in area-1 and compared with classical PID, Pessen integral rule, some overshoot, no overshoot as well as with recently published bacteria foraging optimization algorithm (BFOA) in terms of computed gains and inverse time multiplied absolute error (ITAE). The system performance for hydro energy system is sluggish and oscillatory. Hence, the further enhancement with proposed design are observed by considering the combination of unified power flow control (UPFC) in series with the tie-line and hydrogen aqua electrolyzer units installed at terminal of area-2. The system performance of the designed control is evaluated under various system investigations considering the dead-band and generation rate constraint (GRC) non-linearity and the applications results are presented to show the superiority of the proposed work.