Classical Proportional Integral Derivative Research Articles

Optimization of Energy Consumption of Industrial Robots Using Classical PID and MPC Controllers

Industrial robots have a key role in the concept of Industry 4.0. On the one hand, these systems improve quality and productivity, but on the other hand, they require a huge amount of energy. Energy saving solutions have to be developed and applied to provide sustainable production. The purpose of this research is to develop the optimal control strategy for industrial robots in order to minimize energy consumption. Therefore, a case study was conducted for the development of two control strategies to be applied to the RV-2AJ Mitsubishi robot arm with 5 DOF, where the system is a nonlinear one. The first examined controller is the classical linear proportional integral derivative (PID) controller, while the second one is the linear model predictive control (MPC) controller. In our study, the performances of both the classical PID model and the linear MPC controller were compared. As a result, it was found that the MPC controller in the execution of the three defined reference trajectories [(1) curve motion, (2) N-shaped motion, and (3) circle motion] was always faster and required less energy consumption, whereas in terms of precision the PID succeeded in executing the trajectory more precisely than the MPC but with higher energy consumption. The main contribution of the research is that the performances of the two control strategies with regard to a complex dynamic system were compared in the case of the execution of three different trajectories. The evaluations show that the MPC controller is, on the one hand, more energy efficient; on the other hand, it provides a shorter cycle time compared to the PID controller.

Model predictive control of power plant cycling using Industry 4.0 infrastructure

This work involves the Industry 4.0 infrastructure developed at West Virginia University (WVU) for process systems applications. This infrastructure emulates an interconnected environment, enabling communication and data sharing among different components for use in academic and industrial settings. The current infrastructure encompasses a power plant model interacting with online load demand, distributed control systems, and data analytics components. The developed model of a sub-critical coal-fired power plant is employed to evaluate classical and advanced control strategies using this infrastructure under different operating conditions. Specifically, the control strategies evaluated include classical proportional–integral–derivative (PID) and advanced model predictive control (MPC) structures, focusing on the dynamic matrix control (DMC) approach with an in-house modified sequential quadratic programming (SQP) solver. The MPC approach is developed and simulated in closed loop to address setpoint tracking and load-following scenarios under power plant cycling conditions. In this infrastructure, the PI System centralizes all the information received from the power plant model and the online power demand and sends the control actions calculated by the MPC back to the power plant model for implementation. Results of the implementation of these control strategies are discussed focusing on power plant operating regions associated with cycling.

Capability Exploration of Extended-State Observer-Based Control Under the Uncertain Case of Disturbance and Actuator Saturation

Considering a class of nonlinear systems subject to actuator saturation and uncertain control input, this article explored the control capability of an extended-state observer-based active disturbance rejection controller in the presence of disturbances and uncertainties. First, the actuator saturation is handled with the convex hull in the extended-state observer-based control scheme, and an ellipsoid invariant set inside the domain of attraction for control systems is obtained using the Lyapunov method. Second, the enlargement problem of the invariant set is also studied, which illustrates that the convex hull of the ellipsoid invariant sets is also an enlargement invariant set. Finally, comprehensive comparisons with existing methods on a brushless dc motor demonstrate the stronger capability of the designed controller on disturbance rejection and actuator saturation. The results show that the disturbance offset and performance index (integrated time and absolute error and integral absolute error) are about half of the classical proportional–integral–derivative(PID) and double-integral sliding-mode control, or the disturbance rejection capability is about twice in this case.

Identificação e implementação de um Controle Preditivo em um Sistema Térmico

The present work consists of carrying out the identification and implementation of a predictive controller in a thermal system to follow a didactic design methodology for learning in systems identification, analysis, and controller design. For the identification of the thermal system, the method of least squares was chosen because it allows to obtain a faithful mathematical model that describes the temperature system. The data used in the system identification step are from a didactic industrial plant of a temperature system. From the model obtained for the system in question, the design and implementation of a classic Proportional Integral Derivative (PID) controller and a predictive controller are carried out to verify the behavior of the system against the actions of both controllers. The entire implementation will be carried out in the MATLAB/Simulink computer simulation environment.

Fuzzy Logic Aided PID Controller for Induction Motor Speed Control

This paper presents design of intelligent based controller for speed control of induction motor. The proposed system integrates a classical proportional-integral-derivative (PID) controller and intelligent algorithm based on fuzzy logic control (FLC). This scheme takes advantages of classical PID and FLC to improve the speed response performance of induction motor analysed in terms of transient and steady state time domain characteristics. The FLC designed was implemented using the fuzzy block of MATLAB/Simulink based on Mamdani model and comprises 9 fuzzy variables and 49 logic (intelligent) rules that define the system behaviour. The FLC takes the loop error and its rate of change to manipulate the input command to the PID control so that the response speed signal matches with the desired speed signal resulting in reduced rise time, peak time, settling time, overshoot, and improved steady state error. The designed intelligent aided PID controller was implemented and the simulation result provided a rise of time of 0.8354 second, peak time of 4.9615 seconds, settling time of 1.2240 seconds, final value (actual speed) of 1724.9 rpm, steady state error 0.1 rpm. Simulation comparison with conventional PID controller showed that the PID yielded a rise time of 1.6723 seconds, peak time of 4.5475 seconds, peak overshoot of 0.6913 %, settling time of 2.9646 seconds, final value (actual speed) 1736.1 rpm, and steady state error of 11.1 rpm. Generally, simulation results indicated that the intelligent (FLC) aided classical PID control improve the system performance and achieved the rated speed of the motor.

Performance analysis of proportional integral derivative controller for frequency regulation of an interconnected power system integrated with renewable energy sources

This paper focuses on conducting a performance analysis of classical proportional integral derivative (PID) controller for frequency regulation in multi-area interconnected power system. The power system considered in the analysis includes wind and solar power sources integrated with thermal and hydal system. In area 1 along with thermal unit, WPG is appended and in second control area, a hydal unit is appended with SPG. The simulation of the present work is performed using MATLAB/Simulink. The dynamic responses obtained using the simulation using PID controller and without any controller have been compared. It is clearly demonstrated from the results that implementation of a PID controller effectively reduces the fluctuations in the system and hence, aids in order to maintain constant frequency.

Active power control of autonomous hybrid power system using two degree of freedom PID controller

This paper shows an approach of active power control stratagem for an islanded Hybrid Power System (hPS) comprising of dish-stirling engine solar thermal system, wind accumulated turbine generator, diesel supplied generators, aqua electrolyzer with fuel cells and battery unit. Due to the intermittent output of DSTS and WTG as well as the load fluctuations in such a hPS, there is a rising demand for an appropriate control strategy. The Two-Degree-of-Freedom (2DOF) controllers are employed for the considered autonomous hPS so as to contain the frequency fluctuation under varying conditions. The controller gains are tuned by leveraging Flower Pollination Technique (FPA). The system’s dynamic reveal that 2DOF-Proportional–Integral–Derivative controller performs better to reduce fluctuations and stabilized time factor as compared to classical proportional–integral (PI), classical Proportional–Integral–Derivative (PID), and 2DOF-PI controllers. A Sensitivity assessment is performed to check the sturdiness of the optimized controller gains as well. Results show that the proposed active power control scheme addresses intermittent output of renewable generation and operates in a very robust way with very limited oscillations introduced to the system. In this fashion, more renewable generation can be deployed with stability and reliability.

Proportional-integral-derivative controller optimization by particle swarm optimization and back propagation neural network for a parallel stabilized platform in marine operations

The parallel stabilized platform is an important equipment to ensure the stability of marine operations, which can effectively mitigate collisions of lifted goods. The 3UPU_UP parallel stabilized platform system is proposed, and the kinematics and dynamics of the 3UPU_UP parallel stabilized platform are researched, where U denotes universal joint; P denotes prismatic joint. The platform motion can be realized by controlling the actuators to realize compensation ship motion. While conventional control algorithms are difficult to cope with complex nonlinear systems, the optimization control method has been proposed. Based on the back propagation (BP) neural network proportional-integral-derivative (PID) controller, the particle swarm optimization (PSO) is combined with the BP neural network, and the combined PSO-BP PID controller optimization algorithm is introduced into the servo control system. Compared with classic PID and BP PID, the overshoot of PSO-BP PID to the response of step signal is reduced by 10.2% and 11.48%, respectively, and 0.36s and 0.17s reduce the time required to reach stable states. By comparing the reduction of the error under various sea conditions, the upper platform tracking error curve is flatter under a PID controller based on PSO-BP, the tracking motion is closer to the theoretical value and the control accuracy is improved. The maximum tracking error of the upper platform is smaller, the overall motion vibration of the platform is reduced, and the anti-interference ability is enhanced. The PSO-BP PID controller for wave compensated platform servo system control is more reasonable, more adaptable to the complex and changing environment.

The Prescribed Fixed Structure Intelligent Robust Control of an Electrohydraulic Servo System

The paper is focused to design a robust and a simple fixed structure H ∞ control synthesis for an Electrohydraulic Servo System (EHSS) and comparing its performance with the conventional H ∞ controller and the classical proportional integral derivative (PID) controller. The precise position tracking of the piston load of EHSS system using classical PID controller is a difficult task due to some non-linear properties of EHSS and the dynamic behaviour of its parameters. The conventional H ∞ controllers also have constraints on gain ( H ∞ norm) for typical design requirement such as the speed of response, higher order, complex structure, control bandwidth, and robust stability. To overcome the above limitations, the proposed synthesis is formulated in the MATLAB. The proposed synthesis has fixed order and fixed structure and is also a linear robust controller. The control parameters of proposed synthesis are tuned using Genetic Algorithm optimization in MATLAB. Another advantage of this design is to use higher order complex shaping fillers to shape the close loop sensitivity S s and complementary sensitivity T s for the desired robust performance, without effecting the structure fixed of proposed synthesis. The order of the proposed controller is not affected by using the higher order complex weights transfer functions W s s and W T s on sensitivity and complementary sensitivity, respectively, whereas the structure of the conventional H ∞ controller becomes more complex due to higher order shaping weights. Model of the Electrohydraulic Servo System is validated experimentally, and results of proposed robust synthesis are compared with conventional H ∞ controller and the classical PID.

Numerical and experimental verification of the single neural adaptive PID real-time inverse method for solving inverse heat conduction problems

For the problems of difficult on-line tuning of PID parameters, weak anti-interference ability and poor self-adaptive ability in the classical proportional-integral-derivative (PID) inverse method, combining single artificial neuron with PID inverse algorithm, a single neural adaptive PID (SNA-PID) inverse method is established to estimate the thermal boundary condition. Compared with the classical PID inverse method, the main difference is that SNA-PID can adaptively adjust the weights of PID parameters. In the SNA-PID inverse method, firstly, the deviation signal between the output of the direct heat conduction model and the measured temperature and its first-order difference and second-order difference are taken as three inputs of a single neuron (dendritic input signal of neuron), and the connection strength of each input signal is determined according to the updated weight of the adaptive learning algorithm, The excitation value of the boundary heat flux increment of the heat conduction system is generated according to the linear truncation function. Finally, the real-time estimation of boundary heat flux of heat conduction system is realized. The unsteady boundary heat flux of one-dimensional heat conduction object is estimated through numerical and experimental tests, which verifies the effectiveness of the proposed SNA-PID inverse method.

EFFECT OF RISK-SENSITIVE STOCHASTIC OPTIMAL CONTROL WITH TRACKING IN AN EVAPORATOR

This work presents an application of the Risk-Sensitive (R-S) control with tracking applied to a stochastic nonlinear system which models the operation of an electronic expansion valve (EEV) in a conventional evaporator. A novel dynamical stochastic equation represents the mathematical model of the evaporator system. The R-S stochastic optimal problem consists of the design of an optimal control u(t) such that the state reaches setpoint values (SP) and minimizes the exponential quadratic cost function. The presence of disturbances and errors in the sensor measurements is represented by Gauss white noise in the state equation, with the coefficient v(e/(2?^2 )) . One novel characteristic in this proposal is that the coefficient of the control into the state equation contains the state term. The error and exponential quadratic cost function show that the R-S control has a better performance versus the classical PID (Proportional, Integral Derivative) control. Key Words: Optimal Risk-Sensitive control with tracking, modelling of the evaporator.

Optimal Tuning of FOPID-Like Fuzzy Controller for High-Performance Fractional-Order Systems

This paper addresses improvements in fractional order (FO) system performance. Although the classical proportional–integral–derivative (PID)-like fuzzy controller can provide adequate results for both transient and steady-state responses in both linear and nonlinear systems, the FOPID fuzzy controller has been proven to provide better results. This high performance was obtained thanks to the combinative benefits of FO and fuzzy-logic techniques. This paper describes how the optimal gains and FO parameters of the FOPID controller were obtained by the use of a modern optimizer, social spider optimization, in order to improve the response of fractional dynamical systems. This group of systems had usually produced multimodal error surfaces/functions that occasionally had many variant local minima. The integral time of absolute error (ITAE) used in this study was the error function. The results showed that the strategy adopted produced superior performance regarding the lowest ITAE value. It reached a value of 88.22 while the best value obtained in previous work was 98.87. A further comparison between the current work and previous studies concerning transient-analysis factors of the model’s response showed that the strategy proposed was the only one that was able to produce fast rise time, low-percentage overshoot, and very small steady-state error. However, the other strategies were good for one factor, but not for the others.

A Moth-Flame Optimized Robust PID controller for a SEPIC in Photovoltaic Applications

The paper presents a robust proportional integral derivative (PID) controller improved by the metaheuristic moth-flame optimization (MFO) technique for a DC-DC single-ended primary inductor converter (SEPIC) to achieve aimed user output performance in photovoltaic systems. Having non-linear dynamics, SEPIC with the conventional linear PID controllers possessing constant tuning gains cannot exhibit robust performance under the input and load variations. To confront the problem, a process-adaptive PID controller, whose tuning gains are adjusted by the MFO algorithm to ensure the continuity and stability of output voltage and the power, is designed and simulated. Comparative performance analysis of MFO tuned PID controller compared with the classic PID controller is conducted. The simulation results reveal a substantial performance improvement of the MFO-PID controller over the traditional PID controller in terms of maintaining constant regulated output voltage and having excellent setpoint tracking capability with minimal settling times and overshoot under considerable input and load disturbances.

Design and Implementation of Composed Position/Force Controllers for Object Manipulation

In the design of a controller for grasping objects through a robotic manipulator, there are two key problems: to find the position of the object to be grasped accurately, and to apply the appropriate force to each finger to handle the object properly without causing undesirable movement of it during its manipulation. A proportional-integral-derivative (PID) controller is widely used to grasp objects in robotics; however, its main shortcomings are its sensitivity to controller gains, sluggish response, and high starting overshooting. This research presents three coupled (position/force) controllers for object manipulation using an assembled robotic manipulator (i.e., a gripper attached to a robotic arm mounted on a mobile robot). Specifically, an angular gripper was employed in this study, which was composed of two independent fingers with a piezoelectric force sensor attached to each fingertip. The main contributions of this study are the designs and implementations of three controllers: a classic PID controller, a type-I controller, and a type-II fuzzy controller. These three controllers were used to find an object to be grasped properly (position) and apply an equivalent force to each finger (force).

Artificial Fuzzy-PID Gain Scheduling Algorithm Design for Motion Control in Differential Drive Mobile Robotic Platforms.

Mobile robots are promising devices which are dedicated to human comfort in all areas. However, the control algorithm of the wheels of mobile robot is entirely challenging due to the nonlinearity. Recently, the classical PID (proportional-integral-derivative) controllers are frequently used in robotics for their high accuracy and the smooth determination of their parameters. A robust approach called fuzzy control which is based on the conversion of linguistic inference sets in a suitable control value is a widely used method in industrial system control in our days. A new challenging method to solve the problem of intelligent navigation of nonholonomic mobile robot is suggested. In this work, the presented methodology is based on three hybrid fuzzy logic PID controllers which are adapted to guarantee target achievement and trajectory tracking. A fuzzy-PID control algorithm is designed with 2 inputs and 3 outputs. By the information given by the system response, error and error derivate can be used to extract and adopt the PID controller parameters: proportional, integral, and derivative gains. Besides, a tuning value A is introduced to improve the resulted response in terms of speeding up and reducing error, overshoot, and oscillation, as well as reducing ISE and IAE values. A modelization of a differential drive mobile robot is presented. The developed algorithm is tested and implemented to this mobile robot model via Simulink/MATLAB.

On-Line Auto-Tuning Robust Nonlinear Neural PID Controller Design for Double-Pipe Heat Exchanger Based on Particle Swarm Optimization

The heat exchanger systems have highly nonlinear features between its variables and time delay. In addition, the parameters of them change constantly during operation. Therefore, the heat exchangers need nonlinear robust controller for obtaining required outlet temperature under all operating conditions.In this paper, a robust nonlinear neural PID (NLNPID) controller is proposed for temperature control of double-pipe heat exchanger. Particle swarm optimization (PSO) technique is used to determine on line auto tuning the optimal parameters of proportional-integral-derivative (PID) controller. Mathematical model of heat exchanger takes into account the changes in the density, thermal conductivity, viscosity, specific heat and convection heat transfer coefficient of the fluid which are resulting from changing in temperatures and the velocities with classical PID control. Simulations results show that the performance of the NLNPID controller produces good responses features and give desired outlet temperature under all operating conditions without over shoot and fluctuating.

Control of Nonlinear Uncertain Systems by Extended PID

Since the classical proportional-integral-derivative (PID) controller is the most widely and successfully used ones in industrial processes, it is of vital importance to investigate theoretically the rationale of this ubiquitous controller in dealing with nonlinearity and uncertainty. Recently, we have investigated the capability of the classical PID control for second-order nonlinear uncertain systems, and provided some analytic design methods for the choices of PID parameters, where the system is assumed to be in the form of cascade integrators. In this article, we will consider the natural extension of the classical PID control for high-order affine-nonlinear uncertain systems. In contrast to most of the literature on controller design of nonlinear systems, we do not require such special system structures as normal or triangular forms, thanks to the strong robustness of the extend PID controller. To be specific, we will show that under some suitable conditions on nonlinearity, and uncertainty of the systems, the extended PID controller can semiglobally stabilize the nonlinear uncertain systems, and at the same time the regulation error converges to zero exponentially fast, as long as the control parameters are chosen from an open unbounded parameter manifold constructed in this article.

Hybrid feedback PID-FxLMS algorithm for active vibration control of cantilever beam with piezoelectric stack actuator

FxLMS (Filtered-x Least Mean Square) algorithm and PID (Proportional Integral Derivative) controller have been widely used for AVC (Active Vibration Control). Yet, the convergence rate and vibration suppression performance are restricted by each other for both the classical FxLMS algorithm and the traditional PID controller. A hybrid PID-FxLMS algorithm which combines the feedback FxLMS algorithm and PID controller is proposed, and applied to improve the vibration control efficiency of piezoelectric cantilever beam. In order to conveniently install the piezoelectric stack on the beam structures, a new piezoelectric stack actuator is designed and installed at the root of cantilever beam. Meanwhile, a coupled finite element model of the piezoelectric stack actuator and cantilever beam is established in ABAQUS, and the state-space model of the coupled system is obtained. Subsequently, based on the obtained state-space model, an AVC simulation and experimental system is built to verify the effectiveness of the proposed algorithm. Numerical and experimental results show that convergence rate and vibration suppression performance of the hybrid PID-FxLMS algorithm are much better than that of the classical FxLMS algorithm or traditional PID controller alone, while the hybrid controller also has strong adaptability and anti-noise ability.

Nonlinear rotor side converter control of DFIG based wind energy system

The present study proposes an alternative approach following command-filtered integral backstepping principle to control the rotor side converter (RSC) of a doubly-fed induction generator (DFIG) based wind energy conversion system (WECS). This controller provides better benefit compared to the classical backstepping controller; since the errors introduced by the input signal differentiation is eliminated. This controller essentially stabilises the rotor speed by controlling the electromagnetic torque. The global stability of the overall closed loop system is established using Lyapunov criteria. Moreover, the parameters of the controller are optimally selected using comprehensive learning particle swarm optimisation (CLPSO) technique. The performance of the controller is compared with two other controllers, which include a classical proportional integral derivative (PID) and ΔΣ–based PID. The results of comparative investigation demonstrate that the proposed command-filtered integral backstepping controller is superior and could effectively control the RSC under various fault conditions.

Design and Comparison of Strategies for Level Control in a Nonlinear Tank

In this work, a study of the water level control of an inverted conical tank system is presented. This type of tank has highly nonlinear mathematical and dynamic characteristics. Four control strategies are designed, applied, and compared, namely classical Proportional–Integral–Derivative (PID), Gain Scheduling (GS), Internal Model Control (IMC), and Fuzzy Logic (FL). To determine which of the designed control strategies are the most suitable for an inverted conical tank, a comparative study of the behavior of the system is carried out. With this purpose, and considering situations much closer to reality, a variety of scenarios, such as step responses, random input disturbances, and momentary load disturbances, are conducted. Additionally, performance indexes (error- and statistics-based) are calculated to assess the system’s response.