scholarly journals Design, implementation, control and optimization of single stage pilot scale reverse osmosis process

2021 ◽  
Author(s):  
G. Guna ◽  
D. Prabhakaran ◽  
M. Thirumarimurugan
Keyword(s):  
Objective Function ◽  
Real Time ◽  
Reverse Osmosis ◽  
Absolute Error ◽  
Pilot Scale ◽  
Single Stage ◽  
Proportional Integral Derivative ◽  
Proportional Integral ◽  
Squared Error ◽  
Integral Time

Abstract In this paper, a single-stage pilot-scale RO (Reverse Osmosis) process is considered. The process is mainly used in various chemical industries such as dye, pharmaceutical, Beverage, and so on. Initially, mathematical modeling of the process is to be done followed by linearization of the system. Here a dual loop construction with a master and a slave is used. The slave uses the conventional PID (Proportional Integral Derivative) with a reference model of the RO process and the master uses the FOPID (Fractional Order Proportional Integral Derivative) with a real time RO process. The slave's output is compared with output of the real time RO process to obtain the error which is in turn used to tune the master. The slave controller is tuned using Ziegler Nicholas method and the error criterion such as IAE (Integral Absolute Error), ISE (Integral Squared Error), ITSE (Integral Time Squared Error), ITAE (Integral Time Absolute Error) are calculated and the minimum among them was chosen as the objective function for the master loop tuning. Hence the tuning of the controller becomes a whole. Therefore two optimization techniques such as PSO (Particle Swarm Optimization) and Bacterial Foraging Optimization Algorithm (BFO) are used for the tuning of the master loop. From the calculations the ITSE was having the minimum value among the performance indices hence it was used as the objective function for the BFO and PSO. The best-tuned values will be obtained with the use of these techniques and the best among all can be considered for various industrial applications. Finally, the performance of the process is compared with both techniques and BFO outperforms the PSO from the simulations.

scholarly journals Flight Motion Controller Design using Genetic Algorithm for a Quadcopter

2018 ◽  
Vol 51 (3-4) ◽  
pp. 59-64 ◽  
Author(s):  
Huu Khoa Tran ◽  
Thanh Nam Nguyen
Keyword(s):  
Genetic Algorithm ◽  
Real Time ◽  
Controller Design ◽  
Fitness Function ◽  
Absolute Error ◽  
Proportional Integral Derivative ◽  
Proportional Integral ◽  
Time Operation ◽  
Real Time Operation ◽  
Effective Stability

In this study, the Genetic Algorithm operability is assigned to optimize the proportional–integral–derivative controller parameters for both simulation and real-time operation of quadcopter flight motion. The optimized proportional–integral–derivative gains, using Genetic Algorithm to minimum the fitness function via the integral of time multiplied by absolute error criterion, are then integrated to control the quadcopter flight motion. In addition, the proposed controller design is successfully implemented to the experimental real-time flight motion. The performance results are proven that the highly effective stability operation and the reliable of waypoint tracking.

Two-loop controller design and implementations for an inverted pendulum system with optimal self-adaptive fuzzy-proportional–integral–derivative control

2021 ◽  
pp. 014233122110403
Author(s):  
Tayfun Abut ◽  
Servet Soyguder
Keyword(s):  
Pid Control ◽  
Inverted Pendulum ◽  
Absolute Error ◽  
Performance Criteria ◽  
Control Methods ◽  
Proportional Integral Derivative ◽  
Proportional Integral ◽  
Adaptive Fuzzy ◽  
Squared Error ◽  
Self Adaptive

Since inverted pendulum systems (IPSs) are under-actuated systems, controlling these systems has become an important problem. Because of their irregular movements and being structurally unstable, the balancing of these systems poses a major problem. In this study, the dynamic model of a linear IPS has been obtained by using the Newton–Euler method and also conventional proportional–integral–derivative (PID), fuzzy logic (FLC), and self-adaptive fuzzy-proportional–integral–derivative (SAF-PID) control algorithms have been designed for the control of the system. The purpose of these designed controllers is to keep the arms of the IPS on the moving cart in a vertical position and bring the cart to the specified equilibrium position. In conventional control methods, unwanted oscillations have occurred during the movement of the cart. By using two-loop control methods, it is aimed to reduce these oscillations and to design a robust system. The results have been obtained using the conventional PID, FLC, and SAF-PID controllers. To increase the performance of the SAF-PID control type, the optimum value of the coefficients of the points where the legs of the membership functions touch were calculated using the firefly optimization algorithm. To conclude, the designed controllers were performed on computer simulation. The results are given graphically and numerically. Mean absolute error (MAE), mean squared error, integral absolute error IAE, and integral squared error have been compared and examined with each other and with the studies in the literature using performance criteria. As a result, the proposed control method was at least 26.31% more successful in the angular control of the pendulum and at least 9.26% better in the position control of the cart than the studies in the literature.

Dynamic modeling, system identification and comparative study of various control strategies for a spatial parallel manipulator

2021 ◽  
pp. 095965182110320
Author(s):  
Mervin Joe Thomas ◽  
Shoby George ◽  
Deepak Sreedharan ◽  
ML Joy ◽  
AP Sudheer
Keyword(s):  
Real Time ◽  
Parallel Manipulator ◽  
Control Strategies ◽  
Proportional Integral Derivative ◽  
Real Time Control ◽  
Model Predictive Controller ◽  
End Effector ◽  
Proportional Integral ◽  
Time Control ◽  
Predictive Controller

The significant challenges seen with the mathematical modeling and control of spatial parallel manipulators are its difficulty in the kinematic formulation and the inability to real-time control. The analytical approaches for the determination of the kinematic solutions are computationally expensive. This is due to the passive joints, solvability issues with non-linear equations, and inherent kinematic constraints within the manipulator architecture. Therefore, this article concentrates on an artificial neural network–based system identification approach to resolve the complexities of mathematical formulations. Moreover, the low computation time with neural networks adds up to its advantage of real-time control. Besides, this article compares the performance of a constant gain proportional–integral–derivative (PID), variable gain proportional–integral–derivative, model predictive controller, and a cascade controller with combined variable proportional–integral–derivative and model predictive controller for real-time tracking of the end-effector. The control strategies are simulated on the Simulink model of a 6-degree-of-freedom 3-PPSS (P—prismatic; S—spherical) parallel manipulator. The simulation and real-time experiments performed on the fabricated manipulator prototype indicate that the proposed cascade controller with position and velocity compensation is an appropriate method for accurate tracking along the desired path. Also, training the network using the experimentally generated data set incorporates the mechanical joint approximations and link deformities present in the fabricated model into the predicted results. In addition, this article showcases the application of Euler–Lagrangian formalism on the 3-PPSS parallel manipulator for its dynamic model incorporating the system constraints. The Lagrangian multipliers include the influence of the constraint forces acting on the manipulator platform. For completeness, the analytical model results have been verified using ADAMS for a pre-defined end-effector trajectory.

scholarly journals PERANCANGAN ALAT PENGGERAK ANTENA MENGGUNAKAN METODE KONTROL PROPORTIONAL, INTEGRAL, DERIVATIVE (PID) UNTUK MELACAK OBJEK BERGERAK

Transmisi ◽  
2018 ◽  
Vol 20 (2) ◽  
pp. 71
Author(s):  
Bagus Bernadi Saputra ◽  
Wahyudi Wahyudi ◽  
Sudjadi Sudjadi
Keyword(s):  
Global Positioning System ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Base Station ◽  
Ground Control ◽  
Positioning System ◽  
Proportional Integral Derivative ◽  
Proportional Integral ◽  
Global Positioning ◽  
Aerial Vehicle

Base station atau Ground Control Station (GCS) umumnya menggunakan antena directional untuk dapat berkomunikasi dengan objek bergerak seperti roket dan Unmanned Aerial Vehicle (UAV). Antena directional memiliki jarak jangkau yang jauh, namun memiliki sudut pancar yang sempit. Untuk mengatasi kekurangan dari antena directional, diperlukan alat yang dapat menggerakkan antena ke arah objek bergerak secara nyata pada kisaran sudut azimut dan elevasi. Pada penelitian ini, dirancang alat penggerak antena menggunakan metode kontrol Proportional, Integral, dan Derivative (PID) untuk melacak objek bergerak berbasis Global Positioning System (GPS) dan sensor barometer. Dari hasil perancangan dengan menggunakan nilai parameter PID yang digunakan pada sudut elevasi (Kp=0,03, Ti=150, dan Td=0,22) menghasilkan plant yang mampu mencapai setpoint (74o) dalam waktu 2 detik. Parameter PID yang digunakan pada sudut azimut (Kp=3,5, Ti=100, dan Td=0,09) menghasilkan plant yang mampu mencapai setpoint (180o) dalam waktu 1,1 detik. Dari hasil pengujian, diketahui antena dapat mengikuti objek bergerak (drone) dengan waktu terlama 1 detik pada plant azimut dan 1,5 detik pada plant elevasi. Plant elevasi memiliki Mean Absolute Error (MAE) = 6,54o dan plant azimut memiliki MAE = 8,04o.

Assessment of Proportional Integral Derivative Control Loops for Large Dominant Time Constant Processes

2019 ◽  
Vol 15 (1) ◽  
Author(s):  
K. Ghousiya Begum ◽  
A. Seshagiri Rao ◽  
T. K. Radhakrishnan
Keyword(s):  
Time Constant ◽  
Closed Loop ◽  
Direct Synthesis ◽  
Absolute Error ◽  
Assessment Method ◽  
Proportional Integral Derivative ◽  
Control Loops ◽  
Proportional Integral ◽  
Proportional Integral Derivative Control ◽  
Integrating Process

Abstract This manuscript deals with the assessment of parallel form of proportional integral derivative (PID) control structure for tracking the reference input designed for large dominant time constant processes whose dynamics are slow (integrating processes). The theoretical bound of integral absolute error (IAE) which is established for unstable first order process is extended to pure integrating process without using any approximations. This relies on direct synthesis tuning (DS) and the theoretical bound is obtained from the transfer function of closed loop system subjected to ramp input changes. An error based performance index is formulated on the basis of this IAE theoretical bound and actual IAE, to measure the behaviour of the controller employed for non self regulating (integrating) processes. This error based index evaluates the performance of closed loop controller and specifies whether the controller requires retuning or not. A sequence of simulated examples is used to illustrate the benefit and effectiveness of this new performance assessment method.

Investigation and performance comparison of ride comfort on the created human vehicle road integrated model adopting genetic algorithm optimized proportional integral derivative control technique

2020 ◽  
Vol 234 (2) ◽  
pp. 288-305 ◽  
Author(s):  
Arivazhagan Anandan ◽  
Arunachalam Kandavel
Keyword(s):  
Genetic Algorithm ◽  
Ride Comfort ◽  
Active Suspension ◽  
Absolute Error ◽  
Integrated Model ◽  
Suspension System ◽  
Vehicle Stability ◽  
Head Acceleration ◽  
Proportional Integral Derivative ◽  
Proportional Integral

This context exhaustively investigates the ride comfort performance index on the proposed active suspension vehicle system. Ride comfort in terms of occupants (includes driver and passenger) head acceleration, sprung mass vertical and pitching accelerations is considered. For this examination, a 14-degree-of-freedom human vehicle road integrated system model was extensively developed. Then, an active suspension system composed of a hydraulic actuator and proportional-integral-derivative controller is incorporated into the developed vehicle model to enhance the ride comfort. Besides, the designed controller needs to satisfy other vehicle performance indices like vehicle stability and ride safety. Accordingly, the controller parameters were optimally tunned with the help of genetic algorithm technique, on the basis of integral time absolute error criterion. The objective function was created on the basis of minimizing the integral time absolute error of sprung mass displacement, suspension working space and tire deflection responses. The entire response of human vehicle road integrated model, with the proposed active suspension system and passive suspension system on various random road surfaces (A, B, C, D and E with respect to ISO 8608) with five constant speeds (20, 40, 60, 80 and 100 kmph), was compared via surficial presentation. Furthermore, the comfort measures such as root mean square and vibration dose value from ISO 2631-1 were adopted to evaluate the severity between the occupants via head acceleration response. The simulation results showed that the suggested active suspension system significantly improved the ride comfort with guaranteed vehicle stability and ride safety.

Comparative Study of Fuzzy PID and PID controller optimized with Spider Monkey Optimization for a Robotic Manipulator System

2019 ◽  
Vol 12 ◽  
Author(s):  
Alka Agrawal ◽  
Vishal Goyal ◽  
Puneet Mishra
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Logic ◽  
Optimization Technique ◽  
Absolute Error ◽  
Robotic Manipulator ◽  
Spider Monkey ◽  
Proportional Integral Derivative ◽  
Proportional Integral Derivative Controller ◽  
Proportional Integral ◽  
Spider Monkey Optimization

Background: Robotic manipulator system has been useful in many areas like chemical industries, automobile, medical fields etc. Therefore, it is essential to implement a controller for controlling the end position of a robotic armeffectively. However, with the increasing non-linearity and the complexities of a robotic manipulator system, a conventional Proportional-Integral-Derivative controller has become ineffective. Nowadays, intelligent techniques like fuzzy logic, neural network and optimization algorithms has emerged as an efficient tool for controlling the highly complex non-linear functions with uncertain dynamics. Objective: To implement an efficient and robustcontroller using Fuzzy Logic to effectively control the end position of Single link Robotic Manipulator to follow the desired trajectory. Methods: In this paper, a Fuzzy Proportional-Integral-Derivativecontroller is implemented whose parameters are obtainedwith the Spider Monkey Optimization technique taking Integral of Absolute Error as an objective function. Results: Simulated results ofoutput of the plants controlled byFuzzy Proportional-Integral-Derivative controller have been shown in this paper and the superiority of the implemented controller has also been described by comparing itwith the conventional Proportional-Integral-Derivative controller and Genetic Algorithm optimization technique. Conclusion: From results, it is clear that the FuzzyProportional-Integral-Derivativeoptimized with the Spider monkey optimization technique is more accurate, fast and robust as compared to the Proportional-Integral-Derivativecontroller as well as the controllers optimized with the Genetic algorithm techniques.Also, by comparing the integral absolute error values of all the controllers, it has been found that the controller optimized with the Spider Monkey Optimization technique shows 99% better efficacy than the genetic algorithm technique.

Enhanced variable universe fuzzy proportional–integral–derivative control of structural vibration with real-time adaptive contracting–expanding factors

2021 ◽  
pp. 107754632110026
Author(s):  
Gang Liu ◽  
Wei Jiang ◽  
Qi Wang ◽  
Tao Wang
Keyword(s):  
Real Time ◽  
Control Method ◽  
Magnetorheological Damper ◽  
Frame Structure ◽  
Structural Vibration ◽  
Proportional Integral Derivative ◽  
Proportional Integral ◽  
Variable Universe ◽  
Proportional Integral Derivative Control ◽  
Derivative Control

A conventional variable universe fuzzy proportional–integral–derivative control approach is widely used for semi-active control in mechanical engineering. The performance of the controller is dependent on an optimal selection of parameters of the contracting–expanding factors. An improved variable universe fuzzy proportional–integral–derivative control algorithm is developed in this study where these parameters are automatically determined in real-time according to the error in the controlled responses and its change rate based on fuzzy logic control. The proposed method is numerically and experimentally illustrated with a three-story frame structure with a magnetorheological damper. The amplitude of displacement, velocity, and acceleration at all floor levels under the proposed control method are smaller than those obtained from existing proportional–integral–derivative, fuzzy, and conventional variable universe fuzzy methods.

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

2019 ◽  
Vol 26 (11-12) ◽  
pp. 976-988 ◽  
Author(s):  
Mustafa S Ayas ◽  
Erdinc Sahin ◽  
Ismail H Altas
Keyword(s):  
Trajectory Tracking ◽  
Absolute Error ◽  
Parallel Manipulators ◽  
Stewart Platform ◽  
High Order ◽  
Feedback Controller ◽  
Proportional Integral Derivative ◽  
Proportional Integral Derivative Controller ◽  
Proportional Integral ◽  
Reference Trajectories

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.

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