Proportional, Integral And Derivative Parameters Research Articles

PID Tuning and Stability Analysis of Hybrid Controller for Robotic Arm Using ZN, PSO, ACO, and GA

This paper aims to tune and implement the Hybrid controller for the motion control of robot manipulators using different soft computing techniques. The hybrid controller is the combination of the PID (Proportional, Integral, and Derivative) and overwhelming controller. The different soft computing techniques such as Ziegler-Nichols (ZN), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Genetic Algorithm (GA) are utilized to tune the parameters of the PID. The simulation environment MATLAB has been used to obtain the results for different soft computing techniques, which are compared to obtain the best-tuned parameters. The results of the comparison have indicated that the GA performs exceptionally better than other optimization techniques. The Routh Hurwitz criterion is also used to analyze and validate the stability and efficacy of the proposed work. The tuned PID parameters are applied to a single-arm robot manipulator, which is interfaced by MATLAB Simulink. The results obtained through simulation have revealed that the proposed method is superior to traditional methods.

Speed Control of DC Motor under Reverse Torque Disturbance with Ant Colony Optimized PID Controller

Direct Current (DC) motors are widely used in industrial systems due to their high torque. In ensuring the stability and productivity of a system, it is important that the DC motor within the automation system reaches the reference speed value quickly and its speed remains constant under load. In this study, it is aimed to keep the speed value of DC motor constant under load by optimizing the gain parameters of the Proportional, Integral and Derivative (PID) controller, which is widely used in industrial applications. In the optimization of these parameters, the Ziegler Nichols method (ZNM) and the Ant Colony Optimization method (ACO) were examined comparatively in the simulation environment. PID parameters were determined by open loop responses under the running system with the ZNM. On the other hand, the most optimum solution was obtained among many parameters with the ACO method. Speed control of DC motor was performed with PID controller parameters which are determined according to the best ACO response. Simulation results are presented in comparison with the parameters of settling time, peak time, rising time and response of the system under load. As a result, PID controller run with Kp, Ki, and Kd parameters obtained by ACO algorithm generally gave better results than ZNM.

Real Time Optimal Tuning of Quadcopter Attitude Controller Using Particle Swarm Optimization

A real-time novel algorithm for proportional, integral and derivative (PID) controller tuning for quadcopters is introduced. The particle swarm optimization (PSO) method is utilized to search the quadcopter solution space to find the best PID controller parameters. A fuzzy logic (FL) controller is used to provide proper velocity reference signals to serve as tracking set points to be achieved by the PID controller. This nested loop design is proposed for stabilizing the quadcopter, where the fuzzy logic controller (FL) is used in the stable loop (i.e. outer loop) to control the desired angle, while the PID controller is used for the rate loop (i.e. inner loop). Finally, the optimum generated PID parameters were achieved in real time using the PSO search algorithm. The generated parameters were tested successfully using an experimental quadcopter setup at the University of Jordan.

Comparison Performances of PSO and GA to Tuning PID Controller for the DC Motor

A DC motor widely uses for sensitive speed and position in industry. Stability and productivity of a system are important for controlling of a DC motor speed. Stable of speed which affected from load fluctuation and environmental factors. Therefore, it is important for the speed value which is required as constant and to keep it as its value. In this study, it is aimed that the speed value which is achieved as required value and keeping it as constant using Proportional, Integral and Derivative (PID) controller for tuning parameters. Firstly, Ziegler-Nichols (ZN) is one of a traditional method used. PID parameters are determined with responses of open-loop under running system. Later, parameters of the PID are estimated using two metaheuristic algorithms such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). As a result, three algorithms’ results are compared based on five criteria. The PSO algorithm produces better results than Genetic Algorithm for each criteria.

The modelling and control of the drive system of an Ackermann Robot using GA optimization

This paper provides the mathematical modelling and control optimization, of the drive system of an Ackermann four wheeled autonomous robot, with Genetic algorithm used for tuning the proportional, integral and derivative (PID) Controller parameters. The aim and main objective of this work is focused on the control of the driving speed input from the rear wheels of the robot and control. The robot drive in proportion to obstacle input ahead of the four wheeled chassis using genetic algorithms. A controlled platform that can be deployed for driverless vehicle in the nearest future and military unmanned vehicle is our major concern. The controlled system response stabilized in 0.675 seconds, after exciting the system with a step response. Variation for the system also shows, that the cost function was minimized or adjusted to obtain optimal PID parameters as Proportional (P) = 12.671, Integral (I) = -0.399, Derivative (D) = 1477561, at a value of 9.6778*10-4.Keywords: Ackermann steering, modelling, optimization, Genetic Algorithm, control, PID, driverless vehicle

A 2-Dof LQR based PID controller for integrating processes considering robustness/performance tradeoff

This paper focuses on the analytical design of a Proportional Integral and Derivative (PID) controller together with a unique set point filter that makes the overall Two-Degree of-Freedom (2-Dof) control system for integrating processes with time delay. The PID controller tuning is based on the Linear Quadratic Regulator (LQR) using dominant pole placement approach to obtain good regulatory response. The set point filter is designed with the calculated PID parameters and using a single filter time constant (λ) to precisely control the servo response. The effectiveness of the proposed methodology is demonstrated through a series of illustrative examples using real industrial integrated process models. The whole range of PID parameters is obtained for each case in a tradeoff between the robustness of the closed loop system measured in terms of Maximum Sensitivity (Ms) and the load disturbance measured in terms of Integral of Absolute Errors (IAE). Results show improved closed loop response in terms of regulatory and servo responses with less control efforts when compared with the latest PID tuning methods of integrating systems.

Tuning PID attitude stabilization of a quadrotor using particle swarm optimization (experimental)

Proportional, Integral and Derivative (PID) controllers are the most popular type of controller used in industrial applications because of their notable simplicity and effective implementation. However, manual tuning of these controllers is tedious and often leads to poor performance. The conventional Ziegler-Nichols (Z-N) method of PID tuning was done experimentally enables easy identification stable PID parameters in a short time, but is accompanied by overshoot, high steady-state error, and large rise time. Therefore, in this study, the modern heuristics approach of Particle Swarm Optimization (PSO) was employed to enhance the capabilities of the conventional Z-N technique. PSO with the constriction coefficient method experimentally demonstrated the ability to efficiently and effectively identify optimal PID controller parameters for attitude stabilization of a quadrotor.

Tuning of IMC based PID controllers for integrating systems with time delay

Design of Proportional Integral and Derivative (PID) controllers based on IMC principles for various types of integrating systems with time delay is proposed. PID parameters are given in terms of process model parameters and a tuning parameter. The tuning parameter is IMC filter time constant. In the present work, the IMC filter (Q) is chosen in such a manner that the order of the denominator of IMC controller is one less than the order of the numerator. The IMC filter time constant (λ) is tuned in such a way that a good compromise is made between performance and robustness for both servo and regulatory problems. To improve servo response of the controller a set point filter is designed such that the closed loop response is similar to that of first order plus time delay system. The proposed controller design method is applied to various transfer function models and to the non-linear model equations of jacketed CSTR to demonstrate its applicability and effectiveness. The performance of the proposed controller is compared with the recently reported methods in terms of IAE and ITAE. The smooth functioning of the controller is determined in terms of total variation and compared with recently reported methods. Simulation studies are carried out on various integrating systems with time delay to show the effectiveness and superiority of the proposed controllers.

Optimal transient droop compensator and PID tuning for load frequency control in hydro power systems

Abstract This paper presents an optimal method to tune the Proportional, Integral and Derivative (PID) controller for a hydraulic turbine coupled with the corresponding Transient Droop Compensator (TDC). The proposed methodology is based on the Desired Time Response Specification (DTRS) of the input guide vane servomotor that includes typical rate limiters and gain saturation in power plants. Therefore, the problem consists of adjusting both the parameters of the controller and compensator such as the time response remains close to the specified one. To avoid suboptimal solutions at local minimum points, it is necessary to solve the resulting non linear problem in two steps: (i) firstly, solve a linear programming (LP) to determine the values of PID & TDC block using state space representation to match the input and output time responses specifications and (ii) determine the final values of the PID and TDC parameters using the previous results in a new non linear programming. The proposed methodology has presented the advantage of tuning the PID coordinated with the TDC spending low computational time. The results show that the performance of the method covers a wide range of operating conditions of the system. Comparisons were also made with existing methods in the literature to show the effectiveness of the proposed methodology.

Recurrent fuzzy neural network by using feedback error learning approaches for LFC in interconnected power system

In this study, a new adaptive controller based on modified feedback error learning (FEL) approaches is proposed for load frequency control (LFC) problem. The FEL strategy consists of intelligent and conventional controllers in feedforward and feedback paths, respectively. In this strategy, a conventional feedback controller (CFC), i.e. proportional, integral and derivative (PID) controller, is essential to guarantee global asymptotic stability of the overall system; and an intelligent feedforward controller (INFC) is adopted to learn the inverse of the controlled system. Therefore, when the INFC learns the inverse of controlled system, the tracking of reference signal is done properly. Generally, the CFC is designed at nominal operating conditions of the system and, therefore, fails to provide the best control performance as well as global stability over a wide range of changes in the operating conditions of the system. So, in this study a supervised controller (SC), a lookup table based controller, is addressed for tuning of the CFC. During abrupt changes of the power system parameters, the SC adjusts the PID parameters according to these operating conditions. Moreover, for improving the performance of overall system, a recurrent fuzzy neural network (RFNN) is adopted in INFC instead of the conventional neural network, which was used in past studies. The proposed FEL controller has been compared with the conventional feedback error learning controller (CFEL) and the PID controller through some performance indices.

Design of methanol Feed control in Pichia pastoris fermentations based upon a growth model.

The methylotrophic yeast Pichia pastoris is an effective system for recombinant protein productions that utilizes methanol as an inducer, and also as carbon and energy source for a Mut(+) (methanol utilization plus) strain. Pichia fermentation is conducted in a fed-batch mode to obtain a high cell density for a high productivity. An accurate methanol control is required in the methanol fed-batch phase (induction phase) in the fermentation. A simple "on-off" control strategy is inadequate for precise control of methanol concentrations in the fermentor. In this paper we employed a PID (proportional, integral and derivative) control system for the methanol concentration control and designed the PID controller settings on the basis of a Pichia growth model. The closed-loop system was built with four components: PID controller, methanol feed pump, fermentation process, and methanol sensor. First, modeling and transfer functions for all components were derived, followed by frequency response analysis, a powerful method for calculating the optimal PID parameters K(c) (controller gain), tau(I) (controller integral time constant), and tau(D) (controller derivative time constant). Bode stability criteria were used to develop the stability diagram for evaluating the designed settings during the entire methanol fed-batch phase. Fermentations were conducted using four Pichia strains, each expressing a different protein, to verify the control performance with optimal PID settings. The results showed that the methanol concentration matched the set point very well with only small overshoot when the set point was switched, which indicated that a very good control performance was achieved. The method developed in this paper is robust and can serve as a framework for the design of other PID feedback control systems in biological processes.

Postural control model interpretation of stabilogram diffusion analysis.

Collins and De Luca [Collins JJ. De Luca CJ (1993) Exp Brain Res 95: 308-318] introduced a new method known as stabilogram diffusion analysis that provides a quantitative statistical measure of the apparently random variations of center-of-pressure (COP) trajectories recorded during quiet upright stance in humans. This analysis generates a stabilogram diffusion function (SDF) that summarizes the mean square COP displacement as a function of the time interval between COP comparisons. SDFs have a characteristic two-part form that suggests the presence of two different control regimes: a short-term open-loop control behavior and a longer-term closed-loop behavior. This paper demonstrates that a very simple closed-loop control model of upright stance can generate realistic SDFs. The model consists of an inverted pendulum body with torque applied at the ankle joint. This torque includes a random disturbance torque and a control torque. The control torque is a function of the deviation (error signal) between the desired upright body position and the actual body position, and is generated in proportion to the error signal, the derivative of the error signal, and the integral of the error signal [i.e. a proportional, integral and derivative (PID) neural controller]. The control torque is applied with a time delay representing conduction, processing, and muscle activation delays. Variations in the PID parameters and the time delay generate variations in SDFs that mimic real experimental SDFs. This model analysis allows one to interpret experimentally observed changes in SDFs in terms of variations in neural controller and time delay parameters rather than in terms of open-loop versus closed-loop behavior.

B-splines neural network assisted PID autotuning

This paper describes an extension of previous work on the subject of neural network proportional, integral and derivative (PID) autotuning. Basically, neural networks are employed to supply the three PID parameters, according to the integral of time multiplied by the absolute error (ITAE) criterion, to a standard PID controller. These networks were previously trained off-line, remaining fixed thereafter. In order to make this approach adaptive, one additional neural network is used here to model the relation between the PID parameters and the plant identification measures to the ITAE value. This model will be afterwards employed in an on-line minimization routine which finds the optimal PID parameters; these will be used to adapt, on-line, the neural networks responsible for the PID parameters.

A digital excitation control system for use on brushless excited synchronous generators

Automatic voltage regulators for small to medium size brushless excited synchronous generators have traditionally utilized analog electronics. Modern voltage regulator systems are beginning to utilize the power, flexibility, and cost advantage of the microprocessor for control. Furthermore, digital voltage regulators have the added advantage of ease of compensator custom-design. This paper presents a new method for determining the stability parameters for small/medium size generators that have limited effect on overall system stability. A digital excitation control system (DECS) is used as a platform for this discussion. DECS utilizes a discrete proportional, integral and derivative (PID) control algorithm. A direct design (pole placement) method for selecting PID parameters is presented along with the test results.