Design and Implementation of Non Linear System Using Gain Scheduled PI Controller

Kumar, D. Dinesh; Meenakshipriya, B.

doi:10.1016/j.proeng.2012.06.361

Cited by 25 publications

(6 citation statements)

References 4 publications

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“…The obtained mathematical models ( 7) & ( 8) are transformed into S domain and after applying the suggested time constants and radius by Ravi et al [15],the transfer functions of TTSIS for the four different regions has been found out [16][17][18][19][20].They are listed in the following Table 4.1.…”

Section: Process Descriptionmentioning

confidence: 99%

An Enhanced Approach of Pso Driven Fa for A Spherical Interacting System

2022

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Storage tanks play a vital role in process industries. Controlling the level in storage tank improves and accelerates the process throughput. PID controllers are pressed into service for control purposes and furthermore to tune the controller PSO driven FA is used here. The system to be analysed is segregated into four sub regions and a dedicated PID controller is employed for each and every region. In the proposed work the PID based PSO driven FA is compared with the conventional method (ZN) with MATLAB and Simulink software. The obtained results are validated in the order of time domain specifications and performance indices such as rise time, peak time, Peak Overshoot, Settling time and ISE, IAE and ITAE respectively. The optimized results are obtained with less number of iterations and with considerably less errors.

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Section: Process Descriptionmentioning

confidence: 99%

An Enhanced Approach of Pso Driven Fa for A Spherical Interacting System

2022

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“…The conical tank linearized transfer function obtained for different operating heights of 30 cm, 50 cm, and 70 cm. It is given in Equations (25)- (27).…”

Section: Case-3: Conical Tankmentioning

confidence: 99%

“…This tuned controller was later used to control the level of the conical tank and first-order model with dead time (FOPDT) process. The gain scheduling was done because it is needed in industrial processes whose dynamics keep changing with variables [27].The contribution of this paper, in particular, is to demonstrate that RTD-A controller parameters tuned using grey wolf optimization (GWO) exhibit peerless performance in controlling nonlinear processes. This means that the tuning of the parameters will become easier and a robust controller is developed which can handle any disturbances and setpoint changes [15].…”

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confidence: 99%

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Gain Scheduling of a Robust Setpoint Tracking Disturbance Rejection and Aggressiveness Controller for a Nonlinear Process

Bagyaveereswaran

Arulmozhivarman

2019

Processes

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In this paper, a robust setpoint tracking disturbance rejection and aggressiveness (RTD-A) controller is designed and developed to control the liquid level of a conical tank process. Meta-heuristic algorithms like grey wolf optimization and the genetic algorithm are used to tune the parameters of the RTD-A controller. Its performance is later compared with that of the conventional standard proportional integral derivative controller. The gain scheduled RTD-A controller is designed and implemented on a nonlinear conical tank process. Also, various performances attributes such as the integral square error, integral absolute error, integral time absolute error, rise time, and settling time are calculated for the first-order process and conical tank process. The servo responses with RTD-A are also compared against the responses recorded from the conventional control schemes.Processes 2019, 7, 415 2 of 20 nonlinear systems deteriorates. It is mentioned that though the PID controller is widely used because of its simplicity and it cannot meet the requirements needed for better performances unless the controller is tuned properly [13]. Since PID controllers are fixed gain controllers, they cannot compensate the parameter variation in the plant and cannot adapt to changes in the environment [14]. Processes with large dead time, inverse response, and nonlinear processes are difficult to control efficiently by the PID [15]. In PID, there is no direct relationship between the three tuning parameters and setpoint tracking, disturbance rejection ability, and robustness. Therefore, achieving each performance attributes by individual tuning of PID is not straightforward. To overcome this problem, the model predictive controller (MPC) is widely used in industries which employ nonlinear process in particular. It is often implemented in supervisory mode hierarchically above the base PID controller. Therefore, its performance again primarily depends on the PID in the lower level. The PID has to implement the commands received from MPC [15,16]. MPC is also implemented as a direct control algorithm, mainly where classical PID structures cannot deliver required control performance, due to difficult dynamics, strong interactions, and active constraints [17]. The MPC utilises a distinct model of the process to predict the future response of the process. The disturbance rejection offered by MPC is much better when compared to that of PID. However, the MPC suffers from serious drawbacks in that it is complicated and its parameters are tough to calculate [18].An alternative control strategy to overcome the shortcomings of the existing controllers was proposed by Kapil Mukati which intended on combining the positive attributes of the PID controller and MPC [12]. The controller which was proposed was named as RTD-A because a good controller should possess all of the following characteristics, namely: robustness (R), set-point tracking (T), disturbance rejection (D), and overall aggressiveness (A). All the four tuning parameters are normalize...

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“…The level of liquid is maintained in tanks by managing the inflow. The manipulated variable is the inlet to the tank, while the control parameter is the level in the tank.A Gain scheduling controller [1] changes the parameter of controller based on the operating condition of the process. It is better to make up for the known nonlinearities of the process.…”

Section: Introductionmentioning

confidence: 99%

Experimental Validation of Adaptive Control Techniques for Conical Tank System

Kanthalakshmi¹

2021

Proceedings of the First International Conference on Combinatorial and Optimization, ICCAP 2021, December 7-8 2021, Chennai, In

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A nonlinear system is one whose behavior cannot be described by first-degree equations. The conical tank is an example of a nonlinear system often employed in industry, where maintaining the liquid level is critical. A level that exceeds the tank's maximum capacity may disrupt reaction equilibrium or cause valuable or hazardous substances to leak. If the level is too low, the consecutive processes may have a poor representation. As a result, liquid level control is a critical responsibility in process industries. As a result of its nonlinear behavior and continuallyvarying area of cross section, maintaining the level of liquid in a conical tank turns into a difficult operation. The main goal is to design a control mechanism for a conical tank system that will keep the level constant at the desired value.The conical tank system is first modelled for different height ranges and a conventional proportional controller is accustomed for initial controller implementation and then, two types of adaptive controller namely Gain scheduling controller and Model Reference Adaptive Controller (MRAC) are employed. The system's output is obtained by implementing the control algorithms and their performances are analyzed. It was observed that MRAC when implemented produced a better response compared to gain scheduling controller.

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Design and Implementation of Non Linear System Using Gain Scheduled PI Controller

Cited by 25 publications

References 4 publications

An Enhanced Approach of Pso Driven Fa for A Spherical Interacting System

An Enhanced Approach of Pso Driven Fa for A Spherical Interacting System

Gain Scheduling of a Robust Setpoint Tracking Disturbance Rejection and Aggressiveness Controller for a Nonlinear Process

Experimental Validation of Adaptive Control Techniques for Conical Tank System

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