Analysis and Tuning of RTD-A Controllers

Sendjaja, Antonius Yudi; Ng, Zhen Fu; How, Si Si; Kariwala, Vinay

doi:10.1021/ie102154y

Cited by 14 publications

(10 citation statements)

References 31 publications

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“…Initially, Kapil Mukati and Ogunnaike have developed a tuning rule that deals with the parametric uncertainty of the controller [13]. In 2011, Vinay Kariwala designed the second tuning method that overcomes the problem of more aggressiveness [14]. The proposed idea concentrates on designing the simulation tool for a RTD-A controller in MATLAB.…”

Section: Rtd-a Control Schemementioning

confidence: 99%

“…When the RTD-A controller based on the nominal model G(s) is implemented on the plant with model uncertainty (λ), the resulting closed-loop behavior may be represented in the discrete state-space form in (14) and (15) [13].…”

Section: State Variable Formmentioning

confidence: 99%

“…After calculating the FOPDT model, to calculate RTD-A tuning parameter the Tool-II was designed. This tool has a drop-down menu containing two methods for tuning [14].…”

Section: Tool For Tuning Rtd-a Parameter (Tool-ii)mentioning

confidence: 99%

“…In 2011 Vinay Kariwala introduced the block diagram representation of RTD-A controller implementation and an alternate tuning rule. The rules are focused on optimizing the results, but the Kariwala algorithm was working better than the Ogunnaike rules [14]. In the past, there are tools such as the fractional order modeling and control (FOMCON), Jonathan model predictive controller (jMPC) and N-integer.…”

Section: Introductionmentioning

confidence: 99%

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

2019

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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.

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Section: Rtd-a Control Schemementioning

confidence: 99%

Section: State Variable Formmentioning

confidence: 99%

“…After calculating the FOPDT model, to calculate RTD-A tuning parameter the Tool-II was designed. This tool has a drop-down menu containing two methods for tuning [14].…”

Section: Tool For Tuning Rtd-a Parameter (Tool-ii)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

2019

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“…The reference trajectory and the control action calculator block are in the forward loop. The current disturbance and future disturbance effect predictor form the feedback loop [29].…”

mentioning

confidence: 99%

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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Scalable multiple linear model RTDA controller formulation for constrained nonlinear processes

Haseena

Srinivasan

2021

Optim Control Appl Methods

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Robustness, tracking, disturbance rejection and overall aggressiveness (RTDA) controller is a next generation control strategy with good robustness, tracking, and regulatory performance. This article proposes a scalable multiple linear model based RTDA (M‐RTDA) control scheme to handle nonlinear processes with or without constraints effectively. Multiple linear models at different operating regions are used to represent the nonlinear dynamics of the system. The optimal local linear models are identified using the gap metric based technique. The linear model and the corresponding RTDA controller at each operating region are combined to get a model‐controller unit. The output of each model controller unit is integrated using a fuzzy scheduling technique to ensure bumpless transfer. The effectiveness of the proposed technique is demonstrated through simulation using nonlinear benchmark processes like pH neutralization and continuous stirred tank reactor (CSTR) process with constraints. The block diagram representation of the scalable M‐RTDA controller is developed, and the step‐by‐step procedure (algorithm) to implement the proposed concept is discussed. The proposed scalable multiple linear model scheme is easily reconfigurable as per the performance/operator requirement without affecting the other local RTDA controllers. The qualitative and quantitative performances are compared with the results of the direct extended multiple model based RTDA (DE‐RTDA) controller and dynamic matrix controller(DMC).

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Analysis and Tuning of RTD-A Controllers

Cited by 14 publications

References 31 publications

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

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

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

Scalable multiple linear model RTDA controller formulation for constrained nonlinear processes

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