Genetic Algorithm Based PID Controller Tuning Approach for Continuous Stirred Tank Reactor

Jayachitra, A.; Vinodha, R.

doi:10.1155/2014/791230

Cited by 139 publications

(70 citation statements)

References 21 publications

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“…The optimal solution H * in (8) was shown in [4] to be non-unique and for any non-singular matrix D, H = DH * (9) results in the same loss as the solution given by (8). Therefore, both the Null-space and the Exact local method have an infinite number of solutions for H that satisfies (6) or (9) and thus gives the same steady-state operation.…”

Section: A Optimal Measurement Combinationmentioning

confidence: 99%

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An iterative LMI approach to controller design and measurement selection in self-optimizing control

Klemets

Hovd

2017

2017 11th Asian Control Conference (ASCC)

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Abstract-Self-optimizing control focuses on minimizing loss for processes in the presence of disturbances by holding selected controlled variables at constant set-points. The loss can further be reduced by controlling measurement combinations to constant values. Two methods for finding appropriate measurement combinations are the Null-space and the Exact local method. Both approaches offer sets with an infinite number of solutions that give the same loss. Since self-optimizing control is mainly concerned with minimizing the steady-state loss, little attention has been put on the dynamic performance when selecting measurement combinations.In this work, an iterative LMI approach is used to find a measurement combination and PI controllers for the Null-space or the Exact local method. The measurement combination and the controllers are designed such that, the dynamic response is improved when the process is facing disturbances.

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Section: A Optimal Measurement Combinationmentioning

confidence: 99%

“…However, for restricted-order controllers (e.g., PI/PID controller) the optimization problems tend to become non-convex in the controller parameter space. They are usually solved by employing heuristics or intelligent methods [9], [10]. A loop shaping method was proposed in [11], by specifying bounds on the phase and gain margins.…”

Section: Introductionmentioning

confidence: 99%

An iterative LMI approach to controller design and measurement selection in self-optimizing control

Klemets

Hovd

2017

2017 11th Asian Control Conference (ASCC)

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“…In [16] authors proposed a method for tuning of fuzzy based PID controller. In [17] authors have presented an approach for genetically tuning of PID controller. In last few decades researchers have focused on optimal control theory and formulated the well-known optimal state feedback controller known as linear quadratic regulator, this approach reduces the deviation in state trajectories of a system maintaining minimum control effort.…”

Section: *Author For Correspondencementioning

confidence: 99%

Tracking control of parallel manipulator with 3-DOF

Chaudhary¹,

Ohri²

2016

IJATEE

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A manipulator is fundamentally a reprogrammable, multifunctional mechanical arm intended to move parts, tools, and specialized device through preprogrammed motions for the performance of various tasks. With the advances in technology over last decades Parallel manipulator became more popular for industrial purpose. Parallel manipulators are used for variety of tasks such as flight simulator [1], medical tools [2-4], pick and place operations. The kinematics of parallel manipulator differs from serial manipulator.Parallel manipulator consists of two platforms one of which is fixed and another is moving platform. These two platforms are connected to each other with the help of limbs. These limbs may consist of revolute or translational joints. Each limb is driven individually. Such type of structure results in closed kinematic mechanism. The kinematic analysis of parallel manipulator is difficult and it has been focus of many researchers [5][6][7][8]. The trajectory control of parallel mechanism like this present unique challenges because the dynamics of parallel manipulator are highly coupled and nonlinear. The precision depends on the accuracy of each actuator as well as on the synchronization of the actuators. In last few decades researchers have focused on optimal control theory and formulated the well-known optimal state feedback controller known as linear quadratic regulator, this approach reduces the deviation in state trajectories of a system maintaining minimum control effort. A very detailed study of optimal control is presented in [18]. To get the optimal control signal algebraic Riccati equation Research Article AbstractA parallel three degrees of freedom (3-DOF) manipulator known as Maryland manipulator is considered in this paper. The manipulator model considered in this paper is more practical because the offset lengths are not taken zero. The trajectory tracking control of the Maryland manipulator is done using linear-quadratic regulator (LQR) based proportional-integral-derivative (PID) controller. Three sequential trajectories are used to provide complete dynamic analysis of the manipulator. The manipulator considered here is more practical because the offset lengths are not taken zero unlike previous research articles. The motivation behind using PID controller is its simplicity and effectiveness. The manipulator is a highly coupled and nonlinear system so the correct determination of control signal is very important to achieve good tracking performance. The process for selecting the PID parameters is discussed in detail and the simulations results are used to show the efficacy of the controller.

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“…The best result with the lowest cost function have been reported in the present study. Parameter used in genetic algorithm is shown in Table I and range of FOPID parameter for search space is presented in Table II [11], [13].…”

Section: E Mutationmentioning

confidence: 99%

“…In recent year lots of work have been done on optimized tuning of FOPID [4]- [7] in which objective function is minimized based on particle swarm optimization (PSO) [5] and genetic algorithm based FOPID design in [4]. Maiti et al [7] used ITAE minimization for tuning of PID and FOPID controller and more in [11], [13]. Frequency domain graphical tuning of PI λ D µ controllers based on phase and gain margin for fractional-order time-delay systems is introduced by hammaci [6].…”

Section: Introductionmentioning

confidence: 99%

Tuning of fractional order PID controller for higher order process based on ITAE minimization

Maurya¹,

Bongulwar²,

Patre³

2015

2015 Annual IEEE India Conference (INDICON)

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This paper presents the time domain optimal tuning of fractional order Proportional Integral Derivative (FOPID) controller designed for higher order fractional model by using optimization with integral performance indices. The Integral Time Absolute Error (ITAE) is chosen as performance measure because it is one of the good tuning criterion to obtain controller parameters and widely used in PID controller design. The minimization of the ITAE is carried out by using Genetic Algorithm (GA). GA minimizes the ITAE and gives the optimum values of FOPID controller parameters. The purpose of this paper is to apply GA based ITAE minimization on higher order fractional delayed system to track the set point. Simulation results show the effectiveness of the proposed method.

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Genetic Algorithm Based PID Controller Tuning Approach for Continuous Stirred Tank Reactor

Cited by 139 publications

References 21 publications

An iterative LMI approach to controller design and measurement selection in self-optimizing control

An iterative LMI approach to controller design and measurement selection in self-optimizing control

Tracking control of parallel manipulator with 3-DOF

Tuning of fractional order PID controller for higher order process based on ITAE minimization

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