Tuning guidelines for fractional order PID controllers: Rules of thumb

Dastjerdi, Ali Ahmadi; Saikumar, Niranjan; HosseinNia, S. Hassan

doi:10.1016/j.mechatronics.2018.10.004

Cited by 76 publications

(47 citation statements)

References 39 publications

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“…It is underscored in [5,[18][19][20][21] that successful industry applications always require deep domain knowledge and understanding of process identification (plant or system modeling) [4,11,13,14,[22][23][24][25][26][27][28]. Unfortunately, situations arise when these established control design methods become unreliable because of their basis on imperfect representations of the actual process dynamics.…”

Section: Introductionmentioning

confidence: 99%

“…Unfortunately, situations arise when these established control design methods become unreliable because of their basis on imperfect representations of the actual process dynamics. To remedy this problem, some researchers advocate for computational intensive online parameter estimations and optimizations [13,27,29].…”

Section: Introductionmentioning

confidence: 99%

“…However, these methods are often carried out offline using the nominal process model, and so any serious uncertainty, as can be found in practice, in the parameters of the system can lead to instability. Again, in some cases, the optimal solution may Speed Control of DC Motors: Optimal Closed PID-Loop Model Predictive Control never be found [27]. Therefore these methods have not found scalable use in the industry [36].…”

Section: Introductionmentioning

confidence: 99%

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Speed Control of DC Motors: Optimal Closed PID-Loop Model Predictive Control

Somefun¹,

Akingbade²,

Dahunsi³

2020

ujca

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One of the lowest level control tasks, especially in robotics and other manufacturing industries, upon which other high-level controls are dependent, is the speed control of a dc motor. Usually, tuning the parameters of the proportional integral derivative (PID) controller for this task employs established conventional methods that expose the knowledge of nominal process model parameters to the control algorithm. These methods have found widespread use. Notwithstanding, a promising line of inquiry is: to search alternate possibilities of a PID being designed to automatically achieve comparable good control performance without using a formal mathematical process model approximation of the actual physical system, such as dc motor plant, in the frequency or time domain. In this paper, we propose an intelligent PID design method, "optimal closed PID-loop model predictive control", that answers this question using the characteristic settling-time (including delay-time) property of dynamic processes. The performance of this proposed method is benchmarked with popular process-model based methods. Simulation results illustrate the promise and effectiveness of the proposed tuning method, in ensuring good closed-loop performance quality for the dc motor, without using formal process models.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Speed Control of DC Motors: Optimal Closed PID-Loop Model Predictive Control

Somefun¹,

Akingbade²,

Dahunsi³

2020

ujca

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“…Likewise, numerous advanced control schemes based on FOPID controllers were proposed, such as, Smith predictors structures [25][26][27], internal mode control [28][29][30], hybrid control [31], gain scheduling [32][33][34], gain and order scheduling [35,36], among others. Current reviews in the development of FOPIDs can be found in [37][38][39][40][41][42]; likewise, few current examples of application in industry are described in [43][44][45][46][47][48][49][50].…”

Section: Introductionmentioning

confidence: 99%

Back to Basics: Meaning of the Parameters of Fractional Order PID Controllers

et al. 2019

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The beauty of the proportional-integral-derivative (PID) algorithm for feedback control is its simplicity and efficiency. Those are the main reasons why PID controller is the most common form of feedback. PID combines the three natural ways of taking into account the error: the actual (proportional), the accumulated (integral), and the predicted (derivative) values; the three gains depend on the magnitude of the error, the time required to eliminate the accumulated error, and the prediction horizon of the error. This paper explores the new meaning of integral and derivative actions, and gains, derived by the consideration of non-integer integration and differentiation orders, i.e., for fractional order PID controllers. The integral term responds with selective memory to the error because of its non-integer order λ , and corresponds to the area of the projection of the error curve onto a plane (it is not the classical area under the error curve). Moreover, for a fractional proportional-integral (PI) controller scheme with automatic reset, both the velocity and the shape of reset can be modified with λ . For its part, the derivative action refers to the predicted future values of the error, but based on different prediction horizons (actually, linear and non-linear extrapolations) depending on the value of the differentiation order, μ . Likewise, in case of a proportional-derivative (PD) structure with a noise filter, the value of μ allows different filtering effects on the error signal to be attained. Similarities and differences between classical and fractional PIDs, as well as illustrative control examples, are given for a best understanding of new possibilities of control with the latter. Examples are given for illustration purposes.

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“…For example, Smith indicators structures [23], internal mode controller [24,25], hybrid control [26], gain scheduling [27] and many others. Latest surveys in the evolution of FOPIDs can be viewed in [28][29][30][31]. Furthermore, some usages of fractional calculus are also reported in field of Biomedical [32], electro-hydraulic system [33], robotic manipulators [34,35], Pneumatic position servo system [36], Industrial process [37] and water level control [38].…”

Section: Introductionmentioning

confidence: 99%

Design a novel fractional order controller for smart microgrid using multi-agent concept

Din

Singh

2019

SN Appl. Sci.

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A microgrid system deploys various components such as solar, wind, diesel generator, fuel cell, flywheel, aqua electrolyser, ultra-capacitor, and storage batteries etc. A microgrid system operates through a centralized control system that works on the current condition of the sources and loads. The status of renewable and non renewable sources as well as load is obtained through the multi-agent system (MAS) to regulate a controllable source as per the deficit between demand and supply. The message interchange in the MAS is considered to be compatible with a user data gram protocol/internet protocol-based network. The present study implements the application of MAS to control a smart microgrid in a Matlab/ simulation environment. The simulation results reveal that the present MAS can provide the persistent transition from microgrid when disturbances occur. This indicates the effectiveness of MAS as a technology for controlling the microgrid process. A fractional order (FO) based controller is employed and its parameters are optimized through gravitational search algorithm. The FO controller demonstrates enhanced performance in contrast to the integer order controller under linear as well as nonlinear operating conditions. Furthermore, the proposed controller additionally exhibits its superiority in terms of robustness against parameter changes and disconnection of various components.

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Tuning guidelines for fractional order PID controllers: Rules of thumb

Cited by 76 publications

References 39 publications

Speed Control of DC Motors: Optimal Closed PID-Loop Model Predictive Control

Speed Control of DC Motors: Optimal Closed PID-Loop Model Predictive Control

Back to Basics: Meaning of the Parameters of Fractional Order PID Controllers

Design a novel fractional order controller for smart microgrid using multi-agent concept

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