A novel auto-tuning PID control mechanism for nonlinear systems

Çetin, Meriç; İplikçi, Serdar

doi:10.1016/j.isatra.2015.05.017

Cited by 65 publications

(46 citation statements)

References 31 publications

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“…From (17), V m (k) > 0. us, from (20) and (21), the convergence of the identification error is guaranteed if lim k⟶∞ e m i (k) � 0, and it is necessary that the condition…”

Section: Neural Network Indirect Adaptive Controlmentioning

confidence: 98%

“…Jacobian identification information where the incremental change of the output weights Δw 1 (k) and the hidden layer weights Δw 0 (k) of the neural network model are given as follows [13,[16][17][18][19][20][21][22][23][24]:…”

Section: Neural Network Indirect Adaptive Controlmentioning

confidence: 99%

“…However, hybrid control has received considerable attention during the past two decades, and the class of switching systems is specifically employed in many industrial applications such as in [17][18][19][20][21][22][23][24] and [25]. In fact, in [17] and [21], respectively, the PID is mixed with the model reference adaptive control and the model predictive controller. In [19,20], a mixed method between the PID controller and model reference control is proposed for the nonlinear hydraulic actuator and for an aerial inertially stabilized platform.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Neural Adaptive PID and Neural Indirect Adaptive Control Switch Controller for Nonlinear MIMO Systems

Slama

Errachdi

Benrejeb

2019

Mathematical Problems in Engineering

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This paper proposes an adaptive switch controller (ASC) design for the nonlinear multi-input multi-output system (MIMO). In fact, the proposed method is an online switch between the neural network adaptive PID (APID) controller and the neural network indirect adaptive controller (IAC). According to the design of the neural network IAC scheme, the adaptation law has been developed by the gradient descent (GD) method. However, the adaptive PID controller is built based on the neural network combining the PID control and explicit neural structure. The strategy of training consists of online tuning of the neural controller weights using the backpropagation algorithm to select the suitable combination of PID gains such that the error between the reference signal and the actual system output converges to zero. The stability and tracking performance of the neural network ASC, the neural network APID, and the neural network IAC are analyzed and evaluated by the Lyapunov function. Then, the controller results are compared between APID, IAC, and ASC, in this paper, applying to a nonlinear system. From simulations, the proposed adaptive switch controller has better effects both on response time and on tracking performance with smallest MSE.

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“…From (17), V m (k) > 0. us, from (20) and (21), the convergence of the identification error is guaranteed if lim k⟶∞ e m i (k) � 0, and it is necessary that the condition…”

Section: Neural Network Indirect Adaptive Controlmentioning

confidence: 98%

Section: Neural Network Indirect Adaptive Controlmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Neural Adaptive PID and Neural Indirect Adaptive Control Switch Controller for Nonlinear MIMO Systems

Slama

Errachdi

Benrejeb

2019

Mathematical Problems in Engineering

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“…The use of PID conventional controllers is justified due to their simplicity of design and efficiency in general industrial applications (Astrom 1985). The main problem about a PID controller is the fact that the parameters of the controller must be adjusted properly to satisfy a desired performance (Cetin and Iplikci 2015;Panda et al 2004). In order to determine such parameters, the specialist may proceed by a phenomenological modeling of the system or follow some empirical approach.…”

Section: Foundations For Api Creationmentioning

confidence: 99%

Construction of software using gain-schedule/auto-adaptive control strategy for automation in drilling fluid production plants

Magalhães

Fontella

Borges

et al. 2019

J Petrol Explor Prod Technol

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Although many advanced nonlinear process control techniques have been developed over the past decade, classic control based on feedback response still has its place. This is mostly so because feedback empirical control is robust and simple to implement and does not require fancy calculations or high-qualified operational manpower to operate it. This work has developed an application written in LabVIEW ® environment capable of doing a fully automated single-input single-output control. Preliminary tests were performed in a drilling fluid production unit, controlling flow rate through manipulation of the pump power engine. In the future, tests in the same plant of pressure control by choke valves manipulation will be performed (as found in rig sites, where wellbore pressure is controlled by manipulation of such valves). The final goal is to implement such software in a real rig site, to help operators in drilling control areas such as flow rate and wellbore pressure. The produced software has embedded three self-developed features: automatic plant identification (API), auto-tuning (ABAP) and controllers auto-switch (CAS). The API determines automatically the linearity of the process determining the empirical parameters according to Sundaresan and Krishnaswamy technique. In sequence, it calculates the parameters for P, PI and PID controllers using Cohen-Coon and Ziegler-Nichols methods. The API method automates the sequence of tests necessary to implement the Sundaresan and Krishnaswamy empirical approach. The ABAP feature based on heuristic rules tunes in real time the controllers' parameters to optimize its response. The CAS allows automatic switch between controllers and parameters to avoid instability, overshoots and creates a synergy with ABAP feature. The results have shown that the API feature is a good optimizer reducing the invested time to calculate all the parameters, from hours to a few minutes. The CAS results demonstrated an associative property with the ABAP feature to mitigate instabilities and overshoots. Therefore, the preliminary results suggested this software is a unique and important tool to improve performance, profitability and reliability during offshore and onshore drilling operations. Moreover, this application could be used in any industry with an approximate first-order dynamic system due to its robustness and a low human interaction need.

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“…Approximately 90% of industrial applications use a PID controller for the actual control (Saridhar, Ramrao, & Singh, ) because its characteristics include good performance, low cost, high effectiveness, and a simple structure. Its effect is easy to maintain, implement, and understand (Cetin & Iplikci, ). Achieving the performance of the PID controller depends on three parameters, namely, the proportional gain ( K P ), integral gain ( K I ), and derivative gain ( K D ).…”

Section: Introductionmentioning

confidence: 99%

A novel optimal PID controller autotuning design based on the SLP algorithm

Pongfai

Zhang

et al. 2019

Expert Systems

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A novel optimal proportional integral derivative (PID) autotuning controller design based on a new algorithm approach, the “swarm learning process” (SLP) algorithm, is proposed. It improves the convergence and performance of the autotuning PID parameter by applying the swarm and learning algorithm concepts. Its convergence is verified by two methods, global convergence and characteristic convergence. In the case of global convergence, the convergence rule of a random search algorithm is employed to judge, and Markov chain modelling is used to analyse. The superiority of the proposed method, in terms of characteristic convergence and performance, is verified through the simulation based on the automatic voltage regulator and direct current motor control system. Verification is performed by comparing the results of the proposed model with those of other algorithms, that is, the ant colony optimization with a new constrained Nelder–Mead algorithm, the genetic algorithm (GA), the particle swarm optimization (PSO) algorithm, and a neural network (NN). According to the global convergence analysis, the proposed method satisfies the convergence rule of the random search algorithm. With respect to the characteristic convergence and performance, the proposed method provides a better response than the GA, the PSO, and the NN for both control systems.

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A novel auto-tuning PID control mechanism for nonlinear systems

Cited by 65 publications

References 31 publications

Neural Adaptive PID and Neural Indirect Adaptive Control Switch Controller for Nonlinear MIMO Systems

Neural Adaptive PID and Neural Indirect Adaptive Control Switch Controller for Nonlinear MIMO Systems

Construction of software using gain-schedule/auto-adaptive control strategy for automation in drilling fluid production plants

A novel optimal PID controller autotuning design based on the SLP algorithm

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