Improving PID Control Based on Neural Network

Li, Jun; Gómez-Espinosa, Alfonso

doi:10.1109/icmeae.2018.00042

Cited by 9 publications

(4 citation statements)

References 17 publications

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“…The PID controller cannot achieve real-time adjustment of the controller parameters and lacks control flexibility. The control effect is hardly satisfactory for control systems with complex non-linearity [ 15 ]. In this case, the intelligent PID algorithm, which can adjust the parameters of the PID controller in real time, has emerged.…”

Section: Introductionmentioning

confidence: 99%

Drying Temperature Precision Control System Based on Improved Neural Network PID Controller and Variable-Temperature Drying Experiment of Cantaloupe Slices

Yang

Zheng

Xiao

et al. 2023

Plants

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A drying temperature precision control system was studied to provide technical support for developing and further proving the superiority of the variable-temperature drying process. In this study, an improved neural network (INN) proportional–integral–derivative (PID) controller (INN-PID) was designed. The dynamic performance of the PID, neural network PID (NN-PID) and INN-PID controllers was simulated with unit step signals as an input in MATLAB software. A drying temperature precision control system was set up in an air impingement dryer, and the drying temperature control experiment was carried out to verify the performance of the three controllers. Linear variable-temperature (LVT) and constant-temperature drying experiments of cantaloupe slices were carried out based on the system. Moreover, the experimental results were evaluated comprehensively with the brightness (L value), colour difference (ΔE), vitamin C content, chewiness, drying time and energy consumption (EC) as evaluation indexes. The simulation results show that the INN-PID controller outperforms the other two controllers in terms of control accuracy and regulation time. In the drying temperature control experiment at 50 °C–55 °C, the peak time of the INN-PID controller is 237.37 s, the regulation time is 134.91 s and the maximum overshoot is 4.74%. The INN-PID controller can quickly and effectively regulate the temperature of the inner chamber of the air impingement dryer. Compared with constant-temperature drying, LVT is a more effective drying mode as it ensures the quality of the material and reduces the drying time and EC. The drying temperature precision control system based on the INN-PID controller meets the temperature control requirements of the variable-temperature drying process. This system provides practical and effective technical support for the variable-temperature drying process and lays the foundation for further research. The LVT drying experiments of cantaloupe slices also show that variable-temperature drying is a better process than constant-temperature drying and is worthy of further study to be applied in production.

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Section: Introductionmentioning

confidence: 99%

Drying Temperature Precision Control System Based on Improved Neural Network PID Controller and Variable-Temperature Drying Experiment of Cantaloupe Slices

Yang

Zheng

Xiao

et al. 2023

Plants

View full text Add to dashboard Cite

show abstract

“…Unfortunately, most of the tuning methods of conventional PID parameters require known model parameters and fixed operating points [1,3,6]. Therefore, the conventional PID controller fails when it faces a variation in the system parameters, a sudden load change, an external disturbance, a setpoint change [2,6,7]. For these drawbacks of the conventional PID controller, the researchers worked seriously to find suitable controllers to control such complex nonlinear dynamical systems [8,9].…”

Section: Introductionmentioning

confidence: 99%

“…The time delay temperature system was controlled using adaptive PID with Lyapunov function in [11]. The level in a tank was governed using (8-4-3) structure PID based on neural network as mentioned in [7]. A radial basis function (RBF) neural network was used to tune the PID controller parameters for DC motor position control in [12].…”

Section: Introductionmentioning

confidence: 99%

Polynomial Recurrent Neural Network-Based Adaptive PID Controller With Stable Learning Algorithm

Hanna

Khater

El-Nagar

et al. 2022

Neural Process Lett

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This paper introduces a novel structure of a polynomial weighted output recurrent neural network (PWORNN) for designing an adaptive proportional—integral—derivative (PID) controller. The proposed adaptive PID controller structure based on a polynomial weighted output recurrent neural network (APID-PWORNN) is introduced. In this structure, the number of tunable parameters for the PWORNN only depends on the number of hidden neurons and it is independent of the number of external inputs. The proposed structure of the PWORNN aims to reduce the number of tunable parameters, which reflects on the reduction of the computation time of the proposed algorithm. To guarantee the stability, the optimization, and speed up the convergence of the tunable parameters, i.e., output weights, the proposed network is trained using Lyapunov stability criterion based on an adaptive learning rate. Moreover, by applying the proposed scheme to a nonlinear mathematical system and the heat exchanger system, the robustness of the proposed APID-PWORNN controller has been investigated in this paper and proven its superiority to deal with the nonlinear dynamical systems considering the system parameters uncertainties, disturbances, set-point change, and sensor measurement uncertainty.

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“…etc.) [3,4]. The neuro-fuzzy and adaptive neuro-fuzzy PID combining the artificial neural networks and the fuzzy logic [5], and the genetic evolutionary optimization algorithm based PID which deals with an iteratively evolving group of potential solutions, called individuals or chromosomes [6].…”

Section: Introductionmentioning

confidence: 99%

A Robust LQR-Based Fuzzy-Immune PID Applied for a Greenhouse Temperature Networked Control

Banharkou¹,

Gherbi²,

Mehennaoui³

2022

EJEE

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Bioinspired control system strategies have received considerable attention in recent years, demonstrating their effectiveness to control various classes of systems. This paper presents a new robust LQR-based fuzzy-immune PID controller synthesis method, the particularity of this structure is the combination of the features granted by the linear quadratic regulator LQR and the fuzzy-immune PID controller as: robustness, optimization and self-adapting. The proposed approach is applied to the temperature control of a greenhouse system over Ethernet network. Such control loop is bound to be affected by several uncertainties such as communication delays, packet dropout... etc., it is therefore necessary to adopt a robust control strategy that can handle these problems. MATLAB/Simulink is used to simulate the proposed controller structure under several perturbations/uncertainties scenarios. The results show better time domain performance and greater robustness against uncertainties, in comparison with other PID structures (classic and fuzzy immune PID).

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Improving PID Control Based on Neural Network

Cited by 9 publications

References 17 publications

Drying Temperature Precision Control System Based on Improved Neural Network PID Controller and Variable-Temperature Drying Experiment of Cantaloupe Slices

Drying Temperature Precision Control System Based on Improved Neural Network PID Controller and Variable-Temperature Drying Experiment of Cantaloupe Slices

Polynomial Recurrent Neural Network-Based Adaptive PID Controller With Stable Learning Algorithm

A Robust LQR-Based Fuzzy-Immune PID Applied for a Greenhouse Temperature Networked Control

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