Stability Analysis of a Nonlinear PID Controller

Son, Yung-Deug; Bin, Sang-Do; Jin, Gang-Gyoo

doi:10.1007/s12555-020-0599-y

Cited by 7 publications

(4 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…the weight v j , node center m j and base width parameter s j from hidden layer nodes to the output layer of the neural network are updated by gradient descent during algorithm iteration. Update the output weight v j is shown in (7).…”

Section: Rbf Neural Networkmentioning

confidence: 99%

“…5,6 NPID combines PID control with nonlinear control strategy, which not only inherits the simple structure of linear PID algorithm, but also shows good control performance in nonlinear system control, making it one of the simplest and most effective control methods in many control algorithms. 7 However, one of the main problems of the NPID controller is that control performance is affected by factors such as nonlinear function selection and manual parameter tuning. When the system is uncertain, it is difficult to ensure the optimal effect.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Parameter optimization of nonlinear PID controller using RBF neural network for continuous stirred tank reactor

Shi

Zhao

Zheng

2023

Measurement and Control

View full text Add to dashboard Cite

The temperature system of the Continuous Stirred Tank Reactor (CSTR) has the characteristics of strong nonlinearity and uncertain parameters. The linear PID controller makes it difficult to meet CSTR’s control requirements. Nonlinear PID (NPID) can improve the control effect of nonlinear controlled objects, but due to the influence of nonlinear function selection and manual parameter setting, when parameters are uncertain or subject to external interference, the control performance of the system will decrease. To improve the adaptive capability of the NPID controller, the RBF-NPID control algorithm is proposed. The learning ability of RBF neural network is used to adjust NPID parameters online to improve the control performance of the system. In order to verify the effectiveness of the proposed algorithm, a CSTR model was established in MATLAB and algorithm comparison research was carried out. Simulation results show the effectiveness and superiority of the proposed algorithm.

show abstract

Section: Rbf Neural Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Parameter optimization of nonlinear PID controller using RBF neural network for continuous stirred tank reactor

Shi

Zhao

Zheng

2023

Measurement and Control

View full text Add to dashboard Cite

show abstract

“…12788 127883O-4 The improved ADRC control system equivalent model shown in Figure 5 can be obtained from equations (15)~( 17). If the control system is expected to be stable, G a 0 ~G a 6 must be guaranteed to be positive [11] , that is, the inequality set is…”

Section: Stabilitymentioning

confidence: 99%

“…As can be seen from equations ( 5) and (11), the transfer functions between the actual disturbance of the plant in TLADRC and the higher order component of the disturbance estimated by improved ESO are respectively:…”

Section: Immunitymentioning

confidence: 99%

Voltage fluctuation suppression strategy of DC bus based on improved linear active disturbance rejection control

Cao,

Ma,

Zhou

et al. 2023

Second International Conference on Energy, Power, and Electrical Technology (ICEPET 2023)

View full text Add to dashboard Cite

The photovoltaic microgrid has complex characteristics such as multivariable and strong coupling. To solve the problem that traditional controllers are difficult to achieve ideal control effect, this paper introduces the improved second-order linear active disturbance rejection control technology to the control of the interface converter at the load end of the microgrid. Firstly, the plant is analyzed and the model information is extracted according to the state-space average method. Based on the traditional linear active disturbance rejection controller, the correction function and filter function are introduced to solve the problem of noise sensitivity measurement while the traditional linear active disturbance rejection control(TLADRC) is enhanced with disturbance rejection. On the premise of ensuring the stability of the system, the improved structure is used to improve the anti-disturbance and anti-noise of the system. Finally, the control strategy proposed in this paper and the voltage waveform of DC bus side under linear active disturbance rejection control are compared by simulation under different working conditions, which shows that the active disturbance rejection control strategy proposed in this paper has better control performance.

show abstract

A Particle Swarm Optimization tuned nonlinear PID controller with improved performance and robustness for First Order Plus Time Delay systems

Charkoutsis,

Kara-Mohamed

2023

Results in Control and Optimization

View full text Add to dashboard Cite

Stability Analysis of a Nonlinear PID Controller

Cited by 7 publications

References 17 publications

Parameter optimization of nonlinear PID controller using RBF neural network for continuous stirred tank reactor

Parameter optimization of nonlinear PID controller using RBF neural network for continuous stirred tank reactor

Voltage fluctuation suppression strategy of DC bus based on improved linear active disturbance rejection control

A Particle Swarm Optimization tuned nonlinear PID controller with improved performance and robustness for First Order Plus Time Delay systems

Contact Info

Product

Resources

About