Self-learning fuzzy neural networks for control of uncertain systems with time delays

Chen, S.B.; Wu, Ligang; Wang, Q.L.

doi:10.1109/3477.552196

Cited by 33 publications

(12 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another problem is that the effect of time delay in a DOM study is more serious than in other control fields because of pharmacological reasons. In delay time control studies, many control theories [13][14][15][16][17][18][19][20] using model-based methods have been proposed. But most of these methods have disadvantages such as being time-consuming, needing a precise model, or being too complicated to implement in real practice.…”

Section: Patients Undergoing Intermitted Bolus Control 15 Patients Umentioning

confidence: 99%

Muscle Relaxation Controlled by Automated Administration of Cisatracurium

Chuang

Fan

Shieh

2006

Biomed. Eng. Appl. Basis Commun.

View full text Add to dashboard Cite

In this paper, the muscle relaxant agent (i.e., cisatracurium) and three clinical control methods (i.e., 13 patients undergoing intermitted bolus control, 15 patients undergoing intensive manual control and 15 patients) undergoing automatic fuzzy logic control (FLC), were used for maintaining depth of muscle relaxation (DOM) during surgery. Cisatracurium, a muscle relaxation drug with long-term effect, low metabolic loading, but long delay time, is widely used in operating rooms and ICUs. Meanwhile, the rules for the FLC were developed from the experimental experience of intensive manual control after learning from 15 patient trials. According to experts' experimental experience, our FLC inputs were chosen from T1% error and trend of T1% which differ from other previous studies on eliminating the effect of time delay from cisatracurium. In individual clinical experimental results, the mean(SD) of the mean T1% error in 13 patients for intermitted bolus control, in 15 patients for intensive manual control, and in 15 patients for automatic control was 8.76(1.46), 1.65(1.67), and 0.48(1.43), respectively. The t test results show that automatic control is not significantly different from intensive manual control. The results show that a simple fuzzy logic controller derived from anesthetists ' clinical trials can provide good accuracy without being affected by the pharmacological time delay problem.

show abstract

Section: Patients Undergoing Intermitted Bolus Control 15 Patients Umentioning

confidence: 99%

Muscle Relaxation Controlled by Automated Administration of Cisatracurium

Chuang

Fan

Shieh

2006

Biomed. Eng. Appl. Basis Commun.

View full text Add to dashboard Cite

show abstract

“…Most of these applications have concentrated on achieving the desired system performance from the human operators' experience. However, if a fuzzy-logic-based controller is used as the main controller for nonlinear processes control, it must be difficult to determine optimally the membership functions and linguistic rules [20].…”

Section: Introductionmentioning

confidence: 99%

Neuro-Fuzzy Modified Smith predictor for IPDT and FOPDT Processes Control

Chen

Zouaoui

Chen

2013

IFAC Proceedings Volumes

View full text Add to dashboard Cite

“…A modified Smith-predictor is provided to predict and maintain the desired tracking performance. A self-learning fuzzy neural network (FNN) control scheme is presented in [15] for welding pool dynamics of the arc welding process where a neural network compensator is designed instead of the classical Smith-predictor for attenuating the adverse effects of time delay. In [16], analysis and synthesis of nonlinear time-delay system via T-S model fuzzy method are given.…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand some researchers use intelligent control methods include neural networks and fuzzy logic, etc., to control such kind of delay systems because these intelligent methods have the abilities of learning and reasoning [12][13][14][15][16][17][18][19]. For the glass manufacturing process which is hard to get the model of the controlled plant, a fuzzy prediction control method is presented [12].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy neural network sliding mode control for long delay time systems based on fuzzy prediction

2007

Neural Comput Applic

View full text Add to dashboard Cite

Delay time, which may degrade the control performance, is frequently encountered in various control processes. The fuzzy neural network sliding mode controller (FNNSMC), which incorporates the fuzzy neural network (FNN) with the sliding mode controller (SMC), is developed to control the long delay system with unknown model based on fuzzy prediction algorithm in the paper. According to the characteristics of the long delay systems, we simulate the manual operating process and predict the delayed error and its derivative based on the information of the input and output variables of the process, and then feedback these prediction values to the FNN and train the FNN with the regulation function by the idea of sliding mode control until the better control results are obtained. The FNNSMC has more robustness due to the abilities of the learning and reasoning and can eliminate the drawbacks of the general SMC, namely the chattering in the control signal and the needing knowledge of the bounds of the disturbances and uncertainties. Simulation examples demonstrate the advantages of the proposed control scheme.

show abstract

Self-learning fuzzy neural networks for control of uncertain systems with time delays

Cited by 33 publications

References 18 publications

Muscle Relaxation Controlled by Automated Administration of Cisatracurium

Muscle Relaxation Controlled by Automated Administration of Cisatracurium

Neuro-Fuzzy Modified Smith predictor for IPDT and FOPDT Processes Control

Fuzzy neural network sliding mode control for long delay time systems based on fuzzy prediction

Contact Info

Product

Resources

About