Application of fuzzy logic to reduce modelling errors in PIDSP for FOPDT process control

Chen, Hao; Zouaoui, Zoubir; Chen, Zheng

doi:10.1109/med.2011.5983205

Cited by 5 publications

(2 citation statements)

References 15 publications

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“…Nevertheless, fuzzy control has also been used from more formal points of view which consider model-dependency, self-tuning, stability analysis, hybrid methodologies, etc. Chen et al [20] present some major ways to incorporate FLC on robust control study area; Li and Ruan [4] show a very simple way to provide adaptability to a FLC, while Li et al [2] provide adaptability to a PID controller through fuzzy logic and Chen et al [21] to a smith predictor. Complex systems can also be modeled through Takagi-Sugeno (TS) fuzzy models [10] and some plants have been controlled by TS controllers and TS observers as shown in [3].…”

Section: Introductionmentioning

confidence: 99%

Advantages of Fuzzy Control While Dealing with Complex/ Unknown Model Dynamics: A Quadcopter Example

Ibarra¹,

Webb²

2016

New Applications of Artificial Intelligence

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Commonly, complex and uncertain plants cannot be faced through well-known linear approaches. Most of the time, complex controllers are needed to attain expected stability and robustness; however, they usually lack a simple design methodology and their actual implementation is difficult (if not impossible). Fuzzy logic control is an intelligent technique which, on its basis, allows the translation from logic statements to a nonlinear mapping. Although it has been proven to effectively deal with complex plants, many recent studies have moved away from the basic premise of linguistic interpretability. In this work, a simple fuzzy controller is designed in a clear way, privileging design easiness and logical consistency of linguistic operators. It is simulated together to a nonlinear model of a quadcopter with added actuators variability, so the robust operation of the controller is also proven. Uneven gain, bandwidth, and time-delay variations are applied among quadcopter's motors, so the simulations results enclose those characteristics which could be found in reality. As those variations can be related to actuators' performance, an analysis can be driven in terms of the features which are not commonly included in mathematical models like power electronics drives or electric machinery. These considerations may shorten the gap between simulation and actual implementation of the fuzzy controller. Briefly, this chapter presents a simple fuzzy controller which deals with a quadcopter plant as a first approach to intelligent control.

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Section: Introductionmentioning

confidence: 99%

Advantages of Fuzzy Control While Dealing with Complex/ Unknown Model Dynamics: A Quadcopter Example

Ibarra¹,

Webb²

2016

New Applications of Artificial Intelligence

View full text Add to dashboard Cite

show abstract

“…However, the disadvantage of this proposed controller is that it is very complex and difficult to tune. Fuzzy logic attracted also special attention in recent research works [14][15][16][17][18][19] . Most of these applications have concentrated on achieving the desired system performance from the human operators' experience.…”

Section: Introductionmentioning

confidence: 99%