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The Composition and Validation of Heterogeneous Control Laws
Published in Roderick Murray-Smith, Tor Arne Johansen, Multiple Model Approaches to Modelling and Control, 2020
Fuzzy control is a family of methods for expressing and applying control laws, using fuzzy sets to provide several benefits. First, they provide the ability to express and use incomplete knowledge of the system being controlled and of the control law itself. Second, they allow one to specify a complex control law as the composition of simple components. Third, fuzzy set membership functions provide smooth transitions from region to region. There are at least two distinct approaches to fuzzy control:Fuzzy logic control determines the control action by a combination of fuzzy logic rules.Heterogeneous control determines the control action as the weighted average of classical control laws. A fuzzy logic controller (Zadeh 1973, Mamdani 1974, Michie and Chambers 1968) consists of a collection of simple control laws whose inputs and outputs are both fuzzy values. For example, If water level is high, then set drain opening to wide; where high and wide are qualitative terms described by fuzzy sets over their quantitative domains.
Control of a Flexible Robot Arm using a Simplified Fuzzy Controller
Published in Hongxing Li, C.L. Philip Chen, Han-Pang Huang, Fuzzy Neural Intelligent Systems, 2018
Hongxing Li, C.L. Philip Chen, Han-Pang Huang
A flexible robot arm is a distributed system per se. Its dynamics are very complicated and coupled with the non-minimum phase nature due to the non-collocated construction of the sensor and actuator. This gives rise to difficulty in the control of a flexible arm. In particular, the control of a flexible arm usually suffers from control spillover and observation spillover due to the use of a linear and approximate model. The robustness and reliability of the fuzzy control have been demonstrated in many applications, particularly, it is perfect for a nonlinear system without knowing the exact system model. However, a fuzzy control usually needs a lot of computation time. In order to alleviate this restraint, a simplified fuzzy controller is developed for real-time control of a flexible robot arm. Furthermore, the self-organizing control based on the simplified fuzzy controller is also developed. The simulation results show that the simplified fuzzy control can achieve the desired performance and the computation time is less than 10 ms so that the real-time control is possible.
Intelligent Control for SISO Nonlinear Systems
Published in Yung C. Shin, Chengying Xu, Intelligent Systems Modeling, Optimization, and Control, 2017
Fuzzy control rules are composed of a series of fuzzy if-then rules where the conditions and the consequences are linguistic variables. This collection of fuzzy rules simplifies the input–output relation of the system in linguistic form as R: IF e1 is Ei AND e2 is Ej, THEN u1 is Un(i,j)
Layer width control in robotic pulsed gas tungsten arc additive manufacturing through composite sensing of vision and arc
Published in International Journal of Computer Integrated Manufacturing, 2023
Jun Xiong, Yongsheng Yu, Guangjun Zhang, Senmu Zheng
The next step is to design a closed-loop controller to control the molten pool width by regulating the process parameters. Fuzzy control, as one of the important intelligent controllers, works with related control rules established by expert experiences and does not need to describe an accurate mathematical model of a controlled object. However, the traditional fuzzy controller needs to predetermine the specific values of the quantization factors Ke and Kce and the scale factor Kci. Consequently, it is difficult to achieve the best control performance in pulsed GTA AM with non-linear, time-varying, and strongly coupled features. In this study, a parameter self-adjusting Fuzzy (PSA-Fuzzy) controller is developed to adjust Ke, Kce and Kci based on the deviation change between the setting and detected values, and the molten pool width is selected as the controller input.
A fuzzy control algorithm for tracing air pollution based on unmanned aerial vehicles
Published in Journal of the Air & Waste Management Association, 2022
Xinyan Jiang, Tao Ding, Yuting He, Xuelin Cui, Zhenguo Liu, Zhenming Zhang
Unlike the above bio-inspired traceability algorithms, the fuzzy control traceability (FCT) algorithm does not directly derive inspiration from a particular species but rather attempts to reproduce the multi-scale observations of various species. Fuzzy control is an intelligent control method based on the concepts of fuzzy mathematics (Cheng, Feng, and Lv 2012; Hu 2021). As a branch of artificial intelligence, fuzzy control is widely used in various fields, but it has rarely been used in the field of atmospheric traceability. Fuzzy control has since been applying to the field of active olfactory study, and the combination of its own robustness and “perceptual action behavior” based on physiology enhances the improvement of atmospheric traceability efficiency. A fuzzy controller can be designed to complete corresponding tasks without relying on the mathematical model of the object or on the information of the concentration gradient and wind direction, rather biological expertise is used to complete the corresponding tasks (Ge, Wang, and Wei et al. 2013; Li and Wang 2013). In addition, UAVs are is used as the carrier of the algorithm to identify, judge, and process gas concentration by imitating the thinking of the human brain, which can efficiently solve the traceability problem in unknown and complex environments. The effectiveness and feasibility of the algorithm were verified through simulations.
Speed sensorless control of a bearingless induction motor based on fuzzy PI fractional MRAS scheme
Published in International Journal of Green Energy, 2022
Ting Xu, Zebin Yang, Xiaodong Sun, Jingjing Jia
Fuzzy control is to summarize expert control experience into control rules, and then to invoke corresponding fuzzy control rules by means of fuzzy reasoning and table lookup. Thus, the controller can simulate the way the human brain thinks, and realize the adjustment of the controlled parameters online in real time. Fuzzy PI control is introduced into fractional MRAS in this paper. The flux linkage error between the two models is taken as the basis for fuzzy reasoning and table lookup, and the corresponding fuzzy rules are called to make the controller output the parameters required by the current control system. As a result, PI parameters are adjusted online in real time and the system has higher control precision. The control structure of the block diagram is shown in Figure 3.