scholarly journals Design of Robust Adaptive Fuzzy Controller for a Class of Single-Input Single-Output (SISO) Uncertain Nonlinear Systems

2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Jieqiong Lin ◽  
Jiakang Zhou ◽  
Mingming Lu ◽  
Hao Wang ◽  
Allen Yi
Keyword(s):  
Fuzzy Controller ◽  
Elliptical Vibration Cutting ◽  
Single Input Single Output ◽  
Vibration Cutting ◽  
Adaptive Fuzzy Controller ◽  
Adaptive Fuzzy ◽  
Elliptical Vibration ◽  
Single Output ◽  
Single Input ◽  
Robust Adaptive

In order to solve the precision and stability control problems of nonlinear uncertain systems applied in machining systems, in this paper, a robust adaptive fuzzy control technique based on Dynamic Surface Control (DSC) method is proposed for the generalized single-input single-output (SISO) uncertain nonlinear system. A first-order low-pass filter is introduced in each step of the traditional robust control method to overcome the “calculation expansion” problem, and Takagi–Sugeno (T-S) fuzzy logic system is applied to approximate an uncertain nonlinear function of unknown structure in the system. The designed robust adaptive fuzzy controller is applied to the 3D elliptical vibration cutting (3D EVC) device system model, and the effectiveness of the controller design is verified by analysis of position tracking, speed tracking, and tracking error. The results of studies show that the robust adaptive fuzzy controller can effectively suppress the jitter problem of the three-dimensional elliptical vibration cutting device so that the control object can be stabilized quickly even if it has a little jitter at the beginning. It can be smoothed to move along the ideal displacement and velocity signals. It is verified that the designed controller has strong robust adaptability.

scholarly journals Parameter tuning of robust adaptive fuzzy controller for 3D elliptical vibration-assisted cutting

2021 ◽  
Vol 12 (1) ◽  
pp. 433-442
Author(s):  
Yongsheng Du ◽  
Mingming Lu ◽  
Hao Wang ◽  
Jiakang Zhou ◽  
Jieqiong Lin
Keyword(s):  
Fuzzy Controller ◽  
Optimal Solution ◽  
Parameter Tuning ◽  
Precision Machining ◽  
Step Size ◽  
Adaptive Fuzzy Controller ◽  
Adaptive Fuzzy ◽  
Elliptical Vibration ◽  
Robust Adaptive ◽  
Control Accuracy

Abstract. Elliptical vibration cutting (EVC), as a precision machining technology, is used in many applications. In precision machining, control accuracy plays an essential role in improving the machinability of difficult-to-machine materials. To improve the control accuracy, dynamic and static characteristics of the system need to be tuned to obtain the optimal parameters. In this paper, we use a glowworm algorithm with an improved adaptive step size to tune the parameters of a robust adaptive fuzzy controller. We then obtain the optimal controller parameters through simulation. The optimal solution of the controller parameters is then applied to a 3D EVC system model for simulation and closed-loop testing experiments. The results indicate that a good agreement between the ideal curve and the tracking signal curve verifies the optimality of the controller parameters. Finally, under certain cutting conditions, the workpieces of three different materials are cut with two different cutting methods. The study revealed that the surface roughness value is reduced by 20 %–32 %, which further verifies the effectiveness of the optimal controller's parameters.

Indirect Adaptive Fuzzy H∞ Tracking Control with Self-Structuring Algorithm for a Class of Uncertain Nonlinear SISO Systems

2013 ◽  
Vol 756-759 ◽  
pp. 622-626
Author(s):  
Sen Xu ◽  
Zhang Quan Wang ◽  
You Rong Chen ◽  
Ban Teng Liu ◽  
Lu Yao Xu
Keyword(s):  
Fuzzy Controller ◽  
Controller Design ◽  
Pendulum System ◽  
Single Input Single Output ◽  
Adaptive Fuzzy ◽  
Single Output ◽  
Inverted Pendulum System ◽  
Single Input ◽  
Indirect Adaptive Fuzzy Controller ◽  
Siso Systems

Indirect adaptive fuzzy controller with a self-structuring algorithm is proposed in this paper to achieve tracking performance for a class of uncertain nonlinear single-input single-output (SISO) systems with external disturbances. Selecting membership functions and the fuzzy rules are difficult in fuzzy controller design. As a result, self-structuring algorithm is used in this paper, which simplifies the design of fuzzy controller. Lyapunov analysis is used to prove asymptotic stability of the proposed approach. Application of the proposed control scheme to a second-order inverted pendulum system demonstrates the effectiveness of the proposed approach.

Multi-model Fuzzy Modal Control of SISO Nonlinear Plant

2016 ◽  
Vol 14 (4) ◽  
pp. 19-26 ◽  
Author(s):  
V. Lukov ◽  
M. Alexandrova ◽  
N. Nikolov
Keyword(s):  
Fuzzy Controller ◽  
Static Characteristic ◽  
Control Law ◽  
Modal Control ◽  
Single Input Single Output ◽  
Single Output ◽  
Single Input ◽  
Nonlinear Static ◽  
Takagi Sugeno ◽  
Nonlinear Plant

Abstract The article presents the synthesis of a multi-model modal control of single input – single output nonlinear plant, based on Takagi-Sugeno fuzzy controller. For that purpose, the nonlinear static characteristic of the plant is presented by two linear parts. These two linear structures are described in state space. The feedback vectors and the coefficients ki of the modal controllers are calculated. An integral component in the control law is added.

scholarly journals Design of Hybrid Neural Fuzzy Controller for Human Robotic Leg System

2018 ◽  
Vol 14 (1) ◽  
pp. 145-155
Author(s):  
Ekhlas H. Karam ◽  
Ayam M Abbass ◽  
Noor S. Abdul-Jaleel
Keyword(s):  
Fuzzy Controller ◽  
Nonlinear Function ◽  
Matlab Simulation ◽  
Single Node ◽  
Differential Model ◽  
Single Input Single Output ◽  
Single Output ◽  
Single Input ◽  
Robotic Leg ◽  
Neural Fuzzy

 In this paper, the human robotic leg which can be represented mathematically by single input-single output (SISO) nonlinear differential model with one degree of freedom, is analyzed and then a simple hybrid neural fuzzy controller is designed to improve the performance of this human robotic leg model. This controller consists from SISO fuzzy proportional derivative (FPD) controller with nine rules summing with single node neural integral derivative (NID) controller with nonlinear function. The Matlab simulation results for nonlinear robotic leg model with the suggested controller showed that the efficiency of this controller when compared with the results of the leg model that is controlled by PI+2D, PD+NID, and FPD-ID controllers.

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