On line Tuning Premise and Consequence FIS: Design Fuzzy Adaptive Fuzzy Sliding Mode Controller Based on Lyaponuv Theory (original) (raw)

Design mathematical tunable gain PID-like sliding mode fuzzy controller with minimum rule base

International Journal of Robotic and Automation, 2 (3): 146-156, 2011

In this study, a mathematical tunable gain model free PID-like sliding mode fuzzy controller (GTSMFC) is designed to rich the best performance. Sliding mode fuzzy controller is studied because of its model free, stable and high performance. Today, most of systems (e.g., robot manipulators) are used in unknown and unstructured environment and caused to provide sophisticated systems, therefore strong mathematical tools (e.g., nonlinear sliding mode controller) are used in artificial intelligent control methodologies to design model free nonlinear robust controller with high performance (e.g., minimum error, good trajectory, disturbance rejection). Non linear classical theories have been applied successfully in many applications, but they also have some limitation. One of the best nonlinear robust controller which can be used in uncertainty nonlinear systems, are sliding mode controller but pure sliding mode controller has some disadvantages therefore this research focuses on applied sliding mode controller in fuzzy logic theory to solve the limitation in fuzzy logic controller and sliding mode controller. One of the most important challenging in pure sliding mode controller and sliding mode fuzzy controller is sliding surface slope. This paper focuses on adjusting the gain updating factor and sliding surface slope in PID like sliding mode fuzzy controller to have the best performance and reduce the limitation.

Design a New Fuzzy Optimize Robust Sliding Surface Gain in Nonlinear Controller

Control of robotic manipulator is very important in field of robotic, because robotic manipulators are multi-input multi-output (MIMO), nonlinear and most of dynamic parameters are uncertainty. Today, robot manipulators used in unknown and unstructured environment which caused to provides sophisticated systems, therefore strong mathematical tools used in new control methodologies to design adaptive nonlinear robust controller with acceptable performance (e.g., minimum error, good trajectory, disturbance rejection). One of the best nonlinear robust controller which can be used in uncertainty nonlinear systems, are sliding mode controller but pure sliding mode controller has some disadvantages therefore this research focuses on the design fuzzy sliding mode controller. One of the most important challenging in pure sliding mode controller and sliding mode fuzzy controller is sliding surface slope. This paper focuses on adjusting the sliding surface slope in sliding mode fuzzy controller to have the best performance and reduce the limitation.

Design sliding mode controller for robot manipulator with artificial tunable gain

Canadian journal of Pure and Applied Science,5(2): 1573-1579, 2011

One of the most active research areas in the field of robotics is robot manipulators control, because these systems are multi-input multi-output (MIMO), nonlinear, and uncertainty. At present, robot manipulators are used in unknown and unstructured situation and caused to provide complicated systems, consequently strong mathematical tools are used in new control methodologies to design nonlinear robust controller with satisfactory performance (e.g., minimum error, good trajectory, disturbance rejection). Robotic systems controlling is vital due to the wide range of application. Obviously stability and robustness are the most minimum requirements in control systems; even though the proof of stability and robustness is more important especially in the case of nonlinear systems. The strategies of robotic manipulators control are classified into two main groups: classical and non-classical methods, where the conventional control theory uses the classical method and the artificial intelligence theory (e.g., fuzzy logic, neural network, and neuro fuzzy) uses the non-classical methods. However both of classical and non-classical theories have applied successfully in many applications, but they also have some limitations. One of the best nonlinear robust controllers which can be used in uncertainty nonlinear systems is sliding mode controller (SMC). Sliding mode controller has two most important challenges: chattering phenomenon and nonlinear dynamic equivalent part. This paper is focused on the applied nonclassical method (e.g., Fuzzy Logic) in robust classical method (e.g., Sliding Mode Controller) in the presence of uncertainties and external disturbance to reduce the limitations. Applying the Mamdani’s error based fuzzy logic controller with 7 rules is the main goal that causes the elimination chattering phenomenon with regard to the variety of uncertainty and external disturbance; as a result this paper focuses on the sliding mode controller with artificial tuneable gain (SMCAT) to adjusting the sliding surface slope coefficient depends on applying fuzzy method.

An Adaptive sliding surface slope adjustment in PD Sliding Mode Fuzzy Control for Robot Manipulator

Robotic manipulators are multi-input multi-output (MIMO), nonlinear and most of dynamic parameters are uncertainty so design a high performance controller for these plants is very important. Today, strong mathematical tools used in new control methodologies to design adaptive nonlinear robust controller with acceptable performance. One of the best nonlinear robust controller which can be used in uncertainty nonlinear systems, are sliding mode controller but pure sliding mode controller has some disadvantages such as nonlinear dynamic uncertainties therefore to design model free sliding mode controller this research focuses on applied fuzzy logic controller in sliding mode controller. One of the most important challenging in pure sliding mode controller and sliding mode fuzzy controller is sliding surface slope coefficient therefore the second target in this research is design a supervisory controller to adjusting the sliding surface slope in sliding mode fuzzy controller.

Design a new sliding mode adaptive hybrid fuzzy controller

Journal of Advanced Science & Engineering Research, 1 (1): 115-123, 2011

One of the most important challenges in nonlinear, multi-input multi-output (MIMO) and time variant systems (e.g., robot manipulator) is designing a controller with acceptable performance. This paper focuses on design a new sliding mode on-line tunable gain hybrid fuzzy control (SMHFLC) applied in the robot manipulator. The PD fuzzy controller is designed as 49 rules Mamdani’s error based which the integral error controller is added to the fuzzy controller to get the better performance. One of the most important robust non linear methodologies is sliding mode method. On-line tuning is used in systems with various dynamic parameters, structure and unstructured uncertainties and need to be training on line. Therefore combined adaptive method and hybrid PD fuzzy controllers can solve the uncertainty challenge in nonlinear systems.

Comparison of Conventional & Fuzzy based Sliding Mode PID Controller for Robot Manipulator

IEEE

High accuracy trajectory tracking is challenging topic in robotic manipulator control. This is due to nonlinearities and input coupling present in robotic arm. In this paper, a chattering free sliding mode control (SMC) for a robot manipulator including PID part with a fuzzy tunable gain is designed. The main idea is that the robustness property of SMC and good response characteristics of PID are combined with fuzzy tuning gain approach to achieve more acceptable performance. A PID sliding surface is considered such that the robot dynamic equation can be rewritten in terms of sliding surface. Then in order to decrease the reaching time to the sliding surface and deleting the oscillation of the response, a fuzzy tuning system is used for adjusting both controller gains including sliding controller gain parameter and PID coefficient. Controller is applied to two link robot manipulator including model uncertainty and external disturbance as a case study. Simulation study has been done in MATLAB/Simulink environment shows the improvements of the results compare to conventional SMC.

Comparison of conventional & fuzzy based sliding mode PID controller for robot manipulator

2013 International Conference on Individual and Collective Behaviors in Robotics (ICBR), 2013

Design of Self Adaptive Fuzzy Sliding Mode Controller for Robot Manipulators

Computational Intelligence and Machine Learning, 2022

This paper intends to design and develop an adaptive fuzzy sliding mode controller (SMC) for robotic manipulator. Since it is not viable to pair the SMC operations with the system model every time, this paper adopts a Fuzzy Inference System (FIS) to replace the system model. It effectively achieves the experimentation in two phases. Accordingly, in the first phase, it attains the accurate features of the system model based on varied samples to characterize the robotic manipulator. In the second stage, it represents the derived fuzzy rules based on adaptive fuzzy membership functions. Moreover, it establishes the self-adaptiveness using Grey Wolf Optimization (GWO) to attain the adaptive fuzzy membership functions. The analysis distinguishes the efficiency of the adopted technique with the optimal investigational scheme and the traditional schemes such as SMC, Fuzzy SMC (FSMC) and GWO-SMC. Moreover, the comparative analysis is also performed by including the noise and validates the effectiveness of the proposed and conventional models.

Design a robust self-tuning fuzzy sliding mode control for second order systems

2012

Robotic systems as second-order systems are essential part in the industry world. Controls of These systems are critical outstanding to a wide range of their application. Robustness and stability are two important demands in any control system. Control of Robot manipulators is challenged because they are multi-input multi-output (MIMO), nonlinear, time variant and have uncertainty. Furthermore, the robot manipulators used in unknown and unstructured situation, so they provide sophisticated systems. Therefore, it is a challenge to design an adaptive nonlinear robust controller, with has had suitable performance (e.g., minimum error, good trajectory, disturbance rejection). Non-classical Control methods used the artificial intelligence theory (e.g., fuzzy logic, neural network, and neuro fuzzy) to reduce the limitation of these types of systems. In This paper by using fuzzy rules and a non-classic method, an adaptive fuzzy sliding mode controller will be presented.

A New Estimate Sliding Mode Fuzzy Controller for Robot Manipulator

International Journal of Robotics and Automation,3 (1):45-60, 2012

One of the most active research areas in field of robotics is control of robot manipulator because this system has highly nonlinear dynamic parameters and most of dynamic parameters are unknown so design an acceptable controller is the main goal in this work. To solve this challenge position new estimation sliding mode fuzzy controller is introduced and applied to robot manipulator. This controller can solve to most important challenge in classical sliding mode controller in presence of highly uncertainty, namely; chattering phenomenon based on fuzzy estimator and online tuning and equivalent nonlinear dynamic based on estimation. Proposed method has acceptable performance in presence of uncertainty (e.g., overshoot=0%, rise time=0.8 s, steady state error = 1e-9 and RMS error=0.0001632).

On line Tuning Premise and Consequence FIS: Design Fuzzy Adaptive Fuzzy Sliding Mode Controller Based on Lyaponuv Theory (original) (raw)

Related papers