Multi-Agent Based Adaptive Consensus Control for Multiple Manipulators with Kinematic Uncertainties

Cheng, Long; Hou, Zeng-Guang; Tan, Min; Liu, Derong; Zou, An‐Min

doi:10.1109/isic.2008.4635941

Cited by 60 publications

(61 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This control methodology requires a greater algorithmic complexity and the inverse kinematics used in the feedback control loop. Direct measurement of operational space variables are very expensive that caused to limitation used of this controller in industrial robot manipulators [3]. One of the simplest ways to analysis control of multiple DOF robot manipulators are analyzed each joint separately such as SISO systems and design an independent joint controller for each joint.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Adaptive Gain Backstepping Technique

Heidari¹,

Shahcheraghi²,

Heidari³

et al. 2015

IJITCS

View full text Add to dashboard Cite

Abstract-In this research, intelligent adaptive backstepping control is presented as robust control for continuum robot. The first objective in this research is design a ProportionalDerivative (PD) fuzzy system to compensate the system model uncertainties. The second objective is focused on the design tuning gain adaptive methodology according to high quality partly nonlinear methodology. Conventional backstepping controller is one of the important robust controllers especially to control of continuum robot manipulator. The fuzzy controller is used in this method to system compensation. In real time to increase the system robust fuzzy logic theory is applied to backstepping controller. To approximate a time-varying nonlinear dynamic system, a fuzzy system requires a large amount of fuzzy rule base. The adaptive laws in this algorithm are designed based on the Lyapunov stability theorem. This method is applied to continuum robot manipulator to have the best performance.

show abstract

Section: Introductionmentioning

confidence: 99%

Intelligent Adaptive Gain Backstepping Technique

Heidari¹,

Shahcheraghi²,

Heidari³

et al. 2015

IJITCS

View full text Add to dashboard Cite

show abstract

“…Continuum robots represent a class of robots that have a biologically inspired form characterized by flexible backbones and high degrees-of-freedom structures [1][2][3]. Theoretically, the compliant nature of a continuum robot provides infinite degrees of freedom to these devices.…”

Section: Introductionmentioning

confidence: 99%

Design New Intelligent PID like Fuzzy Backstepping Controller

Khajeh¹,

Piltan²,

Rashidian³

et al. 2014

IJMECS

View full text Add to dashboard Cite

Abstract-The minimum rule base Proportional Integral Derivative (PID) Fuzzy backstepping Controller is presented in this research. The popularity of PID Fuzzy backstepping controller can be attributed to their robust performance in a wide range of operating conditions and partly to their functional simplicity. The process of setting of PID Fuzzy backstepping controller can be determined as an optimization task. Over the years, use of intelligent strategies for tuning of these controllers has been growing. PID methodology has three inputs and if any input is described with seven linguistic values, and any rule has three conditions we will need 7 × 7 × 7 = 343 rules. It is too much work to write 343 rules. In this research the PID-like fuzzy controller can be constructed as a parallel structure of a PD-like fuzzy controller and a PI-like controller to have the minimum rule base. However backstepping controller is work based on cancelling decoupling and nonlinear terms of dynamic parameters of each link, this controller is work based on manipulator dynamic model and this technique is highly sensitive to the knowledge of all parameters of nonlinear robot manipulator"s dynamic equation. This research is used to reduce or eliminate the backstepping controller problem based on minimum rule base fuzzy logic theory to control of flexible robot manipulator system and testing of the quality of process control in the simulation environment of MATLAB/SIMULINK Simulator.Index Terms-PID like fuzzy control, backstepping controller, PD like fuzzy control, PI like control, flexible robot manipulator.

show abstract

“…In 1978 the PUMA (Programmable Universal Mach ine for Assembly) and in 1979 the SCA RA (Select ive Co mpliance Assembly Robot Arm) were introduced and they were quickly used in research laboratories and industries. According to the MSN Learning & Research," 700000 robots were in the industrial world in 1995 and over 500000 were used in Japan, about 120000 in Western Europe, and 60000 in the United States [1][2][3][4][5][6]." Research about mechanical parts and control methodologies in robotic system is shown; the mechanical design, type of actuators, and type of systems drive play important roles to have the best performance controller.…”

Section: Introductionmentioning

confidence: 99%

“…These controllers divided into six groups, namely, feedback linearization (co mputedtorque control), passivity-based control, sliding mode control (variable structure control), artificial intelligence control, lyapunov-based control and adaptive control [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. Sliding mode controller (SM C) is a powerful nonlinear controller which has been analyzed by many researchers especially in recent years.…”

Section: Introductionmentioning

confidence: 99%

“…In the theory of infin ity fast switching can provide a good tracking performance but it also can provide some problems (e.g., system instability and chattering phenomenon). After going toward the slid ing surface by discontinues term, equivalent term help to the system dynamics match to the sliding surface [1][2][3][4][5][6]. However, this controller used in many applications but, pure sliding mode controller has fo llo wing challenges: chattering phenomenon, and nonlinear equivalent dynamic formu lation [20].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Design New Online Tuning Intelligent Chattering Free Fuzzy Compensator

Khalilian¹,

Piltan²,

Avatefipour³

et al. 2014

IJISA

View full text Add to dashboard Cite

Abstract− This research is focused on proposed adaptive fuzzy sliding mode algorithms with the adaptation laws derived in the Lyapunov sense. The stability of the closed-loop system is proved mathematically based on the Lyapunov method. Adaptive M IMO fuzzy compensate fuzzy sliding mode method design a M IM O fuzzy system to compensate for the model uncertainties of the system, and chattering also solved by new adaption method. Since there is no tuning method to adjust the premise part of fuzzy rules so we presented a scheme to online tune consequence part of fuzzy rules. Classical sliding mode control is robust to control model uncertainties and external disturbances. A sliding mode method with a switching control low guarantees the stability of the certain and/or uncertain system, but the addition of the switching control low introduces chattering into the system. One of the main targets in this research to reduce or eliminate chattering is to insert online tuning method. Classical sliding mode control method has difficulty in handling unstructured model uncertainties. One can overcome this problem by combining a sliding mode controller and artificial intelligence (e.g. fuzzy logic). To approximate a time-varying nonlinear dynamic system, a fuzzy system requires a large amount of fuzzy rule base. This large number of fuzzy rules will cause a high computation load. The addition of an adaptive law to a fuzzy sliding mode controller to online tune the parameters of the fuzzy rules in use will ensure a moderate computational load. The adaptive laws in this algorithm are designed based on the Lyapunov stability theorem. Asymptotic stability of the closed loop system is also proved in the sense of Lyapunov. This method is applied to continuum robot manipulator to have the best performance.

show abstract

Multi-Agent Based Adaptive Consensus Control for Multiple Manipulators with Kinematic Uncertainties

Cited by 60 publications

References 18 publications

Intelligent Adaptive Gain Backstepping Technique

Intelligent Adaptive Gain Backstepping Technique

Design New Intelligent PID like Fuzzy Backstepping Controller

Design New Online Tuning Intelligent Chattering Free Fuzzy Compensator

Contact Info

Product

Resources

About