Performance Study of PID and Fuzzy Controllers for Position Control of 6 DOF arm Manipulator with Various Defuzzification Strategies

Kharidege, Ahmed; Ding, Jianbiao; Zhang, Yajun

doi:10.1051/matecconf/20167701011

Cited by 9 publications

(7 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The output of each rule was analyzed by the Mamdani min-max inference method. Various defuzzification methods were tested and compared [19]. It was found that the centroid of area method gave the least steady state error [19].…”

Section: Friction Compensated Fuzzy Controllermentioning

confidence: 99%

“…Fuzzy controller imitates human decision making and can deal with complex problems. The controller which was proved to be simple to implement yet powerful, has been effectively implemented in various applications that dealt with nonlinearities [18][19][20]. The performance of a fuzzy controller was better than PID and genetic algorithm controllers on a cruise control application [18].…”

Section: Introductionmentioning

confidence: 99%

“…The performance of a fuzzy controller was better than PID and genetic algorithm controllers on a cruise control application [18]. Fuzzy control yielded a better transient performance compared to PID controller in a 6 DOF robot manipulator joint control system, though both controllers gave the same converged steady state response [19]. Fuzzy control, implemented along with a simple static friction compensation, could control pneumatic cylinder position to a sub-micrometers order accuracy [20].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Friction Compensated Force Control of Electro-Hydraulic System Using Fuzzy Controller

2022

IJIES

View full text Add to dashboard Cite

Steady state and dynamic LuGre friction models of an electro-hydraulic system were constructed. Indirect electro-hydraulic cylinder force control under friction compensated PID and fuzzy controllers were tested and compared. Feedback force signal was indirectly measured and calculated from pressures to emulate the applications that direct force measurement is impossible. Estimated friction was compensated to desired force command of the PID controller. The proposed fuzzy controller contained two modules. The first, as a friction compensator, calculated modified error according to estimated friction and feedback force error. The second module calculated control action based on PD control scheme. Friction compensated PID and fuzzy controllers could reduce tracking errors at the maximum force commands by 80% and 90%, respectively, compared to the PID controller without compensation. The steady state friction compensation yielded better tracking performances compared to the dynamic friction. Tracking performance of the fuzzy controller was always better than the PID controller.

show abstract

Section: Friction Compensated Fuzzy Controllermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Friction Compensated Force Control of Electro-Hydraulic System Using Fuzzy Controller

2022

IJIES

View full text Add to dashboard Cite

show abstract

“…Respon transien hasil tuning PID controller dan FLC dibandingkan [17], [18]. Hal ini dimaksudkan untuk melihat kinerja controller pada obyek yang sama.…”

Section: Pendahuluanunclassified

Metode Tuning Operating Range Fuzzy PID Controller pada Sistem Orde Tiga

Sutarna

Purwanti

2020

Jurnal Teknik Elektro

View full text Add to dashboard Cite

This research discusses the development of a model Fuzzy Proportional, Integral, Derivative (Fuzzy PID) controller models and self-tuning. This method has been implemented in the third-order system of Differential Equations, as a sample implementation of a PID controller. The methods self-tuning known are fuzzy rules, membership function (MF), and scaling factor. The focus of the discussion in this research is to introduce self-tuning to the operating range (OR) setting of MF. Previous research has succeeded in converting a PID controller to a Fuzzy Logic Controller (FLC) which is in accordance with the PID structure. The FLC has three inputs and one output as in the PID controller, hereinafter referred to as Fuzzy PID controller. Knowledgebase on the FLC structure of three inputs one output is expressed in the form of cubic fuzzy associative memory (FAM). The conversion was done by mapping errors, integrals error, derivative errors and outputs of PID controller into the OR of MF Fuzzy input/output. The size of the OR conversion value on the MF fuzzy input was set, so the response transient is set-to-point. While the OR value of the MF fuzzy output was fixed as a limitation. Improved settling time was reached up to 75.3% and percent overshoot was reduced by 57.7% in Fuzzy PID with PID controller. The output signal from the Fuzzy PID controller showed the smallest amplitude of 24.12, while the PID controller was 32.53. The amplitude unit depends on the Fuzzy PID controller parameters when it applied the real plant. The third-order Fuzzy PID controller has been successfully simulated in Simulink/Matlab.

show abstract

“…Several strategies have focused on the EPW's velocity and direction control using different kinds of motors such as DC Motor [3], PMSM [4], brushless DC motors. We can find several examples in previous works such as adaptive controller [5], neural control techniques [6], robust controllers [7,8], sliding mode [9,10], backstepping [3], and fuzzy logic [11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Comparative Study Between Integrator Backstepping and Fuzzy Logic Control Applied to an Electric Powered Wheelchair

Mohammed¹,

Boumédiène²,

Boubekeur³

2021

EJEE

View full text Add to dashboard Cite

The aim of this paper is the control of electric powered wheelchairs (EPW) which was made for people suffering of temporary or permanent disabilities due to illnesses or accidents. The EPW is powered by two Permanent Magnet Synchronous Motors (PMSM) that are characterized by high efficiency, high torque, low noise and robustness; hence the dynamic model of the both EPW-motors is presented in the first. After that, a comparative study is made between two nonlinear command theory; Integrator Backstepping based on the second method of Lyapunov which combine the choice of the energy function with the laws control, and, fuzzy logic introduced to approach human reasoning with the help of an adequate representation of knowledge. To evaluate the performance of the two controls, numerical simulations are presented to show the evolution of electrical and mechanical quantities, the energy consumed and the squared error of the displacement and velocity. However, the reference trajectory used is that generated by the fifth-degree polynomial interpolation, which ensures a regular trajectory that is continuous in positions, velocities and accelerations.

show abstract

Performance Study of PID and Fuzzy Controllers for Position Control of 6 DOF arm Manipulator with Various Defuzzification Strategies

Cited by 9 publications

References 8 publications

Friction Compensated Force Control of Electro-Hydraulic System Using Fuzzy Controller

Friction Compensated Force Control of Electro-Hydraulic System Using Fuzzy Controller

Metode Tuning Operating Range Fuzzy PID Controller pada Sistem Orde Tiga

Comparative Study Between Integrator Backstepping and Fuzzy Logic Control Applied to an Electric Powered Wheelchair

Contact Info

Product

Resources

About