Adaptive fuzzy logic with self-tuned membership functions based repetitive learning control of robotic manipulators

Yilmaz, Bayram Melih; Tatlıcıoğlu, Enver; Savran, Aydoğan; Alcı, Musa

doi:10.1016/j.asoc.2021.107183

Cited by 22 publications

(9 citation statements)

References 30 publications

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“…Due to the complexity of the temperature system of the plastic extruder [12], there are a large number of uncertain factors affecting the temperature change, such as the raw material temperature, the extrusion stress between the screw and the raw material, the coupling of the temperature zones, the head temperature, shear heat and other factors. Therefore, It is difficult to build a mathematical model of the temperature system of an extruder [12]， and the corresponding parameters of the extruder temperature control system can only be analyzed and calculated through the parameter identification method, which leads to the transfer function model [13].…”

Section: Mathematical Modeling Of Extruder Temperature Control Systemmentioning

confidence: 99%

Research and Application of Improved Particle Swarm Fuzzy PID Algorithm Based on Self- Disturbance Rejection in Temperature Control System of Plastic Extruder

Meng,

Sun,

Pan

et al. 2024

IEEE Access

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Plastic profiles are mainly processed by plastic extruders. In order to better solve the problems of hysteresis, high overshoot and low anti-interference ability of the extruder temperature system. First, the process flow and working principle of the extruder are analyzed, and the extruder temperature control system is developed on this basis. The step response curve identification method is used to obtain the system model parameters and establish the mathematical model of the temperature control system. On the basis of previous research on Fuzzy PID control, for the problem of empirical priority of Fuzzy control, a control method based on the improved particle swarm algorithm iteratively solves the key parameters in the Fuzzy PID variational domain algorithm is proposed, and the improved particle swarm Fuzzy PID controller is designed, and the control model is constructed, and the writing and debugging of the control algorithm is completed. Finally, step and interference simulation experiments are designed to simulate and analyze the temperature control effect of traditional PID, Fuzzy PID and improved particle swarm Fuzzy PID on the temperature control system. The results show that: the improved particle swarm Fuzzy PID significantly reduces the maximum overshooting amount, greatly shortens the restoration of steady state time, and at the same time meets the requirements of the system to control the process overshooting amount, has a strong resistance to dryness, and is able to better adapt to the changes in the system control output, which meets the system's demand for temperature control accuracy, stability and rapidity.Keyword：Plastics extrusion machine， Temperature accuracy， Particle swarm optimization， Stability control.

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Section: Mathematical Modeling Of Extruder Temperature Control Systemmentioning

confidence: 99%

Research and Application of Improved Particle Swarm Fuzzy PID Algorithm Based on Self- Disturbance Rejection in Temperature Control System of Plastic Extruder

Meng,

Sun,

Pan

et al. 2024

IEEE Access

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“…The SMC provides robust control solutions, but the reaching and sliding phases may require a long time and high control effort due to the conservative bounds of the model uncertainty. To ensure high control performance, fast convergence, and robustness against disturbances and robot manipulator variations, a threeterm model-free integral SMC can be defined by (10):…”

Section: A Model-free Integral Smc Designmentioning

confidence: 99%

“…12, No. Substituting the control law (10) into the robot equation ( 1), the closed-loop system dynamics are obtained as (13):…”

Section: A Model-free Integral Smc Designmentioning

confidence: 99%

“…The model uncertainties stemming from frictions, parametric uncertainties and load disturbances are the inherent issues of the model-based controllers. To eliminate the effects of these uncertainties, robust, adaptive and machine learning techniques have been used in the model-based control designs [8]- [10]. Specifically, many forms of SMC approaches have been designed for robot manipulators including traditional SMC [11], integral SMC [12], fuzzy SMC [13], backstepping SMC [7], [14], terminal SMC [15], [16], second-order SMCs [17], nonsingular terminal SMC [18], fast terminal SMC [19], time-delay estimation based SMC [20] and adaptive SMC [21], [22], but these approaches require an accurate robot model to assure a good control response.…”

Section: Introductionmentioning

confidence: 99%

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A model-free continuous integral sliding mode controller for robust control of robotic manipulators

Ablay

2023

IJRA

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<p>This paper proposes a model-free continuous integral sliding mode controller for robust control of robotic manipulators. The highly nonlinear dynamics of robots and load disturbances cause control challenges. To achieve tracking control under load disturbances and nonlinear parameter variations, the controller is constructed with three continuous terms including an integral term that acts as an adaptive controller. The proposed controller is able to accomplish a non-overshoot transient response, a short settling time, and strong disturbance rejection performance for robotic manipulators. The developed model-free control method is implemented on the PUMA 560 robotic manipulator, and its performance is compared with the proportional-derivative (PD) plus gravity controller. Numerical results under measurement noise and load disturbances are provided in order to show the efficacy, validity, and feasibility of the method.</p>

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“…It is always challenging to design controllers for robotic systems in the presence of uncertainties and/or disturbances, despite the extensive so-called robust control methods, such as robust adaptive control [3], repetitive control [4], back-stepping techniques [5], iterative learning control [6], etc. Among them, sliding mode control [7], with its simplicity in application, insensitivity to parameter variations and disturbances implicit in the input channels and non-model based robustness, remains one of the most effective approaches in handling bounded uncertainties and/or disturbances [8,9].…”

Section: Introductionmentioning

confidence: 99%

Chattering-Suppressed Sliding Mode Control for Flexible-Joint Robot Manipulators

Cheng

Liu

2021

Actuators

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In this paper, sliding mode tracking control and its chattering suppression method are investigated for flexible-joint robot manipulators with only state measurements of joint actuators. First, within the framework of singular perturbation theory, the control objective of the system is decoupled into two typical tracking aims of a slow subsystem and a fast subsystem. Then, considering lumped uncertainties (including dynamics uncertainties and external disturbances), a composite chattering-suppressed sliding mode controller is proposed, where a smooth-saturation-function-contained reaching law with adjustable saturation factor is designed to alleviate the inherent chattering phenomenon, and a radial basis function neural network (RBFNN)-based soft computing strategy is applied to avoid the high switching gain that leads to chattering amplification. Simultaneously, an efficient extended Kalman filter (EKF) with respect to a new state variable is presented to enable the closed-loop tracking control with neither position nor velocity measurements of links. In addition, an overall analysis on the asymptotic stability of the whole control system is given. Finally, numerical examples verify the superiority of the dynamic performance of the proposed control approach, which is well qualified to suppress the chattering and can effectively eliminate the undesirable effects of the lumped uncertainties with a smaller switching gain reduced by 80% in comparison to that in the controller without RBFNN. The computational efficiency of the proposed EKF increased by about 26%.

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Adaptive fuzzy logic with self-tuned membership functions based repetitive learning control of robotic manipulators

Cited by 22 publications

References 30 publications

Research and Application of Improved Particle Swarm Fuzzy PID Algorithm Based on Self- Disturbance Rejection in Temperature Control System of Plastic Extruder

Research and Application of Improved Particle Swarm Fuzzy PID Algorithm Based on Self- Disturbance Rejection in Temperature Control System of Plastic Extruder

A model-free continuous integral sliding mode controller for robust control of robotic manipulators

Chattering-Suppressed Sliding Mode Control for Flexible-Joint Robot Manipulators

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