Dynamics and Control of a Two-link Manipulator using PID and Sliding Mode Control

Mohammed, Amin A.; Eltayeb, Ahmed

doi:10.1109/iccceee.2018.8515795

Cited by 21 publications

(11 citation statements)

References 4 publications

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“…In the task of performing the ADRRAC for EMA position servo control, we take two other comparative control strategies, which are PID (Proportional Integral Derivative) control and ARC (Adaptive Robust Control). In practical use, the former is of high robustness and high reliability [36,37] while the latter is known for its stability and accuracy [38,39]. Specifically, the Ziegler-Nichols tuning method is applied to the PID controller for parameter tuning, based on which to reduce the oscillation and improve the robustness [40].…”

Section: Results Analysismentioning

confidence: 99%

Design and Analysis of an Active Disturbance Rejection Robust Adaptive Control System for Electromechanical Actuator

Chen

Zhu

et al. 2021

Actuators

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Airline electromechanical actuators (EMAs), on the task of controlling flight surfaces, hold a great promise with the development of more- and all-electric aircraft. Notwithstanding, the deficiencies in both robustness and adaptability of control algorithms prevent EMAs from extensive use. However, the state-of-the-art control schemes fail to precisely compensate the system nonlinear uncertainties of servo control. In this paper, from the innovation point of view, we tend to put forward the foundation of devising an active disturbance rejection robust adaptive control (ADRRAC) strategy, whose main purpose is to deal with the position servo control of EMA. Specifically, an adaptive control law is designed and deployed for resolving not only the nonlinear disturbance, but also the parameter uncertainties. In addition, an extended disturbance estimator is employed to estimate the external disturbance and thus eliminate its impact. The proposed controlling algorithm is deemed best able to address the external disturbance based on the nonlinear uncertainty compensation. With the input parameters and control commands, the ADRRAC strategy maintains servo system stability while approaching the controlling target. Following the algorithm description, a proof of the controlling stability of ADRRAC strategy is presented in detail as well. Experiments on a variety of tracking tasks are conducted on a prototype of an EMA to investigate the working performance of the proposed control strategy. The experimental outcomes are reported, which verify the effectiveness of the ADRRAC strategy, compared to widely applied control strategies. According to the data analysis results, our controller is capable of obtaining an even faster system response, a higher tracking accuracy and a more stable system state.

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Section: Results Analysismentioning

confidence: 99%

Design and Analysis of an Active Disturbance Rejection Robust Adaptive Control System for Electromechanical Actuator

Chen

Zhu

et al. 2021

Actuators

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“…A robotic arm with two-degree of freedom is a classic example of a simple nonlinear multi-input multi-output (MIMO) dynamic system in robotic literature. It represents a benchmark for testing and evaluating the performance of different control concepts and has been utilized by several researchers to study and compare various control schemes [1]. Compared to other control methods, SMC can offer various good properties, such as simplicity, high robustness to external disturbances and low sensitivity to variations in system parameters [2].…”

Section: Introductionmentioning

confidence: 99%

Optimization of the Performances of a Two-Joint Robotic Arm using Sliding Mode Control

Bendimrad

AMRANI

2022

Int. J. Automot. Mech. Eng.

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In this paper, we worked on the control of the angular position of a two-joint robotic arm by the sliding mode technique, after having establishing the dynamic equations of the system by the Lagrange method, with the purpose of improving the performances of the system by acting on certain parameters related to the sliding mode technique. The simulation results show an optimization of the sliding mode controller parameters, generally, in the response of the controlled system, which consists on minimizing error and settling time, and eliminating the unwanted phenomenon of chattering, after finding the optimal values of these parameters. Verification by simulation of the robustness of the optimized robotic arm shows that its response is independent of the dimensions and masses of the bodies of this robotic arm, as well as of the applied load. this answer always corresponds to the best performances of speed, settling time and margin of error. the only quantity that varies according to the parameters of the robotic arm and the applied load is the torque required. This couple has a compensating effect to the change of these internal parameters and of this applied load, to keep the same optimal response on the condition of communicating these changes with the controller.

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“…Some researchers have developed low-cost robotic arm controller using a simple controller such as proportional integral (PI) controller [6], PID [7]- [9], fuzzy logic [10], [11], hybrid PID, and fuzzy [12], [13], optimal controller [14], neural network [15] and model-based controller [16]. A few researchers also tried to use robust control to control robotic arm joints such as sliding mode controller [17]- [20] and hybrid PIDsliding mode controller [21]. However, all of those researches were done on simulation or implemented on a very small robotic arm using small radio control (RC) servo motors.…”

Section: Introductionmentioning

confidence: 99%

Robotic arm joint position control using iterative learning and mixed sensitivity H∞ robust controller

Sutyasadi

Wicaksono

2021

Bulletin EEI

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This paper proposes an improved control strategy of a robotic arm joint using hybrid controller consist of H∞ robust controller and iterative learning controller. The main advantage of this controller is the simple structure that made it possible to be implemented on a small embedded system for frugal innovation in industrial robotic arm development. Although it has a simple structure, it is a robust H∞ controller that has robust stability and robust performance. The iterative learning controller makes the trajectory tracking even better. To test the effectiveness of the proposed method, computer simulations using Matlab and hardware experiments were conducted. Variation of load was applied to both of the processes to present the uncertainties. The superiority of the proposed controller over the proportional integral derivative (PID) controller that usually being used in a low-cost robotic arm development is confirmed that it has better trajectory tracking. The error tracking along the slope of sinusoidal trajectory input was suppressed to zero. The biggest error along the trajectory that happened on every peak of the sinusoidal input, or when the direction is changed has been improved from 15 degrees to 4 degrees. This can be conceived that the proposed controller can be applied to control a low-cost robotic arm joint position which is applicable for small industries or educational purpose.

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Dynamics and Control of a Two-link Manipulator using PID and Sliding Mode Control

Cited by 21 publications

References 4 publications

Design and Analysis of an Active Disturbance Rejection Robust Adaptive Control System for Electromechanical Actuator

Design and Analysis of an Active Disturbance Rejection Robust Adaptive Control System for Electromechanical Actuator

Optimization of the Performances of a Two-Joint Robotic Arm using Sliding Mode Control

Robotic arm joint position control using iterative learning and mixed sensitivity H∞ robust controller

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