Trajectory Tracking Control of UR5 Robot: a PD with Gravity Compensation and Sliding Mode Control Comparison

Charaja, Jhon; Munoz-Panduro, Emanuel; Ramos, Oscar E.; Canahuire, Ruth

doi:10.1109/iccad49821.2020.9260559

Cited by 5 publications

(11 citation statements)

References 6 publications

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“…The position tracking of the UR5 is illustrated in Figure 4, which demonstrates a very small tracking error in the UR5 end-effector position. To have a comparison with a state-of-the-art control algorithm for UR5, the proposed To have a comparison with a state-of-the-art control algorithm for UR5, the proposed approach is compared with sliding mode control approach, which was previously applied To have a comparison with a state-of-the-art control algorithm for UR5, the proposed approach is compared with sliding mode control approach, which was previously applied to a UR5 in [39]. The trajectory given for the robot is the same as the one given in [39], as follows:…”

Section: Parametermentioning

confidence: 99%

“…Moreover, x i do and x i ro represent the desired quaternion and real quaternion orientation of the object. Using the indexes as introduced in Equations ( 48) and (49), a comparison can be made between the proposed approach and the approach previously introduced in [39]. As presented in Table 2, while ||E p || is equal to 8.255 × 10 −5 m in the case of the proposed approach in this paper, it is equal to 0.0068 m in the sliding mode control approach presented in [39].…”

Section: Parametermentioning

confidence: 99%

“…The results demonstrate the implementability, showing the satisfactory performance of the overall controller in the presence of unknown friction and parameter uncertainties in the robot dynamics. Comparison is made to a previous sliding mode control approach, used to control UR5 [39]. The comparison results show a smaller tracking error value as compared to the sliding mode control approach used in [39] to control UR5.…”

Section: Introductionmentioning

confidence: 97%

“…Comparison is made to a previous sliding mode control approach, used to control UR5 [39]. The comparison results show a smaller tracking error value as compared to the sliding mode control approach used in [39] to control UR5. In summary, the contributions of the proposed approach over approaches in the literature are…”

Section: Introductionmentioning

confidence: 97%

“…In this paper, the sample time considered for the inverse kinematic solver and the controller are both equal to 1 msec. Different from the sliding mode controller, which was designed for UR5 in [39], the control signal designed in this paper does not include angular acceleration, which makes it easier to implement. The desired behavior of the industrial robot is defined in terms of a sliding mode, and the parameter update rules for the fuzzy system in the SMFC are derived from Lyapunov stability theorem.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Robust Sliding Mode Fuzzy Control of Industrial Robots Using an Extended Kalman Filter Inverse Kinematic Solver

Khanesar

Branson

2022

Energies

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This paper presents a sliding mode fuzzy control approach for industrial robots at their static and near static speed (linear velocities less than 5 cm/s). The extended Kalman filter with its covariance resetting is used to translate the coordinates from Cartesian to joint angle space. The translated joint angles are then used as a reference signal to control the industrial robot dynamics using a sliding mode fuzzy controller. The stability and robustness of the proposed controller is proven using an appropriate Lyapunov function in the presence of parameter uncertainty and unknown dynamic friction. The proposed controller is simulated on a 6-DOF industrial robot, namely the Universal Robot-UR5, considering the maximum allowable joint torques. It is observed that the proposed controller can successfully control UR5 under uncertainties in terms of unknown dynamic friction and parameter uncertainties. The tracking performance of the proposed controller is compared with that of the sliding mode control approach. The simulation results demonstrate superior performance of the proposed approach over the sliding mode control method in the presence of uncertainties.

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Section: Parametermentioning

confidence: 99%

Section: Parametermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Robust Sliding Mode Fuzzy Control of Industrial Robots Using an Extended Kalman Filter Inverse Kinematic Solver

Khanesar

Branson

2022

Energies

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Model‐based robust control design and experimental validation of collaborative industrial robot system with uncertainty

Zhen

Zhang

Liu

et al. 2022

Asian Journal of Control

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Based on the traditional PID control and robust control algorithm, a novel practical robust control method is designed for the 6-DOF collaborative industrial robot with uncertainty. The proposed algorithm consists of a robust term and a model-based PD control term, which we call MPDP controller. It is demonstrated by Lyapunov theoretical analysis that the algorithm is able to guarantee uniform boundedness and uniform ultimate boundedness of the system. Simulations and experiments show the good performance of MPDP control in a robot with smaller steady-state tracking errors and better robustness compared to PID controllers.

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Advanced robust control design and experimental verification for trajectory tracking of model-based uncertain collaborative robots

Zhen,

Li,

Liu

et al. 2023

Meas. Sci. Technol.

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At the core of this research is the pursuit of enhancing the trajectory tracking performance of six-degree-of-freedom collaborative robots, with a particular focus on addressing the challenges posed by uncertainties in real-world applications. One of the primary issues encountered with existing methods is the susceptibility of trajectory tracking to uncertainties, which can significantly hinder the performance of robotic systems. To address these challenges, we propose an advanced control method, known as the model-based proportional-derivative controller, or MPDP controller for short, which represents an innovative fusion of model-based PD control principles with a robust control algorithm. This amalgamation is driven by the need to mitigate the impact of uncertainties and external disturbances on trajectory tracking. A comprehensive assessment grounded in Lyapunov theory has been undertaken to validate the effectiveness of our approach. The analysis has firmly established that our method ensures not only the ultimate boundedness but also the uniform boundedness of the robotic system, which is critical for its operational stability. Both experimental and simulation studies have been meticulously conducted to benchmark the performance of the MPDP controller against the conventional proportional-integral-derivative controller, which serves as a widely adopted baseline in the field. The results unequivocally demonstrate the superiority of the MPDP controller across multiple dimensions. It exhibits exceptional robustness, resulting in a smaller steady-state tracking error, a critical advantage when addressing inherent uncertainties and external disturbances that can perturb the robot system. This translates to a more stable trajectory tracking performance. Furthermore, the MPDP controller empowers the robot with the capability to precisely follow predefined trajectories, thus ensuring high-precision and reliable execution of tasks. This feature significantly contributes to an overall enhancement of system performance and productivity.

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Trajectory Tracking Control of UR5 Robot: a PD with Gravity Compensation and Sliding Mode Control Comparison

Cited by 5 publications

References 6 publications

Robust Sliding Mode Fuzzy Control of Industrial Robots Using an Extended Kalman Filter Inverse Kinematic Solver

Robust Sliding Mode Fuzzy Control of Industrial Robots Using an Extended Kalman Filter Inverse Kinematic Solver

Model‐based robust control design and experimental validation of collaborative industrial robot system with uncertainty

Advanced robust control design and experimental verification for trajectory tracking of model-based uncertain collaborative robots

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