Trajectory Tracking of Robotic Manipulators with Constraints Based on Model Predictive Control

Tang, Qirong; Chu, Zhugang; Yu, Qiang; Wu, Shun; Zhou, Zheng

doi:10.1109/ur49135.2020.9144943

Cited by 6 publications

(3 citation statements)

References 10 publications

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“…Based on the sampled system state information and the control inputs that will be optimized, the predicted position states x1 (k + j + 1) will be obtained via the ReLU-RNN predictive model (17). Then, the fitness function ( 29) is computed, and the system control inputs can be optimized via the DEO algorithm.…”

Section: Rnn and Deo Based Nmpc Controllermentioning

confidence: 99%

“…Unlike the model-free method, model predictive control (MPC) as a primary model-dependent control method is an efficient controller to handle the performance requirements. For example, in [17,18], the MPC controller has been utilized for robot manipulator trajectory tracking. For high precision position tracking of the robot arm, a data-driven MPC method has been proposed which has shown performance improvement compared to the PID method [19].…”

Section: Introductionmentioning

confidence: 99%

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Nonlinear Model Predictive Control of Single-Link Flexible-Joint Robot Using Recurrent Neural Network and Differential Evolution Optimization

et al. 2021

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A recurrent neural network (RNN) and differential evolution optimization (DEO) based nonlinear model predictive control (NMPC) technique is proposed for position control of a single-link flexible-joint (FJ) robot. First, a simple three-layer recurrent neural network with rectified linear units as an activation function (ReLU-RNN) is employed for approximating the system dynamic model. Then, using the RNN predictive model and model predictive control (MPC) scheme, an RNN and DEO based NMPC controller is designed, and the DEO algorithm is used to solve the controller. Finally, comparing numerical simulation findings demonstrates the efficiency and performance of the proposed approach. The merit of this method is that not only is the control precision satisfied, but also the overshoots and the residual vibration are well suppressed.

show abstract

Section: Rnn and Deo Based Nmpc Controllermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Nonlinear Model Predictive Control of Single-Link Flexible-Joint Robot Using Recurrent Neural Network and Differential Evolution Optimization

et al. 2021

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“…MPC discovered practices in dynamic and unpredictable environments such as chemical plants and oil refineries [29], as well as power system balancing [30]. More recently, the MPC has also begun to find applications in the control of autonomous vehicles [31] and robotic trajectory control [32][33][34]. In 2016, Best et al applied MPC to robot joint control.…”

Section: Introductionmentioning

confidence: 99%

Trajectory Control of An Articulated Robot Based on Direct Reinforcement Learning

et al. 2022

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Reinforcement Learning (RL) is gaining much research attention because it allows the system to learn from interacting with the environment. Yet, with all these successful applications, the application of RL in direct joint torque control without the help of an underlining dynamic model is not reported in the literature. This study presents a split network structure that enables successful training of RL to learn the direct torque control for trajectory following a six-axis articulated robot without prior knowledge of the dynamic robot model. The training took a very long time to converge. However, we were able to show the successful control of four different trajectories without needing an accurate dynamics model and complex inverse kinematics computation. To show the RL-based control’s effectiveness, we also compare the RL control with the Model Predictive Control (MPC), another popular trajectory control method. Our results show that while the MPC achieves smoother and more accurate control, it does not automatically treat the singularity. In addition, it requires complex inverse dynamics calculations. On the other hand, the RL controller instinctively avoided the violent action around the singularities.

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Development of a compact rotary series elastic actuator with neural network-driven model predictive control implementation

Zhang,

Lin,

Wang

et al. 2024

Intel Serv Robotics

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Trajectory Tracking of Robotic Manipulators with Constraints Based on Model Predictive Control

Cited by 6 publications

References 10 publications

Nonlinear Model Predictive Control of Single-Link Flexible-Joint Robot Using Recurrent Neural Network and Differential Evolution Optimization

Nonlinear Model Predictive Control of Single-Link Flexible-Joint Robot Using Recurrent Neural Network and Differential Evolution Optimization

Trajectory Control of An Articulated Robot Based on Direct Reinforcement Learning

Development of a compact rotary series elastic actuator with neural network-driven model predictive control implementation

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