Neural Control of Mobile Robot Motion Based on Feedback Error Learning and Mimetic Structure

Noormohammadi-Asl, Ali; Saffari, Mohsen; Teshnehlab, Mohammad

doi:10.1109/icee.2018.8472657

Cited by 7 publications

(2 citation statements)

References 19 publications

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“…Adaptive neural control methods have also been investigated for humanoid robots [33,34]. Other proposed control frameworks that use general function approximators for linearizing controller design can be found in [35][36][37].…”

Section: Related Workmentioning

confidence: 99%

Industrial Robot Trajectory Tracking Control Using Multi-Layer Neural Networks Trained by Iterative Learning Control

Chen

Wen

2021

Robotics

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Fast and precise robot motion is needed in many industrial applications. Most industrial robot motion controllers allow externally commanded motion profiles, but the trajectory tracking performance is affected by the robot dynamics and joint servo controllers, to which users have no direct access and about which they have little information. The performance is further compromised by time delays in transmitting the external command as a setpoint to the inner control loop. This paper presents an approach for combining neural networks and iterative learning controls to improve the trajectory tracking performance for a multi-axis articulated industrial robot. For a given desired trajectory, the external command is iteratively refined using a high-fidelity dynamical simulator to compensate for the robot inner-loop dynamics. These desired trajectories and the corresponding refined input trajectories are then used to train multi-layer neural networks to emulate the dynamical inverse of the nonlinear inner-loop dynamics. We show that with a sufficiently rich training set, the trained neural networks generalize well to trajectories beyond the training set as tested in the simulator. In applying the trained neural networks to a physical robot, the tracking performance still improves but not as much as in the simulator. We show that transfer learning effectively bridges the gap between simulation and the physical robot. Finally, we test the trained neural networks on other robot models in simulation and demonstrate the possibility of a general purpose network. Development and evaluation of this methodology are based on the ABB IRB6640-180 industrial robot and ABB RobotStudio software packages.

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Section: Related Workmentioning

confidence: 99%

Industrial Robot Trajectory Tracking Control Using Multi-Layer Neural Networks Trained by Iterative Learning Control

Chen

Wen

2021

Robotics

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“…Specifically, the emergence of deep neural architectures has opened new research gates for the sake of achieving the utmost goal of ML, i.e., providing large generalization capacities and avoiding overfitting on the training datasets. Shallow and Deep NNs are widely employed for many aspects of contemporary applications such as fingerprint presentation attack detection [ 1 ], sequential modelling of multi-scale energy time series [ 2 ] and mobile robot motion control [ 3 ] etc. due to large computational power, handling uncertainty factors, and efficient implementation.…”

Section: Introductionmentioning

confidence: 99%

Maximum Relevance Minimum Redundancy Dropout with Informative Kernel Determinantal Point Process

Saffari

Khodayar

Ebrahimi

et al. 2021

Sensors

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In recent years, deep neural networks have shown significant progress in computer vision due to their large generalization capacity; however, the overfitting problem ubiquitously threatens the learning process of these highly nonlinear architectures. Dropout is a recent solution to mitigate overfitting that has witnessed significant success in various classification applications. Recently, many efforts have been made to improve the Standard dropout using an unsupervised merit-based semantic selection of neurons in the latent space. However, these studies do not consider the task-relevant information quality and quantity and the diversity of the latent kernels. To solve the challenge of dropping less informative neurons in deep learning, we propose an efficient end-to-end dropout algorithm that selects the most informative neurons with the highest correlation with the target output considering the sparsity in its selection procedure. First, to promote activation diversity, we devise an approach to select the most diverse set of neurons by making use of determinantal point process (DPP) sampling. Furthermore, to incorporate task specificity into deep latent features, a mutual information (MI)-based merit function is developed. Leveraging the proposed MI with DPP sampling, we introduce the novel DPPMI dropout that adaptively adjusts the retention rate of neurons based on their contribution to the neural network task. Empirical studies on real-world classification benchmarks including, MNIST, SVHN, CIFAR10, CIFAR100, demonstrate the superiority of our proposed method over recent state-of-the-art dropout algorithms in the literature.

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Deep recurrent extreme learning machine for behind-the-meter photovoltaic disaggregation

Saffari

Khodayar

2022

The Electricity Journal

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Neural Control of Mobile Robot Motion Based on Feedback Error Learning and Mimetic Structure

Cited by 7 publications

References 19 publications

Industrial Robot Trajectory Tracking Control Using Multi-Layer Neural Networks Trained by Iterative Learning Control

Industrial Robot Trajectory Tracking Control Using Multi-Layer Neural Networks Trained by Iterative Learning Control

Maximum Relevance Minimum Redundancy Dropout with Informative Kernel Determinantal Point Process

Deep recurrent extreme learning machine for behind-the-meter photovoltaic disaggregation

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