Remarks on Control of a Robot Manipulator using a Quaternion Recurrent Neural-Network-Based Compensator

Takahashi, Kazuhiko; Watanabe, Lisa; Yamasaki, Hidemi; Hiraoka, Satoka; Hashimoto, Masafumi

doi:10.1109/anzcc50923.2020.9318414

Cited by 6 publications

(2 citation statements)

References 6 publications

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“…A servo-level controller using a high-dimensional NN based on quaternion numbers was proposed in our previous work. Its characteristics for controlling nonlinear dynamical systems were investigated as a control system application of quaternion NNs (QNNs) [13][14][15]. Moreover, we found that the QNN outperformed the other networks in the learning and control of a three-link robot manipulator [16], such as real-and hypercomplexvalued NNs.…”

Section: Introductionmentioning

confidence: 99%

Feedforward–Feedback Controller Based on a Trained Quaternion Neural Network Using a Generalised HR Calculus with Application to Trajectory Control of a Three-Link Robot Manipulator

2022

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This study derives a learning algorithm for a quaternion neural network using the steepest descent method extended to quaternion numbers. This applies the generalised Hamiltonian–Real calculus to obtain derivatives of a real–valued cost function concerning quaternion variables and designs a feedback–feedforward controller as a control system application using such a network. The quaternion neural network is trained in real-time by introducing a feedback error learning framework to the controller. Thus, the quaternion neural network-based controller functions as an adaptive-type controller. The designed controller is applied to the control problem of a three-link robot manipulator, with the control task of making the robot manipulator’s end effector follow a desired trajectory in the Cartesian space. Computational experiments are conducted to investigate the learning capability and the characteristics of the quaternion neural network used in the controller. The experimental results confirm the feasibility of using the derived learning algorithm based on the generalised Hamiltonian–Real calculus to train the quaternion neural network and the availability of such a network for a control systems application.

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Section: Introductionmentioning

confidence: 99%

Feedforward–Feedback Controller Based on a Trained Quaternion Neural Network Using a Generalised HR Calculus with Application to Trajectory Control of a Three-Link Robot Manipulator

2022

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“…A quaternion neural network (QNN) is aware of the internal relationship among the components in 3-D as well as the external relationship between the entities. QNNs have found many applications [5] such as color image processing by associating RGB channels with one another [6]- [8], robot-kinematic model construction [9], [10], controller design [11], [12], polarimetric synthetic aperture radar (PolSAR) land classification [13], [14], and so forth. On the other hand, complex-valued neural networks (CVNNs) are excellent in learning the wireless channel characteristics consisting of amplitude and phase for channel prediction [15], [16].…”

Section: Introductionmentioning

confidence: 99%

Polarization-Aware Prediction of Mobile Radio Wave Propagation Based on Complex-Valued and Quaternion Neural Networks

2022

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It is important to predict the characteristics of radio waves propagating in wireless environment for compensating information loss caused by fading effects. Though fading is often analyzed as the changes of intrinsic electric-field phasor describing a communication channel, it is also attributed greatly to its polarization state (PS). In this paper, we propose PS and phasor prediction methods based on quaternion neural networks (QNNs) and complex-valued neural networks (CVNNs). Quaternion is a hypercomplex number system for representing three dimensional (3-D) spatial rotation and orientation. It realizes adaptive prediction more stably and effectively than a real-valued rotation matrix. The time trajectories of PS are expressed as 3-D rotational motions on the Poincare sphere. We show that QNNs learn the spatiotemporal movements of the PS with high generalization ability and thus provide a superior prediction performance. In addition, CVNNs are efficient predictors for processing complex-valued signals such as the phasor of an incident wave. We evaluate the precision of the proposed QNN-CVNN prediction scheme in channel equalization in a time-division-duplex (TDD) orthogonal-frequency-division-multiplexing (OFDM) communication system. In physical wireless-environment experiments, we find that the proposed scheme provides 3.0 dB and 6.0 dB improvements of bit error rate (BER) at 10 −4 , showing almost the same BER performance as a system perfectly knowing the actual radio-wave characteristics in future TDD frames. Accordingly, this work also reveals that PS needs to be considered as one of the significant characteristics in various radio-wave systems in the near-future technology.

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Remarks on System Identification Using a Quaternion Recurrent Neural Network Trained by Backpropagation through Time

Takahashi

Shibata

Hashimoto

2021

2021 Australian &Amp; New Zealand Control Conference (ANZCC)

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Remarks on Control of a Robot Manipulator using a Quaternion Recurrent Neural-Network-Based Compensator

Cited by 6 publications

References 6 publications

Feedforward–Feedback Controller Based on a Trained Quaternion Neural Network Using a Generalised HR Calculus with Application to Trajectory Control of a Three-Link Robot Manipulator

Feedforward–Feedback Controller Based on a Trained Quaternion Neural Network Using a Generalised HR Calculus with Application to Trajectory Control of a Three-Link Robot Manipulator

Polarization-Aware Prediction of Mobile Radio Wave Propagation Based on Complex-Valued and Quaternion Neural Networks

Remarks on System Identification Using a Quaternion Recurrent Neural Network Trained by Backpropagation through Time

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