Extended Kalman Filter Based Linear Quadratic Regulator Control for Optical Wireless Communication Alignment

Alalwan, Asem; Tadjine, Mohamed; Chakir, Messaoud; Laleg, Taous Meriem

doi:10.1109/jphot.2020.3037223

Cited by 10 publications

(9 citation statements)

References 16 publications

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“…Standard EKF tool generally consists of two phases: prediction and updating. There are three covariance matrices: P, Q, and R [39]. The Q and R are both positive definite matrices which depended on the environment settings, and the P 0j0 is initialized as an identity matrix.…”

Section: Optical Channel Estimationmentioning

confidence: 99%

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Reinforcement Learning-Based Adaptive Switching Scheme for Hybrid Optical-Acoustic AUV Mobile Network

Luo

Wang

et al. 2022

Wireless Communications and Mobile Computing

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In an autonomous underwater vehicles– (AUVs–) based optical-acoustic hybrid network, it is critical to achieve ultra high-speed reliable communications, in order to reap the benefits of the complementary systems and perform high-bandwidth and low-latency operations. However, as the mobile AUVs operate in harsh oceanic environments, it is essential to design an effective switching algorithm to execute flexible hybrid acoustic-optical communications and increase the network throughput. In this paper, we propose a Q-learning-based adaptive switching scheme to maximize the network throughput by capturing the dynamics of the varying channels as well as the mobility of AUVs. In order to address the challenge associated with partial observations of the optical channel and improve the switching efficiency in extreme conditions, a blind optical channel estimation method is designed and implemented with the Extended Kalman Filter (EKF), in which the relationship between the underwater acoustic and optical channels is utilized to improve the channel prediction accuracy. Based on this environmental status, a reinforcement learning approach is leveraged to build a near-optimal switching strategy for the hybrid network. We conduct numerical simulations to verify the performance of the scheme, and the simulation results demonstrate that the proposed switching scheme is effective and robust.

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Section: Optical Channel Estimationmentioning

confidence: 99%

“…The Q and R are both positive definite matrices which depended on the environment settings, and the P 0j0 is initialized as an identity matrix. The state vector and its covariance matrix can be iteratively updated by the following relations [39]:…”

Section: Optical Channel Estimationmentioning

confidence: 99%

Reinforcement Learning-Based Adaptive Switching Scheme for Hybrid Optical-Acoustic AUV Mobile Network

Luo

Wang

et al. 2022

Wireless Communications and Mobile Computing

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“…Since the observation model is nonlinear, the observation matrix H t is represented by the jacobian matrix of function h as ∂h ∂X . The usage of the standard EKF tool to estimate the approximate relative position can be split into the following two processes: prediction and update [9]. Prediction:…”

Section: ) Position Localizationmentioning

confidence: 99%

Reliable Water-Air Direct Wireless Communication: Kalman Filter-Assisted Deep Reinforcement Learning Approach

Wang

Luo

Ruby

et al. 2022

2022 IEEE 47th Conference on Local Computer Networks (LCN)

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Optical wireless communication (OWC) is an emerging technology for direct communication through the water-air interface. However, due to the high directionality of optical beams and the harsh oceanic environment, it faces significant challenges to achieve the alignment and preserve the link availability, as the waves cause beam deflection and the mobility of the transceivers makes the link worse. To tackle these challenges and achieve reliable optical communication between autonomous underwater vehicles and unmanned aerial vehicles, we propose a deep reinforcement learning algorithm assisted by an extended Kalman filter to solve the alignment issue. To improve the reliability of communication, we present an algorithm to obtain the optimal beam divergence angle to maximize the link availability. The numerical simulations demonstrate that the proposed scheme achieves better performance in terms of energy consumption and alignment accuracy, and the link availability is increased by 25% compared to that without adjustment.

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“…Xin et al have implemented the system's controller with reverse dynamic control to track the path of a four-legged mobile robot reliably; in this way, the current status of the robot is rapidly updated, and the desired values have been sent to the actuators by minimizing the cost function of LQR controller [7]. In another study, they have used the LQR controller with the Extended Kalman Filter Estimator method for underwater mobile robot positioning problems and proposed a method to determine it accurately [8]. Trimp et al have suggested a method for extracting C The Author(s), 2022.…”

Section: Introductionmentioning

confidence: 99%

Regulation of cost function weighting matrices in control of WMR using MLP neural networks

Korayem

Adriani²,

Lademakhi³

2022

Robotica

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In this paper, a method based on neural networks for intelligently extracting weighting matrices of the optimal controllers’ cost function is presented. Despite the optimal and robust performance of controllers with the cost function, adjusting their gains which are the weighting matrices for the system state variables vector and the system inputs vector, is a challenging and time-consuming task that is usually selected by trial and error method for each specific application; and even little changes in the weighting matrices significantly impact problem-solving and system optimization. Therefore, it is necessary to select these gains automatically to improve controller performance and delete human energy to find the best gains. As a linear controller, linear quadratic regulator, and as a nonlinear controller, nonlinear model predictive control have been employed with trained networks to track the path of a wheeled mobile robot. The simulation and experimental results have been extracted and compared to validate the proposed method. These results have been demonstrated that the intelligent controller’s operation has lower error than the conventional method, which works up to 7% optimal in tracking and up to 19% better in angle state error; furthermore, as the most important aim, the required time and effort to find the weighting matrices in various situations has been omitted.

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Extended Kalman Filter Based Linear Quadratic Regulator Control for Optical Wireless Communication Alignment

Cited by 10 publications

References 16 publications

Reinforcement Learning-Based Adaptive Switching Scheme for Hybrid Optical-Acoustic AUV Mobile Network

Reinforcement Learning-Based Adaptive Switching Scheme for Hybrid Optical-Acoustic AUV Mobile Network

Reliable Water-Air Direct Wireless Communication: Kalman Filter-Assisted Deep Reinforcement Learning Approach

Regulation of cost function weighting matrices in control of WMR using MLP neural networks

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