Neural Identifier-Control Scheme for Nonlinear Discrete Systems with Input Delay

Rios, Jorge D.; Alanís, Alma Y.; Arana‐Daniel, Nancy; López-Franco, Carlos

doi:10.1007/978-3-319-67137-6_26

Cited by 1 publication

(2 citation statements)

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“…Each plant is modeled using a neural identifier consisting of a recurrent high order neural network (RHONN) trained with an extended Kalman filter (EKF). A similar approach can be seen in [11][12][13][14]. All the above-mentioned works have presented results of simulation stage, or at most, they have achieved to execute parts of the navigation system (some algorithms, such as path planners, controllers, or image processing methods) on personal computers, and they send the outputs of the computer-executed-algorithms to the robot via Wi-Fi communication, which makes these systems highly susceptible to delays and most of the time impossible to be executed in real-time on robot platforms.…”

Section: Introductionmentioning

confidence: 88%

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An Autonomous Path Controller in a System on Chip for Shrimp Robot

et al. 2020

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This paper presents a path planning and trajectory tracking system for a BlueBotics Shrimp III®, which is an articulate mobile robot for rough terrain navigation. The system includes a decentralized neural inverse optimal controller, an inverse kinematic model, and a path-planning algorithm. The motor control is obtained based on a discrete-time recurrent high order neural network trained with an extended Kalman filter, and an inverse optimal controller designed without solving the Hamilton Jacobi Bellman equation. To operate the whole system in a real-time application, a Xilinx Zynq® System on Chip (SoC) is used. This implementation allows for a good performance and fast calculations in real-time, in a way that the robot can explore and navigate autonomously in unstructured environments. Therefore, this paper presents the design and implementation of a real-time system for robot navigation that integrates, in a Xilinx Zynq® System on Chip, algorithms of neural control, image processing, path planning, and inverse kinematics and trajectory tracking.

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Section: Introductionmentioning

confidence: 88%

“…ζ Q is positive. Equilibrium point x k = 0 of System (1) is globally exponentially stabilized by the control law in Equation (11). Additionally, this control law is inverse optimal since it minimizes cost function in Equation (2) with l (x k ) = −V.…”

Section: Inverse Optimal Controllermentioning

confidence: 99%