Research on Horizontal Path Tracking Control of a Biomimetic Robotic Fish

Liu, Jinhao; Song, Xiaoru; Gao, Song; Chen, Chaobo; Liu, Kang; Li, Tonghui

doi:10.1109/icmee56406.2022.10093562

Cited by 3 publications

(1 citation statement)

References 14 publications

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“…For linear systems, for example, methods of modal control [15] can be applied, as well as such analytical methods such as backstepping [16,17] or synthesis based on the application of the Lyapunov function [18]. In practice, stability is ensured through linearization of the model (1) in the terminal state and setting PI or PID controllers in control channels [19,20]. All known analytical and technical methods have their limitations, which mostly depend on the type of the model used to describe the control object.…”

Section: Methods Of Solvingmentioning

confidence: 99%

Adaptive Synthesized Control for Solving the Optimal Control Problem

Diveev,

Shmalko

2023

Mathematics

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The development of artificial intelligence systems assumes that a machine can independently generate an algorithm of actions or a control system to solve the tasks. To do this, the machine must have a formal description of the problem and possess computational methods for solving it. This article deals with the problem of optimal control, which is the main task in the development of control systems, insofar as all systems being developed must be optimal from the point of view of a certain criterion. However, there are certain difficulties in implementing the resulting optimal control modes. This paper considers an extended formulation of the optimal control problem, which implies the creation of such systems that would have the necessary properties for its practical implementation. To solve it, an adaptive synthesized optimal control approach based on the use of numerical methods of machine learning is proposed. Such control moves the control object, optimally changing the position of the stable equilibrium point in the presence of some initial position uncertainty. As a result, from all possible synthesized controls, one is chosen that is less sensitive to changes in the initial state. As an example, the optimal control problem of a quadcopter with complex phase constraints is considered. To solve this problem, according to the proposed approach, the control synthesis problem is firstly solved to obtain a stable equilibrium point in the state space using a machine learning method of symbolic regression. After that, optimal positions of the stable equilibrium point are searched using a particle swarm optimization algorithm using the source functional from the initial optimal control problem statement. It is shown that such an approach allows for generating the control system automatically by computer, basing this on the formal statement of the problem and then directly implementing it onboard as far as the stabilization system has already been introduced.

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Section: Methods Of Solvingmentioning

confidence: 99%

Adaptive Synthesized Control for Solving the Optimal Control Problem

Diveev,

Shmalko

2023

Mathematics

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Bionic Fishtail-Shaped Proprioceptive Sensing Technology Based on Triboelectric Nanogenerator

Liu,

Xu,

et al. 2023

2023 China Automation Congress (CAC)

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Adaptive Synthesized Control for Solving The Optimal Control Problem

Diveev,

Shmalko

2023

Preprint

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The development of artificial intelligence systems assumes that a machine can independently generate an algorithm of actions or a control system to solve the tasks. To do this, the machine must have a formal description of the problem and possess computational methods for solving it. The article deals with the problem of optimal control, which is the main task in the development of control systems, insofar as all systems being developed must be optimal from the point of view of a certain criterion. However, there are certain difficulties in implementing the resulting optimal control modes. The paper considers an extended formulation of the optimal control problem, which implies the creation of such systems that would have the necessary properties for its practical implementation. To solve it, an adaptive synthesized optimal control approach based on the use of numerical methods of machine learning is proposed. The method moves the control object, optimally changing the position of the stable equilibrium point in the presence of some initial position uncertainty. As a result, from all possible synthesized controls, he chooses one that is less sensitive to changes in the initial states. As an example, the optimal control problem of quadcopter with complex phase constraints is considered. To solve this problem? according to the proposed approach, the control synthesis problem is firstly solved to obtain a stable equilibrium point in the state space by a machine learning method of symbolic regression. After that optimal positions of the stable equilibrium point are searched according to source functional from the optimal control problem by particle swarm optimization algorithm. It is shown that such approach allows generating the control system automatically by computer basing on the formal statement of the problem and then directly implementing it onboard as far as they have already had a stabilization system inserted.

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Research on Horizontal Path Tracking Control of a Biomimetic Robotic Fish

Cited by 3 publications

References 14 publications

Adaptive Synthesized Control for Solving the Optimal Control Problem

Adaptive Synthesized Control for Solving the Optimal Control Problem

Bionic Fishtail-Shaped Proprioceptive Sensing Technology Based on Triboelectric Nanogenerator

Adaptive Synthesized Control for Solving The Optimal Control Problem

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