Nonlinear Control System with Reinforcement Learning and Neural Networks Based Lyapunov Functions

Rego, Rosana C. B.; Araújo, Fábio Meneghetti Ugulino de

doi:10.1109/tla.2021.9475855

Cited by 15 publications

(8 citation statements)

References 28 publications

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“…For backstep method, each order corresponds to a virtual controller. The higher the order, the more complicated the controller design process [4,5].…”

Section: Introductionmentioning

confidence: 99%

“…In [38,39], a decentralized controller based on Lyapunov theory for excitation control of multi-machine power systems ensured global asymptotic stability and realized voltage regulation through excitation control. In [40,41], For the multi-electromechanical power system including largescale photovoltaic energy storage power station, the multi-objective holographic feedback control method is adopted. Through the test of the IEEE 6 machine 24-node system, the control method can effectively suppress the voltage and frequency oscillation under the most serious fault of the power system during the three-phase short circuit, and can quickly recover to the stable rated operating state after the short circuit is removed.…”

Section: Introductionmentioning

confidence: 99%

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Research on robust adaptive control of strong nonlinear complex large power grids

Huang,

Chen,

Zhang

et al. 2024

PLoS ONE

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In this paper, a Multi-Index Nonlinear Robust Adaptive Control (MINRAC) method was proposed for ultra-complex multi-input multi-output power systems with uncertain parameter factors and external disturbances. The controller designed by this method has excellent static and dynamic characteristics. Under the condition of the uncertainty of system parameters and external disturbance at the same time, the MINRAC method can ensure that the multiple indexes concerned by ultra-complex power systems can be controlled at their expected values. The simulation results showed that the control mechanism of MINRAC method was consistent in both single-machine infinite bus system and multi-machine interconnected coupling system. The output function chooses power angle, angular frequency and terminal voltage as constraints. When the system has parameter uncertainty and external interference, the uncertain parameter values are adjusted by adaptive control to force these indicators to tend to the given expected value. For three-phase short circuit, which is the most serious fault in power system, the use of multi index nonlinear robust controller can ensure that the system is stable in a wide range and has better dynamic performance.

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“…For backstep method, each order corresponds to a virtual controller. The higher the order, the more complicated the controller design process [4,5].…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research on robust adaptive control of strong nonlinear complex large power grids

Huang,

Chen,

Zhang

et al. 2024

PLoS ONE

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show abstract

“…In recent years, there have been many approaches to non‐linear system, such as robust adaptive backstepping control [14], adaptive fuzzy tracking control [15], reinforcement learning, and neural networks control [16], etc. Besides, many important works on non‐linear analysis and modelling of EHPS have become available.…”

Section: Introductionmentioning

confidence: 99%

“…shown in Figures15 to 18, where the desired trajectory steering angles of step response test(15), sinusoid test(16), double lane change test(17), and sine with dwell test(18) are generated by computer according to ISO standards. Experimental studies using the sinusoid condition and double lane change condition are representative of the vehicle handling operation experiment condition, which can represent most of the realistic driving conditions.Figures 15-18 have illustrated the front wheel angle-tracking performance of the MPC method and the PID method.…”

mentioning

confidence: 99%

Front wheel angle control for steering system of intelligent commercial vehicle based on model predictive control

Yang

2023

IET Intelligent Trans Sys

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Accurate control of front wheel angle is essential in intelligent vehicle electric‐hydraulic power steering (EHPS) system, because it has a significant impact on the safety, comfort, and reliability of vehicle steering. However, the accurate control of front wheel angle is a challenging problem due to the non‐linearity of hydraulic power in steering system and the uncertainty of steering resistance. In this paper, a model predictive control (MPC) method for front wheel angle based on steering resistance estimation is proposed. Firstly, through theoretical analysis and practical experiment of EHPS system model, the relationship between front wheel angle and steering wheel angle and the non‐linear characteristics of hydraulic power are obtained. Secondly, a sliding mode observer is established to estimate the front axle lateral tyre force and steering resistance, which is verified by MATLAB/TruckSim co‐simulation. Then a controller based on the MPC theory is designed and applied to the accurate control unit of front wheel angle in the EHPS system for real‐time control. Finally, the simulation and test platform results show that the control algorithm proposed in this paper can precisely control the vehicle's front wheel turning angle within a certain frequency range.

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“…Rego deals with the problem of finding the control Lyapunov function that keeps the system stable. To find the Lyapunov function, this paper proposes the use of reinforcement learning with two neural networks based on the Lyapunov stability theory [18]. Nobahari focuses on developing a nonlinear controller based on the convolutional neural networks to control different plants.…”

Section: Introductionmentioning

confidence: 99%

Adaptive PID Control and Its Application Based on a Double-Layer BP Neural Network

Zhang

et al. 2021

Processes

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In this paper, focusing on the inconvenience of variable value PID based on manual parameter adjustment for the hydraulic drive unit (HDU) of a legged robot, a method employing double-layer back propagation (BP) neural networks for learning the law of PID control parameters is proposed. The first layer is used to learn the relationship between different control parameters and the control performance of the system under various working conditions. The second layer is used to study the relationship between the parameters of the working conditions and the optimizing control parameters under various working conditions. The effectiveness of the proposed control method was verified by simulation and experiment. The results showed that the proposed method can provide a theoretical and experimental basis for the selection of control parameters, and can be extended to similar controllers, therefore possessing engineering application value.

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Nonlinear Control System with Reinforcement Learning and Neural Networks Based Lyapunov Functions

Cited by 15 publications

References 28 publications

Research on robust adaptive control of strong nonlinear complex large power grids

Research on robust adaptive control of strong nonlinear complex large power grids

Front wheel angle control for steering system of intelligent commercial vehicle based on model predictive control

Adaptive PID Control and Its Application Based on a Double-Layer BP Neural Network

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