The LQG Control Algorithms for Nonlinear Dynamic Systems

György, Katalin

doi:10.1016/j.promfg.2019.02.252

Cited by 6 publications

(3 citation statements)

References 3 publications

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“…Broad description Here, the landing procedure is performed automatically based on the autopilot which is a part of the integrated control system. It is based on both LQR (Topczewski et al, 2018) and LQG (György, 2019) algorithms to calculate control commands for performing all of the helicopter landing procedure phases. For estimating gains, both algorithms require linear model of the controlled object dynamics which was generated using global linearization of the helicopter nonlinear model developed in FLIGHTLAB software.…”

Section: Control Methodologymentioning

confidence: 99%

LQR and LQG control of the helicopter during landing on the ship deck

Topczewski

Bibik

2023

AEAT

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Purpose The purpose of this study is to test the performance of the designed automatic control system based on the Linear Quadratic Regulator (LQR) and Linear Quadratic Gaussian (LQG) algorithms during landing of the helicopter on the ship deck. This paper is a further development of the series based on Topczewski et al. (2020). Design/methodology/approach The system consists of two automatic control algorithms based on LQR and the LQG. It is integrated with the ship motion prediction system based on autoregressive algorithm with parameters calculated using Burg’s method. It is assumed that the source of necessary navigation data is integrated Inertial Navigation System with Global Positioning System. Landing of the helicopter on the ship deck is performed in automatic way, based on the preselected procedure. Performance of the control system is analyzed when all necessary navigation data is available for the system and in case when one of the parameters is unavailable during performing the procedure. Findings In this paper, description of the designed control system developed for performing the approach and landing of the helicopter using selected procedure is presented. Helicopter dynamic model is validated using the manufacturer data and by test pilots, overview is presented. Necessary information about ship motion model is also included. Tests showing mission performance while using LQR and LQG algorithms applied to the control system are presented and analyzed, taking into account both situations when full navigation data is available/unavailable for the control system. Practical implications Results of the system performance analyses can be used for selection of the proper control methodology for prospective helicopters autopilots. Furthermore, the system can be used to analyze the mission safety when information about one of the navigation parameters is identified by the navigation system as unavailable or incorrect and therefore unavailable during landing on the ship deck. Originality/value In this paper, control system dedicated for the automatic landing of the helicopter on the ship deck, based on two different control algorithms is presented. Influence of lack of information about one of the navigation parameters on the mission performance is analyzed.

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Section: Control Methodologymentioning

confidence: 99%

LQR and LQG control of the helicopter during landing on the ship deck

Topczewski

Bibik

2023

AEAT

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“…LQG is an optimal control scheme that is capable of taking care of Gaussian noise corrupted output measurements and can be used to control perturbed non-linear systems [35]. It is simple in design and easy to implement as it requires less number of design parameters.…”

Section: General Form Of Conventional Lqg and Modified Lqg Controller...mentioning

confidence: 99%

LQG-Based Virtual Inertial Control of Islanded Microgrid Load Frequency Control and DoS Attack Vulnerability Analysis

2023

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The load frequency control (LFC) in modern power system like microgrid has turned out to be significantly challenging due to the high penetration of renewable energy sources (RESs) and the consequent reduction of overall system inertia. The inverter-equipped RESs like solar and wind power generation units, besides the load variations can prompt sustained frequency fluctuations in microgrid and further lead to system instability, power outages, and even complete system blackout in the worst case. As a solution to the concerns of intermittent power source integration and resulting microgrid frequency instability, in this work, two robust LFC schemes using conventional linear quadratic gaussian (LQG) and modified LQG with linear quadratic integral (LQI) control schemes are proposed for secondary/battery energy storage system (BESS)-based auxiliary (virtual inertia (VI)) control of islanded/non-linear microgrid. The efficacy of the suggested control strategy is confirmed through MATLAB/SIMULINK simulations and by comparing with other different control schemes under various scenarios of distinct load and RES disturbance input profiles. The simulation results exhibited superior frequency regulation performance for the proposed control mechanisms over other types of control schemes. The proposed control schemes also ensure fast settling of frequency transients and help improve frequency stability under stochastic loads and random RES output power. In addition to the development of an effective robust controller, the vulnerability of the microgrid LFC system towards the denial of service (DoS) attack is analyzed for different control schemes. The vulnerability analysis is performed in the presence and absence of local auxiliary control loop and the remote secondary measurement communication channel is considered as the DoS attack point. The simulation results indicate that the local auxiliary control mechanism can not only help to improve frequency stability but also helps to add cyber-attack resilience to an extent when the secondary control loop is under attack.INDEX TERMS Linear quadratic Gaussian control, load frequency control, auxiliary control, virtual inertia control, denial of service attack.

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“…1. The Euler-Lagrange theory has been used to determine this mathematical model, where the inertia of the system has been neglected [9], [11], [12]. The nonlinear mathematical discrete time model is the following.…”

Section: Example and Numerical Simulationmentioning

confidence: 99%

Theoretical Study of the Unconstrained and Constrained Nonlinear Optimal Discrete Time State Feedback Control

György

Dávid

2021

Acta Universitatis Sapientiae, Electrical and Mechanical Engineering

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Majority of the optimal control techniques can only be applied successfully if the model of the controlled process is known and it is linear. If the system model is nonlinear, then this nonlinear model can be approximated with different simple, linear models. However, these models are valid only in the neighbourhood of the operating points. The success of the control algorithms is highly dependent on the used linearization methods. The aim of the paper is to compare different optimal control algorithms and linearization methods. The presented optimal control algorithms have been also tested in constrained and unconstrained versions.

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The LQG Control Algorithms for Nonlinear Dynamic Systems

Cited by 6 publications

References 3 publications

LQR and LQG control of the helicopter during landing on the ship deck

LQR and LQG control of the helicopter during landing on the ship deck

LQG-Based Virtual Inertial Control of Islanded Microgrid Load Frequency Control and DoS Attack Vulnerability Analysis

Theoretical Study of the Unconstrained and Constrained Nonlinear Optimal Discrete Time State Feedback Control

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