State estimation of nonlinear dynamic system using novel heuristic filter based on genetic algorithm

Zandavi, Seid Miad; Chung, Vera

doi:10.1007/s00500-018-3213-y

Cited by 12 publications

(8 citation statements)

References 24 publications

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“…The UKF involves a sample-based method, which is explained in Section 2.2. In addition, a few examples of sample-based filters are heuristic filters, such as simplex [33] and genetic [34] filters. In this study, we did not focus on heuristic filters, but we discuss how a skeleton system can be incorporated in detail.…”

Section: Related Workmentioning

confidence: 99%

State Estimation Using a Randomized Unscented Kalman Filter for 3D Skeleton Posture

Musunuri

Kwon

2021

Electronics

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In this study, we propose a method for minimizing the noise of Kinect sensors for 3D skeleton estimation. Notably, it is difficult to effectively remove nonlinear noise when estimating 3D skeleton posture; however, the proposed randomized unscented Kalman filter reduces the nonlinear temporal noise effectively through the state estimation process. The 3D skeleton data can then be estimated at each step by iteratively passing the posterior state during the propagation and updating process. Ultimately, the performance of the proposed method for 3D skeleton estimation is observed to be superior to that of conventional methods based on experimental results.

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Section: Related Workmentioning

confidence: 99%

State Estimation Using a Randomized Unscented Kalman Filter for 3D Skeleton Posture

Musunuri

Kwon

2021

Electronics

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“…T is the angular acceleration disturbance corresponded to propeller angular speed, and these acceleration disturbances are modeled by Eq (14).…”

Section: Dynamic Modelmentioning

confidence: 99%

“…In addition to the control, integrated estimation of states and parameters plays an important role in improving the performance of the quadcopter in the presence of uncertainties and measurement noise. Two different categories of classical and heuristic [14] filters have been used to address the state estimation problem. For example, Kalman Filter (KF) [15], Extended Kalman Filter (EKF) [16], Unscented Kalman Filter (UKF) [17] are classical filters, and Particle Filter (PF) [18], Simplex Filter (SF) [19] and Genetic Filter (GF) [14] are introduced as heuristic filters [14].…”

Section: Introductionmentioning

confidence: 99%

“…Two different categories of classical and heuristic [14] filters have been used to address the state estimation problem. For example, Kalman Filter (KF) [15], Extended Kalman Filter (EKF) [16], Unscented Kalman Filter (UKF) [17] are classical filters, and Particle Filter (PF) [18], Simplex Filter (SF) [19] and Genetic Filter (GF) [14] are introduced as heuristic filters [14].…”

Section: Introductionmentioning

confidence: 99%

“…For example, SF [19] utilizes Nelder-Mead simplex approach for state estimation. GF [14] was utilized a genetic algorithm scheme, and its operators to estimate the state of dynamic systems.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

PIDA: Smooth and Stable Flight Using Stochastic Dual Simplex Algorithm and Genetic Filter

Zandavi,

Chung,

Anaissi

2020

Preprint

Self Cite

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This paper presents a new Proportional-Integral-Derivative-Accelerated (PIDA) control with a derivative filter to improve quadcopter flight stability in a noisy environment. The mathematical model is derived from having an accurate model with a high level of fidelity by addressing the problems of non-linearity, uncertainties, and coupling. These uncertainties and measurement noises cause instability in flight and automatic hovering. The proposed controller associated with a heuristic Genetic Filter (GF) addresses these challenges. The tuning of the proposed PIDA controller associated with the objective of controlling is performed by Stochastic Dual Simplex Algorithm (SDSA). GF is applied to the PIDA control to estimate the observed states and parameters of quadcopters in both attitude and altitude. The simulation results show that the proposed control associated with GF has a strong ability to track the desired point in the presence of disturbances.

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Improved robust filter synthesis combining linear matrix inequalities and heuristic algorithms

Souza

Agulhari

Goedtel

et al. 2022

Optim Control Appl Methods

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This article introduces an alternative and less conservative  ∞ filter design procedure applied to continuous time uncertain systems. In order to provide a reduction in conservatism and an  ∞ norm improvement, the proposed technique avoids the conventional convexification procedure-which uses structure restrictions and slack variables. Instead, it defines an unconstrained structure.The proposed technique consists of a hybrid procedure, combining the application of linear matrix inequalities (LMIs) with heuristic optimization algorithms (HOAs). The HOAs, specifically genetic algorithms, are used to optimize terms that could cause bilinearities, subsequently evaluated by LMIs and generating the desired filter. Numerical examples and a comparison with other methodologies are presented, testifying to the proposed method's efficacy and the performance.

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State estimation of nonlinear dynamic system using novel heuristic filter based on genetic algorithm

Cited by 12 publications

References 24 publications

State Estimation Using a Randomized Unscented Kalman Filter for 3D Skeleton Posture

State Estimation Using a Randomized Unscented Kalman Filter for 3D Skeleton Posture

PIDA: Smooth and Stable Flight Using Stochastic Dual Simplex Algorithm and Genetic Filter

Improved robust filter synthesis combining linear matrix inequalities and heuristic algorithms

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