tracking problem
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Mathematics ◽  
2021 ◽  
Vol 9 (24) ◽  
pp. 3160
Author(s):  
Luis Arturo Soriano ◽  
José de Jesús Rubio ◽  
Eduardo Orozco ◽  
Daniel Andres Cordova ◽  
Genaro Ochoa ◽  
...  

Sliding mode control is a robust technique that is used to overcome difficulties such as parameter variations, unmodeled dynamics, external disturbances, and payload changes in the position-tracking problem regarding robots. However, the selection of the gains in the controller could produce bigger forces than are required to move the robots, which requires spending a large amount of energy. In the literature, several approaches were used to manage these features, but some proposals are complex and require tuning the gains. In this work, a sliding mode controller was designed and optimized in order to save energy in the position-tracking problem of a two-degree-of-freedom SCARA robot. The sliding mode controller gains were optimized usinga Bat algorithm to save energy by minimizing the forces. Finally, two controllers were designed and implemented in the simulation, and as a result, adequate controller gains were found that saved energy by minimizing the forces.


2021 ◽  
Vol 2085 (1) ◽  
pp. 012017
Author(s):  
Fan Lin ◽  
Xinjie Shen

Abstract The roll pitch seeker has a large field of view, which can achieve large off-axis angle attacks. At the same time, it has a simple structure and is easy to miniaturize, which is beneficial to the overall design of the missile. However, the over-tracking problem of the rolling seeker restricts her application in engineering. To solve this problem, this paper proposes a method to calculate the roll frame angle by using the angular rate of the projectile line of sight when the pitch frame angle is small. The simulation results show that this method is effective in the overhead tracking control.


2021 ◽  
Vol 2123 (1) ◽  
pp. 012014
Author(s):  
Firman

Abstract We present an output tracking problem for a non-minimum phase nonlinear system. In this paper, the input control design to solve the output tracking problem is to use the input output linearization method. The use of the input output linearization method cannot be initiated from output causing the system to be non-minimum phase. Therefore the output of the system will be redefined such that the system will become minimum phase with respect to a new output.


2021 ◽  
pp. 1-17
Author(s):  
Codrut Florin Ivascu

Index tracking is one of the most popular passive strategy in portfolio management. However, due to some practical constrains, a full replication is difficult to obtain. Many mathematical models have failed to generate good results for partial replicated portfolios, but in the last years a data driven approach began to take shape. This paper proposes three heuristic methods for both selection and allocation of the most informative stocks in an index tracking problem, respectively XGBoost, Random Forest and LASSO with stability selection. Among those, latest deep autoencoders have also been tested. All selected algorithms have outperformed the benchmarks in terms of tracking error. The empirical study has been conducted on one of the biggest financial indices in terms of number of components in three different countries, respectively Russell 1000 for the USA, FTSE 350 for the UK, and Nikkei 225 for Japan.


2021 ◽  
Vol 27 (8) ◽  
pp. 409-418
Author(s):  
A. D. Grigorev ◽  
◽  
A. N. Gneushev ◽  

The paper considers multiple object tracking. Existing methods tend to be either resource-intensive or prone to high object densities errors failing to provide competitive performance at high frame rates without significant tracking disruptions and error accumulation. We formulate the multiple object tracking problem under the assumption of linearity and independence of the movement of objects. The factorization of the posterior distribution of objects' parameters provides proof of the equivalence of the initial problem and the tracking procedure containing two subtasks: track prediction and assignment of measurements and objects. A modification of the assignment cost is introduced to achieve the stability of assignments in challenging scenarios of tracking, such as multiple objects occlusions and missing detections. We consider adding a term that states to re-identification of the candidate by comparing its descriptor with descriptors from the track history. Given that track measurements are not equal in terms of usefulness for re-identification, we introduce the technique of track descriptor pre-filtering based on quality assessment in order to select the most relevant descriptors for re-identification and reduce method algorithmic complexity. Both known quality assessment methods and an alternative detector-based approach are taken into account. Computational experiments were conducted on MOT20-01, MOT20-02 datasets containing CCTVcameras data in order to compare the proposed method with other approaches. The results showed the computational efficiency of the proposed methods and the increased stability of tracking in complex scenarios.


2021 ◽  
Vol 17 ◽  
pp. 87-92
Author(s):  
OSCAR IBARRA-MANZANO ◽  
JOSE ANDRADE-LUCIO ◽  
YURIY S. SHMALIY ◽  
YUAN XU

Information loss often occurs in industrial processes under unspecified impacts and data errors. Therefore robust predictors are required to assure the performance. We design a one-step H2 optimal finite impulse response (H2-OFIR) predictor under persistent disturbances, measurement errors, and initial errors by minimizing the squared weighted Frobenius norms for each error. The H2-OFIR predictive tracker is tested by simulations assuming Gauss-Markov disturbances and data errors. It is shown that the H2-OFIR predictor has a better robustness than the Kalman and unbiased FIR predictor. An experimental verification is provided based on the moving robot tracking problem


2021 ◽  
Vol 11 (14) ◽  
pp. 6620
Author(s):  
Arman Alahyari ◽  
David Pozo ◽  
Meisam Farrokhifar

With the recent advent of technology within the smart grid, many conventional concepts of power systems have undergone drastic changes. Owing to technological developments, even small customers can monitor their energy consumption and schedule household applications with the utilization of smart meters and mobile devices. In this paper, we address the power set-point tracking problem for an aggregator that participates in a real-time ancillary program. Fast communication of data and control signal is possible, and the end-user side can exploit the provided signals through demand response programs benefiting both customers and the power grid. However, the existing optimization approaches rely on heavy computation and future parameter predictions, making them ineffective regarding real-time decision-making. As an alternative to the fixed control rules and offline optimization models, we propose the use of an online optimization decision-making framework for the power set-point tracking problem. For the introduced decision-making framework, two types of online algorithms are investigated with and without projections. The former is based on the standard online gradient descent (OGD) algorithm, while the latter is based on the Online Frank–Wolfe (OFW) algorithm. The results demonstrated that both algorithms could achieve sub-linear regret where the OGD approach reached approximately 2.4-times lower average losses. However, the OFW-based demand response algorithm performed up to twenty-nine percent faster when the number of loads increased for each round of optimization.


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