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Consensus for Heterogeneous Systems with Delays
Published in Magdi S Mahmoud, Multiagent Systems, 2020
and control design parameters c=2,γ1=0.1,nΔi=1,i∈{2,3,4},σΔi=2,i∈{2,3,4},nϒ1=1, and σϒ1=3. The system performance of the controller given by (4.9) and (4.10) with the proposed adaptive scheme is shown in Figs. 4.2–4.5 for the ith follower agent, where i ∈ {1, 2, 3, 4}. Specifically, Figs. 4.2–4.5 show a sample trajectory along with the standard deviation of the state tracking error εi(t) = xi(t) − x0(t) for agent i ∈ {1, 2, 3, 4} versus time for 10 sample paths. The mean control profile is also plotted in Figs. 4.7–4.5. It follows from Theorem 4.1, that the state tracking error for each agent is guaranteed to be uniformly ultimate bounded in a mean-square sense.
Data Fusion in Intelligent Traffic and Transportation Engineering Recent Advances and Challenges
Published in Hassen Fourati, Krzysztof Iniewski, Multisensor Data Fusion, 2016
Nour-Eddin El Faouzi, Lawrence A. Klein
The Kalman filter provides a general solution to the recursive, minimum mean-square estimation problem within the class of linear estimators. It minimizes the mean-squared error as long as the object’s dynamics and measurement noise are accurately modeled. As applied to a tracking problem, the filter estimates an object’s state at some time, for example, the predicted time of the next observation, and then updates that estimate using noisy measurements. It also provides an estimate of object-tracking error statistics through the state error-covariance matrix [48].
Examples – tuning predictive control and numerical conditioning
Published in J.A. Rossiter, Model-Based Predictive Control, 2004
There is a need for some simple insight. The reason for the apparent ineffectiveness of λ is that the performance index is dominated by the steady-state output tracking error. So the optimisation places all the emphasis on making tracking errors small and the impact of the control weighting is relatively small. However, if one changed the output horizon to, for instance, ny = 8, the control weighting now has a large impact, as the cost is no longer dominated by tracking errors.
Sliding mode controlled DC microgrid system with enhanced response
Published in Journal of Control and Decision, 2022
B. Balaji, S. Ganesan, P. Pugazhendiran, S. Subramanian
The standard error of the discrepancy among a value's results as well as its reference is known as tracking error. Tracking error is determined as described in the following provided a series of outcomes for an institution or vehicle and it evaluate: Standard Deviation of the Tracking Error (P – B). Among the most crucial metrics for evaluating a portfolio's profitability and an investment management's potential for outperforming the marketplace or comparison is tracking error. It is utilised as a parameter to determine the informational proportion for the approaches explained earlier. The goal is to utilise a well-behaved tracking error function, s from (2) and then set the feedback control rule u in (1) such that s2 maintains a closed-loop despite model imprecision and disturbances.
Variable neighborhood search heuristic for nonconvex portfolio optimization
Published in The Engineering Economist, 2019
Andrijana Bačević, Nemanja Vilimonović, Igor Dabić, Jakov Petrović, Darko Damnjanović, Dušan Džamić
In terms of portfolio performance measures, we consider the following quantities:IR is a risk-adjusted measure that captures excess or active returns and relates them to excess or active risk. It measures a portfolio manager’s ability to generate excess returns relative to a benchmark. A high value can be achieved by having a high return in the portfolio, a low return of the benchmark, and a low tracking error. Sharpe ratio (SR) is a measure of investment efficiency expressed as the amount of return earned per unit of associated risk. It can be used to compare two managers directly on how much excess return each manager achieved for a certain level of risk. Tracking error (TE) is a measure of how closely the manager follows the benchmark index, and is measured as the standard deviation of the difference between the manager and index returns. An active manager whose sector and security selection deviates widely from the index may exhibit high tracking error. A passively managed index fund is expected to replicate the returns of an index and should have a low tracking error.
Impact of investor sentiment on mutual fund risk taking and performance: evidence from China
Published in Enterprise Information Systems, 2020
Jian Wang, Xiaoting Wang, Jun Yang, Xintian Zhuang
For risk-taking measures, Chen and Pennacchi (2009) and Schwarz (2012) argue that the conventional return volatility may not be an appropriate indicator of risk taken by mutual fund. In this study, two measures are adopted for risk-taking by mutual funds. One is the tracking error volatility relative to a benchmark (denoted as RT_trkerr), the most popular measure on the degree of active management in mutual fund industry. Actively managed funds try to outperform their benchmark by strategically managing the tracking error instead of the standard deviation of returns (Roll 1992; Jorion 2003).