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Estimating model parameters is a crucial step to understand the behavior of biological systems. To perform parameter estimation, a commonly used formulation is the least square method that minimizes the mean squared error. This method finds the model parameters that minimize the sum of the squared error between experimental data and model predictions. However, such a formulation can misguide parameter...
This paper presents a chance-constrained scheduling (CCS) approach for variable wind generation, in the day-ahead timescale, including energy storage. The day-ahead CCS utilizes the ramping of conventional generation as well as the dispatch of energy storage to enhance the load following and ramping support capabilities, to mitigate the impact of net load ramps. The proposed CCS approach is converted...
This paper presents a new method for designing the weights used for development of robust controllers for a quadrotor model with parameter uncertainty. The weights alongside the controllers are developed for attitude and altitude tracking by resolving a constrained non-linear minimization problem formulated over the conventional mixed sensitivity optimization S over T method. The optimization routine...
Although many articles have shown that the control performance of interval type-2 fuzzy logic controllers (IT2 FLCs) is better than type-1 FLCs, the computational cost of it is high, which makes it hard to develop in the real world. Our previous research has recommended six TR approaches for their efficiency, but which one has the best performance is still an unknown problem. In the paper, IT2 FLCs...
This paper studies a formation control problem for multiple mobile robot systems with model uncertainty. The formation is achieved by a distributed robust predictive control algorithm. Firstly, a distributed model of the formation system is derived by using the path tracking error dynamic of single mobile robot. Considering the error of linearization and measurement error caused by the accelerometer...
Existing approaches to system optimization (optimal design) under uncertainty are considered. The exact formulation of constrained optimization problem with interval uncertainty of goal function and constraints parameters is given. Mathematical theory of intervals comparison including the precise definition of minimum and maximum ranges is stated. On the basis of this theory the determination method...
To deal with the increasing penetration of uncertainties caused by renewable generations and uncertain loads, this paper proposes a novel robust optimization model for the optimal power flow (OPF) problem of power systems. In the model proposed, the generation and the network topology are co-optimized, and then the proposed robust optimal power flow with transmission switching (ROPF_TS) model is converted...
This paper proposes a robust optimization framework for energy hub management. It is well known that the operation of energy systems can be negatively affected by uncertain parameters, such as stochastic load demand or generation. In this regard, it is of high significance to propose efficient tools in order to deal with uncertainties and to provide reliable operating conditions. On a broader scale,...
Bayesian Optimization or Efficient Global Optimization (EGO) is a global search strategy that is designed for expensive black-box functions. In this algorithm, a statistical model (usually the Gaussian process model) is constructed on some initial data samples. The global optimum is approached by iteratively maximizing a so-called acquisition function, that balances the exploration and exploitation...
The paper presents a variation of the systems' synthesis problem setting, where it is proposed to use the values of the entropy potentials of the output parameters as an optimization criterion. The use of such criteria makes it possible to improve the quality of the process dynamics assessment, what creates the prerequisites for the control efficiency improvement. With the reference to this specific...
This paper presents the implementation of a nonlinear robust controller in a boost converter, which operates in an uncertain environment. The proposed controller deals with uncertainties, which are unknown but bounded, and occur in the input voltage and the output pure resistive load. Mathematical proof of the efficiency of the controller and quantitative results about the amount of the uncertainty...
The problems of control applied investment processes under risk conditions are considered. Firstly, theoretical considerations given regarding the formulation and approximate solution of the optimization problem in the presence of finite series of observation of the flow of the process. □ Secondly, we present the results of the computational experiments.
Fuzzy logic systems have been extensively applied for solving many real world application problems because they are found to be universal approximators and many methods, particularly, gradient descent (GD) methods have been widely adopted for the optimization of fuzzy membership functions. Despite its popularity, GD still suffers some drawbacks in terms of its slow learning and convergence. In this...
Energy flow control (EFC) of residential microgrids (RMGs) equipped with renewable generations (RGs) is an essential component for the future smart grid that contributes to enhance renewable energy consumption and reduce cost. Different from most existing papers that devote to offline EFC to against the uncertainties caused by RGs and local load demand in RMGs, this paper focuses on online EFC framework...
The conceptual framework of future power grid incorporates self-automated power flow components. It incorporates high penetration of intermittent renewable energy and variable users' load demands. To establish an efficient real-time power flow mechanism, a realization of the exact amount of energy generation and consumption is inevitable. Hence, we propose a novel framework of a smart micro-grid (SMG),...
This paper presents a generalized iterative learning control (ILC) design in the frequency domain with experimental validation. The optimal ILC learning function and robustness filter function are simultaneously optimized by solving a linear programming problem using frequency response functions. Moreover, the design realizes an optimal trade-off between robust convergence, converged tracking performance,...
The operation scheduling is a crucial factor that can affect the economic and environmental benefits and operation reliability of the microgrids (MGs). However, widely used scheduling methods are lacking in the comprehensive consideration for the multi-objective (MO) property and robustness of the system operation. Also, the generated solutions might not diminish the disturbance of uncertainty under...
Urban evacuation is of great significance in saving human lives when disasters strike major cities. Comparing with the large population to evacuate, the number of evacuation guiders is much smaller. Since the location of evacuation guiders can greatly influence the evacuation process, it is crucial to identify the most critical locations to assign guiders. The assignment optimization for evacuation...
Estimating the 6-DoF pose of a camera from a single image relative to a pre-computed 3D point-set is an important task for many computer vision applications. Perspective-n-Point (PnP) solvers are routinely used for camera pose estimation, provided that a good quality set of 2D-3D feature correspondences are known beforehand. However, finding optimal correspondences between 2D key-points and a 3D point-set...
With the diversified development of electrical loads, and the large-scale integration of volatile renewable generations, power system operation, control and planning face the challenge of increasing injection uncertainties. In this paper, a new method for reference network considering nodal injection uncertainties is proposed. The actual operation mode optimization and reserve configuration of power...
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