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This paper studies an uncertain time-dependent vehicle routing problem with soft time windows. A novel mathematical model which considers both transportation costs (total traveling distance and number of vehicles) and service costs (early and late arrivals) is developed, and the equations for calculating the expected total service costs are deduced under uncertainty and time-dependency. A variation...
The Robust Vehicle Routing Problem with Time Windows has been gaining popularity over the past few years due to its focus on tackling uncertainty inherent to real world problems. Most of the current approaches in generating robust solutions require prior knowledge on the uncertainties, such as uncertainties in travel time. Hence, they are less than favorable to use in the absence of data, i.e., in...
This paper describes a framework of incorporating smart sampling techniques in a probabilistic look-ahead contingency analysis application. The predictive probabilistic contingency analysis helps describe the impact of uncertainties caused by variable generation and load on potential violations of transmission limits. The objectives of smart sampling techniques are to represent structure and statistical...
This paper presents a method for analyzing patterns of criminal activity that occur in space and time. The method uses the fuzzy C-means algorithm to cluster criminal events in space. In addition, a cluster reorganization algorithm is included to preserve the order of fuzzy partitions from one time step analysis to another. Order preservation is possible since crime forms relatively stable patterns...
An intelligent recommendation algorithm based on hidden Markov chain model is proposed and used to conduct intelligent recommendation on document search in this paper. This algorithm is faster in operational efficiency than regular Markov chain algorithm and the collaborative filtering algorithm and has a certain improvement in the accuracy of recommendation. Firstly, the algorithm has advantages...
This paper studies a data-driven adaptive optimal control problem of a Quanser's 2-degree-of-freedom (DOF) helicopter via output-feedback. A novel sampled-data-based approximate/adaptive dynamic programming (ADP) approach is developed. We start from a stabilizing controller computed using the bound of model uncertainties. Then the optimal control gain is iteratively learned by input/output information...
In this paper we consider the problem of set-membership identification of multiple-inputs multiple-outputs (MIMO) linear models when both input and output measurements are affected by bounded additive noise. First a general formulation is proposed which allows the user to take into account possible a-priori information about the structure of the MIMO model to be identified. Then, the problem is formulated...
We study in this paper the problem of adaptive trajectory tracking control for a class of nonlinear systems with parametric uncertainties. We propose to use a modular approach: We first design a robust nonlinear state feedback that renders the closed loop input-to-state stable (ISS). Here, the input is considered to be the estimation error of the uncertain parameters, and the state is considered to...
A robust adaptive observer based fault diagnosis method is proposed for non-Gaussian uncertain stochastic distribution control (SDC) systems based on the linear B-spline model. When certain conditions are satisfied, the weight state variable proves to be bounded. Stability analysis is performed for the observation error dynamic system raised form robust fault diagnosis when the uncertainty exists...
The problem of generating a robust attack position plan for multi-UCAV cooperative attacking the moving target on the sea area is studied. There are many kinds of uncertain factors in the execution of multi-UCAV cooperative attack task, and the hostile target's maneuverability plays an important role. In order to handle the uncertainties in the cooperative attack mission well, a new approach based...
With the availability of communication technologies within the smart grid infrastructure, demand response has emerged as a viable mechanism of influencing user behavior. Varying prices in real-time motivates the users to modify their energy consumption schedule and obtain fiscal benefits by running non-critical loads at off-peak hours. This paper considers the design of domestic load scheduler under...
The certainty-equivalence super-twisting controller (CESTA) combines approaches from variable structure and adaptive control. In this paper, an improved variant of this algorithm is presented that resorts to a recently introduced Lyapunov function for the super twisting algorithm, establishing a novel continuous adaptation law. These controllers are beneficial for systems that are affected by structured...
In this paper it is presented the second stage analysis used to determine the signal quality coefficient for vibro-acoustic signals. After successfully determining the integrity coefficient in the “first stage analysis on determining the Signal Quality Coefficient in vibro-acoustic domain-waveform integrity analysis” paper a second and deeper analysis is presented. This is a frequency domain analysis...
The existing second-order sliding mode (SOSM) control methods can only be used to handle sliding mode control systems subject to matched disturbance. This paper proposes a novel SOSM control method, which allows the sliding mode control systems with mismatched disturbance. Such a control approach is intended to deal with the first derivative of the sliding variable characterized by a certain term...
In this paper, second-order systems in the presence of both uncertainties and disturbances are considered. It is revealed that super-twisting controllers based on high-order sliding mode observers cannot be implemented when uncertainties are taken into account. An output feedback control law adopting the twisting algorithm is designed such that a Lipschitz continuous as well as saturated control signal...
In this paper, we propose a multi-layer self-diagnosis framework for networking services within SDN and NFV environments. The framework encompasses three main contributions: 1) the definition of multi-layered templates to identify what to supervise while taking into account the physical, logical, virtual and service layers. These templates are also finer-granular, extendable and machine-readable;...
This paper proposes a disturbance observer (DOB)-based adaptive non-homogeneous higher order sliding mode control (HOSMC) scheme. Given a large initial tracking error, the proposed HOSMC provides fast convergence rate by using non-homogeneous finite time stabilization. In addition, a disturbance observer based adaptive control method is employed to identify bounded uncertainties. To solve the overestimation...
Considering the nonlinear and uncertainty in the MINS/GNSS navigation system, a nonlinear Sage-Husa noise maximum posterior estimator was designed. Since the estimator cannot solve problems both of system noise and observation, a Bi-parallel BP neural network controller is designed to approximate the estimator. Then an adaptive UKF algorithm based on Bi-parallel neural network is proposed. In the...
Planned Sensor Distortion (PSD) is a compression method that quantizes shifted signal with low bit depth. In this paper, we analyze the dynamic range loss issue in the PSD algorithm and propose two novel methods to overcome this issue: a blocked PSD, which divides the image into sub-blocks that adapt to pixel values, and an auto-reset PSD, which utilizes Markov property to recover a high dynamic range...
The traditional iterative learning control (ILC) algorithm improves the control performance by updating the control input to implicitly compensate the periodic uncertainties. In order to enhance the convergence rate of ILC, a new concept, iterative extended state observer (IESO), is presented which can estimate explicitly the periodic uncertainties during the process of iterations and be used to update...
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