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In this paper, a discrete terminal sliding mode controller (DTSMC) is proposed based on a new nonlinear sliding function. This strategy of control is developed using only the input-output (I/O) model. To improve the effectiveness of DTSMC, we applied it to a DC-DC buck converter. The results obtained by simulations are given at the end to demonstrate the effectiveness of our proposed scheme in terms...
Simultaneous Localization and Mapping(SLAM) algorithms can infer the robot's trajectory as well as the map under unknown environment. Robust and time-efficient optimization methods are important requirements for SLAM. There are many algorithms designed for the graph optimization. However, it is hard to select an appropriate algorithm and corresponding software library, due to the difficulty of evaluating...
The orthogonal least square regression (OLSR) serves as a pretty significant problem for the dimensionality reduction. Due to lack of the scale change in OLSR, the scaling term is at first introduced to OLSR to build up a novel orthogonal least square regression with optimal scaling (OLSR-OS) problem. However, OLSR-OS is still sensitive to the outliers, such that associated results could be fallacious...
Iterative Learning Control (ILC) enables performance improvement by learning from previous tasks. The aim of this paper is to develop an ILC approach for Linear Parameter Varying (LPV) systems to enable improved performance and increased convergence speed compared to the linear time-invariant approach. This is achieved through dedicated analysis and norm-optimal synthesis of LPV learning filters....
This paper proposes a RISE-based cooperative control of uncertain multi-agent system with exogenous disturbances. First, we introduce the second order model with disturbance for agent. The network topology among agents is undirected and connected, and information of reference is allowed to be available to at least one agent. Second, we propose the control law consists of graph theory, consensus algorithm...
Optimal power flow (OPF) problems are non-convex and large-scale optimization problems. Finding an optimal solution for the OPF problem in real time is challenging and important in various applications. Recent studies show that a wide class of OPF problems have an exact semidefinite programming (SDP) convex relaxation. However, only few works have considered distributed algorithms to solve these....
This paper considers a nonsingular sliding mode controller for a second order system without a priori knowledge of the control direction. A novel nonsingular terminal sliding hypersurface is presented to adapt to the challenges of unknown control direction and avoid singularity issues at the origin. In contrast to the Nussbaum gain, where the equilibrium point is reached asymptotically, or the classical...
This paper studies linear set-dynamics driven by random convex compact sets (RCCSs), where the parameter matrix evolves according to an underlying Markovian random process taking values in a finite set. We derive dynamics of the expectations of the associated reach sets. We establish that such expectations evolve according to coupled deterministic set-dynamics. We provide sufficient conditions for...
In this paper, a recurrent wavelet neural network(RWNN) control system based on chaotic series adaptive particle swarm optimization (PSO) is proposed to control the PMSM drive system. First, the RWNN control system is developed for the control of PMSM. Moreover, the on-line learning algorithms of the connective weights, translations and dilations of the RWNN are derived using back-propagation(BP)...
In the large-scale machine learning, it is very important to effectively train machine learning model. Stochastic gradient descent and its variants have achieved good performance in model training. Recently there have been the stochastic L-BFGS method, which has better efficiency of the training compared to stochastic gradient descent. However, it is difficult to find a suitable step selection method...
A robust flight control for a quadrotor unmanned aerial vehicle is addressed in this paper. An adaptive super-twisting control, a second order sliding mode control, is used to control position and attitude of a quadcopter. The controller reduces chattering, is robust to uncertainties and perturbations, increases accuracy and does not require time-derivative of sliding variables. A time-varying sliding...
Iterative learning control (ILC) involves the tracking of a trajectory over a finite interval, but with the introduction of new applications, scenarios emerged where tracking for whole duration is not mandatory, while tracking on specific points have more significance, so other forms of ILC emerged which are termed as point to point ILC with significance at intermediate points or specific points during...
In this paper, we propose an adaptive framework for the variable step size of the fractional least mean square (FLMS) algorithm. The proposed algorithm named the robust variable step size-FLMS (RVSS-FLMS), dynamically updates the step size of the FLMS to achieve high convergence rate with low steady state error. For the evaluation purpose, the problem of system identification is considered. The experiments...
This thesis tries to analyze the drawbacks and shortages of the present ant-based text clustering algorithms (Z-ACTCA algorithm in short). The author attempts to improve the ant-based text clustering algorithms from the following three aspects: text similarity calculation, iterations termination condition as well as parametric adaption. Meanwhile, preprocession will be done on the two-dimensional...
Dictionary learning follows a synthesis framework; the dictionary is learnt such that the data can be synthesized / re-generated from the coefficients. Transform learning on the other hand is based on analysis formulation; it learns a transform so as to generate the coefficients. The basic formulations of dictionary learning and transform learning employ a Euclidean cost function for the data fidelity...
Vector approximate message passing (VAMP) is a computationally simple approach to the recovery of a signal x from noisy linear measurements y = Ax + w. Like the AMP proposed by Donoho, Maleki, and Montanari in 2009, VAMP is characterized by a rigorous state evolution (SE) that holds under certain large random matrices and that matches the replica prediction of optimality. But while AMP's SE holds...
Large-scale networks of computing units, often characterised by the absence of central control, have become commonplace in many applications. To facilitate data processing in these large-scale networks, distributed signal processing is required. The iterative behaviour of distributed processing algorithms combined with energy, computational power, and bandwidth limitations imposed by such networks,...
The Fast Iterative Shrinkage-Thresholding Algorithm (FISTA) is regarded as the state-of-the-art among a number of proximal gradient-based methods used for addressing large-scale optimization problems with simple but non-differentiable objective functions. However, the efficiency of FISTA in a wide range of applications is hampered by a simple drawback in the line search scheme. The local estimate...
Many computer vision problems are formulated as an objective function consisting of a sum of functions. In the case of ill-constrained problems, regularization terms are included in the objective function to reduce the ambiguity and noise in the solution. The most commonly used regularization terms are the L2 norm and the L1 norm. Since the last two decades, the class of regularized problems, especially...
This paper outlines the development of a new type of digital adaptive-array algorithm for interference cancellation. It is fast in the sense that it requires only order-of-N computations per iteration and converges in a substantially fixed number of iterations (proportional to N) regardless of the signal scenario or geometry of the N-element array. The need for such algorithms arises in dynamic signal...
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