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In this study, we designed an autonomous mobile robot according to the rules of the Federation of International Robot-soccer Association (FIRA) RoboSot category, integrating the techniques of computer vision, real-time image processing, dynamic target tracking, wireless communication, self-localization, motion control, path planning, and control strategy to achieve the contest goal. The real time...
This paper introduces a few iterative algorithms for the governing equations of motion. The algorithms described here cater for a wide range of flexible mechanism models. These iterative processes facilitate the straightforward extension to solve flexible multibody dynamics by including additional constraints. The convergence analysis of the iteration approach is achieved. The paper focuses on the...
This paper presents an algorithm, based on the alternating direction method of multipliers, for the convex optimal control problem arising in input-constrained model predictive control. We develop an efficient implementation of the algorithm for the extended linear quadratic control problem (LQCP) with input and input-rate limits. The algorithm alternates between solving an extended LQCP and a highly...
In order to reduce the response time and solve the problem of reactive power overcompensation of the magnetically controlled reactor (MCR), a novel compensation algorithm is proposed in this paper. The algorithm is based on the principles and algorithms of unbalanced load compensation and via modifying the compensation conditions of the Steinmetz principle. The general formula of the compensatory...
Under an extended proportional-integral (PI) control scheme, distributed average tracking (DAT) control algorithms are derived for networked Euler-Lagrange systems for two different kinds of reference signals: reference signals with steady states and reference signals with bounded derivatives.
In this work, Differential Evolution Algorithm (DEA) is implemented on an embedded systems based on FPGA for the training of multi-layer perceptron (MLP). The classification performance of the MLP trained by DEA on FPGA has been analyzed by using a non-linear database. The MLP performance on FPGA has been compared with that on MATLAB in terms of computational performance and test accuracy. It is proved...
We consider an approach for solving strictly convex quadratic programs (QPs) with general linear inequalities by the alternating direction method of multipliers (ADMM). In particular, we focus on the application of ADMM to the QPs of constrained Model Predictive Control (MPC). After introducing our ADMM iteration, we provide a proof of convergence closely related to the theory of maximal monotone...
This paper investigates proportional-integral distributed optimization when the underlying information exchange network is dynamic. Proportional-integral distributed optimization is a technique which combines consensus-based methods and dual-decomposition methods to form a method which has the convergence guarantees of dual-decomposition and the damped response of the consensus methods. This paper...
This paper addresses the problem of consensus in the presence of Byzantine faults, modeled by an attacker injecting a perturbation in the state of the nodes of a network. It is firstly shown that Set-Valued Observers (SVOs) attain finite-time consensus, even in the case where the state estimates are not shared between nodes, at the expenses of requiring large horizons, thus rendering the computation...
Simultaneous recording of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) represents a powerful tool for brain activity investigation. Unfortunately, EEG data collected during concurrent fMRI are affected by very large artifacts. This paper focuses on the gradient artifact (GRA), related to the sawtooth profiles of magnetic flux inside the MRI scanner. A novel removal...
Assessments of functional connectivity between brain networks is a fixture of resting state fMRI research. Until very recently most of this work proceeded from an assumption of stationarity in resting state network connectivity. In the last few years however, interest in moving beyond this simplifying assumption has grown considerably. Applying group temporal independent component analysis (tICA)...
We propose an algorithm for consensus of second-order sampled-data multi-agent systems in the presence of misbehaving agents. Each normal agent updates its values (position and velocity) following a predetermined control law based on local information obtained through directed interaction while some malicious agents make updates arbitrarily. The normal agents do not know the global topology of the...
Closeness centrality is a basic centrality measure that characterizes how centrally located a node is, within a network, based on its distances to all other nodes. In this paper, we address the distributed computation of two variants of this measure, known as classic closeness and exponential closeness, which differ in how the distances are taken into account. For each variant, we construct continuous-...
This paper gives new results on the design of iterative learning control algorithms in the repetitive process setting for error convergence and regulation of the transient dynamics. The analysis makes use of the generalized Kalman-Yakubovich-Popov lemma to develop a design algorithm different performance specifications are imposed in one or more frequency ranges. Also these new results allow, if required...
Particle filters are well-known as powerful tools for accomplishing state and parameter estimation and their propagation prediction in nonlinear dynamical systems. Their ability to include system model parameters as part of the system state vector is among one of the key factors for their use in prognostics. Estimation of system parameters along with the states produces an updated model that can be...
Counting the number of perfect matchings in arbitrary graphs is a sharpP-complete problem. However, for some restricted classes of graphs the problem can be solved efficiently. In the case of planar graphs, and even for K_{3, 3}-free graphs, Vazirani showed that it is in NC^2. The technique there is to compute a Pfaffian orientation of a graph. In the case of K_5-free graphs, this technique will not...
Existing mainstream indoor localization technologies mainly rely on RF signatures and thus incur significant and recurring labor cost to measure the time-varying signature map. We have proposed a smartphone localization system using the embedded gyroscope for triangulation from nearby physical features (e.g., store logos) recognized from photo-taking. It requires a much reduced and one-time measurement,...
In this paper, a new online evolving clustering approach for streaming data is proposed, named Dynamically Evolving Clustering method. The clustering approach attempts to meet the following three key requirements of data stream clustering: (i) fast and memory efficient (ii) adaptive (iii) robust to noise. The proposed clustering approach processes one sample at a time and makes necessary changes to...
In the past decade, adaptive dynamic programming (ADP) has been widely used to realize online learning tracking control of dynamical systems, where neural networks with manually designed features are commonly used. In order to improve the generalization capability and learning efficiency of ADP, this paper presents a novel framework of ADP with sparse kernel machines by integrating kernel methods...
This paper focuses on parameters estimation problems of multivariable nonlinear systems. A hierarchical least squares algorithm is proposed by using key-term separation principle and hierarchical identification principle. The algorithm has lower computational load than the existing over-parametrization methods. Finally, a numerical example is given to show the effectiveness of the proposed algorithm.
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