Adaptive Backstepping Control for Fractional-Order Nonlinear Systems with External Disturbance and Uncertain Parameters Using Smooth Control

In this paper, we consider controlling a class of single-input-single-output (SISO) commensurate fractional-order nonlinear systems with parametric uncertainty and external disturbance. Based on backstepping approach, an adaptive controller is proposed with adaptive laws that are used to estimate the unknown system parameters and the bound of unknown disturbance. Instead of using discontinuous functions such as the $\mathrm{sign}$ function, an auxiliary function is employed to obtain a smooth control input that is still able to achieve perfect tracking in the presence of bounded disturbances. Indeed, global boundedness of all closed-loop signals and asymptotic perfect tracking of fractional-order system output to a given reference trajectory are proved by using fractional directed Lyapunov method. To verify the effectiveness of the proposed control method, simulation examples are presented.Comment: Accepted by the IEEE Transactions on Systems, Man and Cybernetics: Systems with Minor Revision

Intensity Mapping Functions For HDR Panorama Imaging: Weighted Histogram Averaging

It is challenging to stitch multiple images with different exposures due to possible color distortion and loss of details in the brightest and darkest regions of input images. In this paper, a novel intensity mapping algorithm is first proposed by introducing a new concept of weighted histogram averaging (WHA). The proposed WHA algorithm leverages the correspondence between the histogram bins of two images which are built up by using the non-decreasing property of the intensity mapping functions (IMFs). The WHA algorithm is then adopted to synthesize a set of differently exposed panorama images. The intermediate panorama images are finally fused via a state-of-the-art multi-scale exposure fusion (MEF) algorithm to produce the final panorama image. Extensive experiments indicate that the proposed WHA algorithm significantly surpasses the related state-of-the-art intensity mapping methods. The proposed high dynamic range (HDR) stitching algorithm also preserves details in the brightest and darkest regions of the input images well. The related materials will be publicly accessible at https://github.com/yilun-xu/WHA for reproducible research.Comment: 11 pages, 5 figure

Numerical simulation and process optimization on cast steel bearing sleeve

A three-dimensional Computer Aided Design (CAD) model of the bearing sleeve casting is established by Pro/E software. The ViewCast program is utilized for studying casting processes of solidification and mould-filling in order to optimize the casting technology. Based on the solidification simulation, the casting shrinkage and porosity as well as solidification processes are forecast visually in diagrams with the help of ViewCast. The mould-filling simulation results verify whether the fluid/liquid metal fills gates and the mould smoothly. The simulation results of an initial casting scheme show that this scheme is improper. Two optimization schemes have been completed based on the filling simulation so that a modified casting technology is obtained. The simulation results of optimized schemes indicate that the metal fluid fills the mold smoothly and the shrinkage is eliminated effectively. The optimized scheme II is preferred to scheme I. Experimentally, the casting confirms that these optimized methods are very useful in reducing the casting defects and improving the product quality

Resilient Multi-Dimensional Consensus in Adversarial Environment

This paper considers the multi-dimensional consensus in networked systems, where some of the agents might be misbehaving (or faulty). Despite the influence of these misbehaviors, the healthy agents aim to reach an agreement within the convex hull of their initial states. Towards this end, this paper develops a resilient consensus algorithm, where each healthy agent sorts its received values on one dimension, computes two "middle points" based on the sorted values, and moves its state toward these middle points. We further show that the computation of middle points can be efficiently achieved by linear programming. Compared with the existing works, this approach has lower computational complexity. Assuming that the number of malicious agents is upper bounded, sufficient conditions on the network topology are then presented to guarantee the achievement of resilient consensus. Some numerical examples are finally provided to verify the theoretical results.Comment: arXiv admin note: substantial text overlap with arXiv:1911.1083

Enabling controlling complex networks with local topological information

Complex networks characterize the nature of internal/external interactions in real-world systems including social, economic, biological, ecological, and technological networks. Two issues keep as obstacles to fulflling control of large-scale networks: structural controllability which describes the ability to guide a dynamical system from any initial state to any desired fnal state in fnite time, with a suitable choice of inputs; and optimal control, which is a typical control approach to minimize the cost for driving the network to a predefned state with a given number of control inputs. For large complex networks without global information of network topology, both problems remain essentially open. Here we combine graph theory and control theory for tackling the two problems in one go, using only local network topology information. For the structural controllability problem, a distributed local-game matching method is proposed, where every node plays a simple Bayesian game with local information and local interactions with adjacent nodes, ensuring a suboptimal solution at a linear complexity. Starring from any structural controllability solution, a minimizing longest control path method can efciently reach a good solution for the optimal control in large networks. Our results provide solutions for distributed complex network control and demonstrate a way to link the structural controllability and optimal control together.The work was partially supported by National Science Foundation of China (61603209), and Beijing Natural Science Foundation (4164086), and the Study of Brain-Inspired Computing System of Tsinghua University program (20151080467), and Ministry of Education, Singapore, under contracts RG28/14, MOE2014-T2-1-028 and MOE2016-T2-1-119. Part of this work is an outcome of the Future Resilient Systems project at the Singapore-ETH Centre (SEC), which is funded by the National Research Foundation of Singapore (NRF) under its Campus for Research Excellence and Technological Enterprise (CREATE) programme. (61603209 - National Science Foundation of China; 4164086 - Beijing Natural Science Foundation; 20151080467 - Study of Brain-Inspired Computing System of Tsinghua University program; RG28/14 - Ministry of Education, Singapore; MOE2014-T2-1-028 - Ministry of Education, Singapore; MOE2016-T2-1-119 - Ministry of Education, Singapore; National Research Foundation of Singapore (NRF) under Campus for Research Excellence and Technological Enterprise (CREATE) programme)Published versio

Author correction: Enabling controlling complex networks with local topological information

Correction to: Scientific Reports https://doi.org/10.1038/s41598-018-22655-5, published online 15 March 2018. The Acknowledgements section in this Article is incomplete.The work was partially supported by National Science Foundation of China (61603209, 61327902), and Beijing Natural Science Foundation (4164086), and the Study of Brain-Inspired Computing System of Tsinghua University program (20151080467), and SuZhou-Tsinghua innovation leading program 2016SZ0102, and Ministry of Education, Singapore, under contracts RG28/14, MOE2014-T2-1-028 and MOE2016-T2-1-119. Part of this work is an outcome of the Future Resilient Systems project at the Singapore-ETH Centre (SEC), which is funded by the National Research Foundation of Singapore (NRF) under its Campus for Research Excellence and Technological Enterprise (CREATE) program. (61603209 - National Science Foundation of China; 61327902 - National Science Foundation of China; 4164086 - Beijing Natural Science Foundation; 20151080467 - Study of Brain-Inspired Computing System of Tsinghua University program; 2016SZ0102 - SuZhou-Tsinghua innovation leading program; RG28/14 - Ministry of Education, Singapore; MOE2014-T2-1-028 - Ministry of Education, Singapore; MOE2016-T2-1-119 - Ministry of Education, Singapore; National Research Foundation of Singapore (NRF) under its Campus for Research Excellence and Technological Enterprise (CREATE) program)Published versio