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Target Localization with Drones using Mobile CNNs

Target Localization with Drones using Mobile CNNs

引用

25th IEEE/RSJ international conference on Intelligent Robots and systems (IROS)

作者： Lu, Yongxi Wang, Zeyangyi Tang, Ziyao Javidi, Tara Univ Calif San Diego Dept Elect & Comp Engn San Diego CA 92103 USA

ISBN: (纸本)9781538680940

Fast and accurate visual search is an enabler for many applications of drones. Prior works use POMDPs to produce effective search strategies. As the observation models are from heuristics, the robustness of these approaches on the fields is unclear. This work builds a testbed that combines latest developments in related areas, including mobile CNNs for inference on mobile platforms and policy search with point based methods, in a POMDP framework. A dataset for a simple but realistic application, search for a single basketball, is collected to train the perception modules, investigate their error characteristics and validate the control algorithm. From simulation using realistic parameters, we found the significant role persistent factors in the environment can play in designing a fast search strategy. Failure to taking these factors into account results in up to 60% longer search time at the same success rate. Our empirical tests using mobile CNN and real data reveals that prior assumptions on error rates as functions of heights are wrong. The errors grows non-linearly, and there is significant between false positive and false negative rates. Our findings shed new lights on what to consider in designing visual search strategies in a drone platform and is one step towards a fast and robust algorithm.

关键词： Drones Sensors Search problems Data collection Computational modeling Delays Visualization

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Motion control of Triple Links Inverted Pendulum on Two-Wheeled System Using Interval Type-2 Fuzzy Logic control Base on Particle Swarm Optimization

Motion Control of Triple Links Inverted Pendulum on Two-Whee...

引用

IEEE international conference on control System, Computing and Engineering (ICCSCE)

作者： M. F. Masrom N. M. Ghani N. F. Jamin N. A. A. Razali Engineering Universiti Malaysia Pahang Pahang Malaysia

ISBN: (数字)9781728135007

ISBN: (纸本)9781728135014

This paper presents a motion control of a triple link inverted pendulum on two-wheeled system, through Interval Type-2 Fuzzy logic control based Particle Swarm Optimization. Despite this model's heightened non-linearity and complexity for being a triple link system, it has enabled additional degree of freedom for having a two-wheeled mechanism. The model was developed in SimWise 4D to maintain its complex features, while allowing observations to the system's movements. The developed system was then integrated with Interval Type-2 Fuzzy Logic control, as designed in Matlab/Simulink. Specifically, Particle Swarm Optimization was applied to obtain the optimal values for input/output gain and parameters of the Interval Type-2 Fuzzy Logic control, towards enhancing the system's performance. Results have shown that the proposed controller was able to perform forward and backward motions control to the system in an upright position. The system is able to perform motion task with recorded system velocity of 0.04m/s and 0.02m/s for forward and backward motions, respectively. The controller was then evaluated based on its velocity and angular position or in stable manner. This system is envisaged to be very vital in the development of mobile robots with extended functionality.

关键词： Fuzzy logic Mathematical model Solid modeling Particle swarm optimization Wheels conferences control systems

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On-line identification of the heat Peclet number in a gasification reactor

On-line identification of the heat Peclet number in a gasifi...

引用

2nd IFAC conference on Modelling, Identification and control of Nonlinear systems (MICNON)

作者： Badillo-Hernandez, Ulises Santamaria-Padilla, Luis Alvarez, Jesus Alvarez-Icaza, Luis A. CONACYT Univ Autonoma Metropolitana Iztapalapa Mexico City DF Mexico Univ Autonoma Metropolitana Iztapalapa Dept Ingn Proc & Hidraul Apdo 55534 Mexico City 09340 DF Mexico Univ Nacl Autonoma Mexico Inst Ingn Mexico City 04510 DF Mexico

Motivated by the need of developing systematic safe operation as well as monitoring and control schemes, in this study the problem of on-line identifying the heat Peclet number of a bistable tubular gasification reactor is addressed. The Pe number, the ratio of dispersive-radiative to convective heat transport characteristic times, is a key parameter in the modeling of the transient and stationary complex reactor behavior. First, the nonlinear PDE model is discretized with finite differences, yielding a low-order model of global-nonlinear dynamics. Then, the combination of identification ideas from systems theory and tracer technology yields that the Pe number can be on-line identified from the kurtosis and skewness of the effluent temperature response to an induced feed temperature rectangular pulse. The proposed approach is applied to a reactor PDE model calibrated with experimental data, finding that, with respect to a previous study with response variance: (i) kurtosis halves the estimate uncertainty, and (ii) the amplitude of the temperature pulse is halved. (C) 2018, IFAC (international Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Identification of Nonlinear systems Process control Stability & complex Dynamics

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Aero-structure Coupled Optimization for High Aspect Ratio Wings Using Multi-model Fusion Method

Aero-structure Coupled Optimization for High Aspect Ratio Wi...

引用

IEEE international conference on Cyber Technology in Automation, control, and Intelligent systems

作者： Yifan Tang Jing Sun Teng Long Yan Wang Renhe Shi School of Aerospace Engineering Beijing Institute of Technology Beijing P.R. China China Research and Development Academy of Machinery Equipment Beijing P.R. China Key Laboratory of Dynamics and Control of Flight Vehicle Ministry of Education Avic Chengdu Aircraft Industrial (Group) Co. Ltd. Sichuan P.R. China School of Aerospace Engineering Tsinghua University Beijing P.R. China

ISBN: (数字)9781728107707

ISBN: (纸本)9781728107714

To effectively solve the aero-structure coupled optimization problem of high aspect ratio wings, a multi-model fusion method is enhanced and utilized in this paper. First, considerable efforts are spent on aero-structure coupled analysis using computational fluid dynamic (CFD) and finite element analysis (FEA) methods with various levels of fidelity (i.e., coarse and fine grids). Then a multi-model fusion method (MMF) is developed to improve the efficiency of aero-structure coupled optimization. The enhanced multi-model fusion method integrates the limited high-fidelity (HF) simulations with a large number of low-fidelity (LF) simulations to create a surrogate model with preferable accuracy. Moreover, the multi-model fusion based aero-structure coupled optimization strategy is proposed. Finally, the proposed optimization strategy is tested by solving a high aspect ratio wing aero-structure coupled optimization problem. After optimization, the overall performance including lift-drag ratio and structure weight is significantly improved. Compared with Kriging-based optimization method using pure high fidelity model, the multi-model fusion based optimization can achieve a better optimization result under the same computational cost, which illustrates the effectiveness of the proposed multi-model fusion based optimization strategy in solving aero-structure coupled optimization problems of high aspect ratio wings.

关键词： Optimization Analytical models Load modeling Aerodynamics Computational modeling Geometry Strain

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A Reinforcement Learning Approach for Intelligent Traffic Signal control at Urban Intersections

A Reinforcement Learning Approach for Intelligent Traffic Si...

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international conference on Intelligent Transportation

作者： Mengyu Guo Pin Wang Ching-Yao Chan Sid Askary California PATH University of California Berkeley Richmond CA USA Futurewei Technologies Inc. Santa Clara CA USA

ISBN: (纸本)9781538670255

Ineffective and inflexible traffic signal control at urban intersections can often lead to bottlenecks in traffic flows and cause congestion, delay, and environmental problems. How to manage traffic smartly by intelligent signal control is a big challenge in urban traffic management. With recent advances in machine learning, especially reinforcement learning (RL), traffic signal control using advanced machine learning techniques represents a promising solution to tackle this problem. In this paper, we propose a RL approach for traffic signal control at urban intersections. Specifically, we use neural networks as Q-function approximator (a.k.a. Q-network) to deal with the complex traffic signal control problem where the state space can be huge and the action space can be discrete. The state space is defined based on real-time traffic information, i.e. vehicle position, direction and speed. The action space includes various traffic signal phases which are critical in generating a reasonable and realistic control mechanism, given the prominent spatial-temporal characteristics of urban traffic. We use SUMO, an open source traffic simulator, to construct realistic urban intersections. Moreover, we use different traffic patterns, such as major/minor road traffic, through/left-turn lane traffic, tidal traffic, and varying demand traffic, to train a generalized traffic signal control model that can be adapted to various traffic conditions. The simulation results demonstrate the convergence and generalization performance of our RL approach as well as its significant benefits in terms of queue length and wait time over several benchmarking methods in traffic signal control.

关键词： Real-time systems Aerospace electronics Neural networks Traffic control Training Roads Learning (artificial intelligence)

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Parameter Tuning for Comparing Multi-Objective Evolutionary Algorithms Applied to System Identification problems

Parameter Tuning for Comparing Multi-Objective Evolutionary ...

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IEEE international conference on Smart Instrumentation, Measurement and Applications (ICSIMA)

作者： Saiful Farhan M. Samsuri Robiah Ahmad Mohd Zakimi Zakaria Mohd Zarhamdy Md. Zain Department of Applied Mechanics Universiti Teknologi Malaysia Skudai Johor Malaysia UTM Razak Faculty of Technology and Informatics UTM International Campus Kuala Lumpur Malaysia School of Manufacturing Engineering Universiti Malaysia Perlis (UniMAP) Arau Perlis Malaysia

ISBN: (数字)9781728139524

ISBN: (纸本)9781728139531

The parameter estimation tuning is one of the procedures of system identification. One of the identification problems is to find an optimal model structure especially for representing the complex multivariable dynamic systems. Therefore, this problem needs to be solved by using multi-objective optimization with two objective functions namely minimum predictive error and model complexity. In this paper, the tuning of control parameters is studied before comparing multi-objective evolutionary algorithms. The framework is based on the system identification problems. In parameter tuning, two main qualitative parameters of evolutionary algorithms are focused on crossover rate and mutation rate. The multi-objective evolutionary algorithms used for this study are an elitist non-dominated sorting genetic algorithm (NSGA-II) and multi-objective optimization using differential evolution (MOODE). The different parameter settings are compared in order to obtain good parameter setting for NSGA-II and MOODE. Then, the performance for both algorithms is also compared using Pareto front. The plotted graphs of two objective functions show the non-dominated fronts achieved by the algorithms. The results proved that MOODE shows an advantage in solving system identification problems in a multivariable nonlinear dynamic system.

关键词： Autoregressive processes complexity theory Mathematical model Tuning Predictive models Data models Sociology

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Uniqueness Conditions for ALS problems

Uniqueness Conditions for ALS Problems

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6th international-Federation-of-Automatic-control (IFAC) conference on Nonlinear-Model-Predictive-control (NMPC)

作者： Arnold, Travis J. Rawlings, James B. Univ Wisconsin Madison Dept Chem & Biol Engn Madison WI 53706 USA

Knowledge of the process, measurement, and cross noise covariance matrices (denoted Q, R, and S, respectively) is necessary for tasks such as state estimation and performance monitoring. Several different types of algorithms have been developed to estimate these parameters from plant output data. Chief among them are the so-called correlation methods, such as autocovariance least squares (ALS). Despite the advances in covariance estimation algorithms, relatively little attention has been given to the topic of parameter identifiability. This paper discusses the limitations of when Q, R, and S are identifiable from output data. In particular, it is shown that for stable, linear time-invariant systems, the Kalman predictor gain, but not Q, R, and S, can be uniquely identified from the steady-state output autocovariance. Constrained ALS problems and the extension of the ALS problem to nonlinear and linear time-varying systems are also discussed. (C) 2018, IFAC (international Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Covariance matrices state estimation parameter estimation model predictive control uniqueness least squares

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A Brief Survey on Six-Degree-of-Freedom modeling for Air-breathing Hypersonic Vehicles

A Brief Survey on Six-Degree-of-Freedom Modeling for Air-bre...

引用

5th IFAC conference on Advances in control and Optimization of Dynamical systems (ACODS)

作者： Sachan, Kapil Padhi, Radhakant IISc Dept Aerosp Engn Bangalore Karnataka India

The six-degree-of-freedom (six-DOF) modeling of air-breathing hypersonic vehicles (AHVs) is highly complex due to the variable flight conditions, structural elasticity and inherent aero-propulsive coupling. However, considering different modeling approaches, a significant amount of work has been reported in the area of AHV modeling. The aim of this paper is to investigate the six-DOF modeling of AHVs reported in the literature. Next, owing to the modeling uncertainties and coupled dynamics, state-of-the-art robust adaptive control design techniques have been presented for these models, which are also briefly reviewed in this paper. This survey also contains a small discussion on possible future advancements in the modeling and control of these vehicles. (C) 2018, IFAC (international Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Six-degree-of-freedom modeling Winged-cone hypersonic model control of hypersonic vehicle Hypersonic vehicle modeling

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On multiparametric/explicit NMPC for Quadratically Constrained problems

On multiparametric/explicit NMPC for Quadratically Constrain...

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6th international-Federation-of-Automatic-control (IFAC) conference on Nonlinear-Model-Predictive-control (NMPC)

作者： Diangelakis, Nikolaos A. Pappas, Iosif S. Pistikopoulos, Efstratios N. Texas A&M Univ Artie McFerrin Dept Chem Engn College Stn TX 77845 USA Texas A&M Univ Texas A&M Energy Inst College Stn TX 77845 USA

We present the expansion of the Basic Sensitivity Theorem to a second order Taylor approach and the implications to explicit model predictive control of quadratically constrained systems. The expansion enables the derivation of an algorithm for the analytical solution of convex multiparametric quadratically constraint programming (mpQCQP) problems and explicit quadratically constrained NMPC problems. We derive the analytical parametric expressions of the control actions for a quadratically constrained MPC problem and its corresponding critical regions. We show the piecewise non-linear form of the solution and closed loop validation of the results. (C) 2018, IFAC (international Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Multiparametric programming quadratically constrained quadratic programming explicit MPC

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Dependable Parallel Canonical Differential Evolutionary Particle Swarm Optimization for Voltage and Reactive Power control

Dependable Parallel Canonical Differential Evolutionary Part...

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10th IFAC Symposium on control of Power and Energy systems (CPES)

作者： Yoshida, Hotaka Azuma, Daich Fukuyama, Yoshikazu Meiji Univ Tokyo Japan

This paper proposes dependable parallel canonical differential evolutionary particle swarm optimization (C-DEEPSO) for voltage and reactive power control (Volt/Var control: VVC). Since the problem can be formulated as a mixed integer nonlinear optimization problem (MINLP), various evolutionary computation techniques have been applied to the problem including PSO, differential evolution (DE), and DEEPSO. Considering large penetration of renewable energies and deregulated environments of power systems, VVC requires fast computation for larger-scale VVC problems. One of the solutions to speed-up the computation is to utilize parallel and distributed computing. Since power system is one of the infrastructures of social community, not only fast computation, but also sustainable control (dependability) is strongly required for VVC. The simulation results with with IEEE 14, 30, 57, and 118 bus systems indicate that parallel C-DEEPSO is superior to the conventional parallel DEEPSO from the dependability point of view. (C) 2018, IFAC (international Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Voltage control Reactive power Parallel computation Heuristic searches Optimization problems

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