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A hybrid evolutionary algorithm for recurrent neural network control of a three-dimensional tower crane

AUTOMATION IN CONSTRUCTION 2012年第May期23卷 55-63页

作者： Duong, Sam Chau Uezato, Eiho Kinjo, Hiroshi Yamamoto, Tetsuhiko Univ Ryukyus Fac Engn Nishihara Okinawa 9030213 Japan Univ Ryukyus Grad Sch Engn & Sci Nishihara Okinawa 9030213 Japan Tokushima Coll Technol Tokushima Japan

This paper is concerned with the control of an underactuated three-dimensional tower crane system using a recurrent neural network (RNN) which is evolved by an evolutionary algorithm. In order to improve the performance in evolving the RNN, a hybrid evolutionary algorithm (HEA) which utilizes the operators of a constricted particle swarm optimization (PSO) and a binary-coded genetic algorithm (GA) is proposed. Simulation results show that the proposed HEA has superior performance in a comparison with the canonical algorithms and that the control system works effectively. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Hybrid evolutionary algorithm Recurrent neural network Tower crane Underactuated system nonlinear system control

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Enhancing Zero Moment Point-Based control Model: system Identification Approach

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ADVANCED ROBOTICS 2011年第3-4期25卷 427-446页

作者： Suleiman, Wael Kanehiro, Fumio Miura, Kanako Yoshida, Eiichi Natl Inst Adv Ind Sci & Technol Intelligent Syst Res Inst CNRS AIST JRLCRT UMI3218 Tsukuba Ibaraki 3058568 Japan

The approximation of a humanoid robot by an inverted pendulum is one of the most frequently used models to generate a stable walking pattern using a planned zero moment point (ZMP) trajectory. However, on account of the difference between the multibody model of the humanoid robot and the simple inverted pendulum model, the ZMP error might be bigger than the polygon of support and the robot falls down. To overcome this limitation, we propose to improve the accuracy of the inverted pendulum model using system identification techniques. The candidate model is a quadratic in the state space representation. To identify this system, we propose an identification method that is the result of the comprehensive application of system identification to dynamic systems. Based on the quadratic system, we also propose controlling algorithms for on-line and off-line walking pattern generation for humanoid robots. The efficiency of the quadratic system and the walking pattern generation methods has been successfully shown using dynamical simulation and conducting real experiments on the cybernetic human HRP-4C. (C) Koninklijke Brill NV, Leiden and The Robotics Society of Japan, 2011

关键词： Humanoid robot zero moment point control system identification optimization nonlinear system control

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Structured Wavelet-based Neural Network for control of nonlinear systems

Structured Wavelet-based Neural Network for Control of Nonli...

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50th IEEE Conference of Decision and control (CDC)/European control Conference (ECC)

作者： Karami, A. Yazdanpanah, M. J. Univ Tehran Sch Elect & Comp Engn Control & Intelligent Proc Ctr Excellence Tehran Iran

ISBN: (纸本)9781612848013

In this paper, a wavelet-based neural network is proposed for the control of nonlinear systems. Activation functions of neural network nodes are determined based on the wavelet transform. The controller can efficiently compensate for the undesired effects of hard nonlinearities such as saturation and/or dead zone of control input. Compared with standard neuro-controllers, the structure of the controller is definite and simple. The proposed controller is localizable and has a systematically chosen structure, which improves the close-loop performance. An off-line algorithm determines the number of nodes. In addition, an on-line algorithm adjusts the parameters of wavelet bases and network weights. Back propagation algorithm with a momentum term is used for updating the weights and parameters of activation functions. This controller reduces the quantity of network parameters, calculation cost and convergence time of online algorithms with respect to the conventional neural network. Also, the controller is able to control unstable and MIMO systems. To illustrate the capability and performance superiority of the proposed controller, two nonlinear systems are controlled and the corresponding results are compared.

关键词： Wavelet transform neural network nonlinear system control adaptive activation functions

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Efficient Knowledge-Based Cultural Differential Evolution for Neural Fuzzy Inference systems

Efficient Knowledge-Based Cultural Differential Evolution fo...

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16th Annual International Conference on Technologies and Applications of Artificial Intelligence (TAAI)

作者： Chen, Cheng-Hung Yang, Sheng-Yen Natl Formosa Univ Dept Elect Engn Huwei Township 632 Yunlin Taiwan

ISBN: (纸本)9780769546018

This study proposes a knowledge-based cultural differential evolution (KCDE) for neural fuzzy inference systems (NFIS). The cultural algorithms involve acquiring the belief space from the evolving population space and then exploiting that information to guide the search. The proposed KCDE method adopts the mutation strategies of differential evolution as knowledge sources to influence a population space. These knowledge sources including normative knowledge, situational knowledge, domain knowledge, history knowledge, and topographic knowledge are integrated in the proposed method for optimizing parameters of the NFIS model. Experimental results have demonstrated that the proposed NFIS-KCDE method performs well in nonlinear system control problems.

关键词： Neural fuzzy inference systems cultural algorithm differential evolution water bath temperature system backing up the truck nonlinear system control

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Dynamic Inversion Based on Neural Network Applied to nonlinear Flight control system

Dynamic Inversion Based on Neural Network Applied to Nonline...

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The 2010 2nd International Conference on Future Computer and Communication(第二届未来计算机与通信国际会议 ICFCC 2010)

作者： Kechang Qian Zili Chen Ordance Engineering College Shijiazhuang China

ISBN: (纸本)9781424458219;9781424458240

Modern flight control system is always a complicated nonlinear flight control system, this paper presents a control method of dynamic inversion based on neural network applied to nonlinear flight control system. Through analyzes the decoupling inversion and tracking principle of neural network, a dynamic neural network is presented, which consist of static neural network and integrators. Based on the dynamic neural network, a control method of dynamic inversion used for multi-variable nonlinear flight control system is designed, and simulation of flight control system of power parafoil is made. Simulation results demonstrate that the control method has strong ability of control and robustness.

关键词： dynamic inversion nonlinear system control neural network flight control system

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Boundary Feedback control of Coupled Hyperbolic Linear PDEs systems with nonlinear Boundary Conditions

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IFAC Proceedings Volumes 2011年第1期44卷 14464-14469页

作者： Masayasu Suzuki Jun-ichi Imura Kazuyuki Aihara Japan Science and Technology Agency 2-12-1 Ookayama Meguro-ku Tokyo Japan Tokyo Institute of Technology 2-12-1 Ookayama Meguro-ku Tokyo Japan University of Tokyo 4-6-1 Komaba Meguro-ku Tokyo Japan

Abstract We try to stabilize equilibrium solutions of physical models described by a class of coupled first-order linear partial differential equations with nonlinear boundary conditions. These systems are distributed parameter systems in which spatio-temporal chaos occurs. In order to stabilize the equilibrium solutions of the systems, at first, we reduce the PDEs to discrete-time systems using the method of characteristics. Then, we design control laws for the discrete-time systems and use them for the original PDEs systems. In this report, we propose a method to get corresponding discrete-time systems for coupled PDEs systems with 2n (≥2) state variables. Furthermore, we apply the method to a coupled time-delayed Chua's circuit, and design a control law using dynamic feedback linearization.

关键词： Partial differential equation Boundary value problem Chaos nonlinear system control Application of nonlinear analysis and design

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Structured Wavelet-based Neural Network for control of nonlinear systems

Structured Wavelet-based Neural Network for Control of Nonli...

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IEEE Conference on Decision and control

作者： A. Karami M.J. Yazdanpanah Control & Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran

ISBN: (纸本)9781612848006

关键词： Wavelet transform neural network nonlinear system control adaptive activation functions

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Parameter estimation and compensation in systems with nonlinearly parameterized perturbations

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AUTOMATICA 2010年第1期46卷 19-28页

作者： Grip, Havard Fjaer Johansen, Tor A. Imsland, Lars Kaasa, Glenn-Ole NTNU Dept Engn Cybernet NO-7491 Trondheim Norway Cybernet AS NO-7038 Trondheim Norway StatoilHydro Res Ctr NO-3908 Porsgrunn Norway

We consider a class of systems influenced by perturbations that are nonlinearly parameterized by unknown constant parameters, and develop a method for estimating the unknown parameters. The method applies to systems where the states are available for measurement, and perturbations with the property that an exponentially stable estimate of the unknown parameters can be obtained if the whole perturbation is known. The main contribution is to introduce a conceptually simple, modular design that gives freedom to the designer in accomplishing the main task, which is to construct an update law to asymptotically invert a nonlinear equation. Compensation for the perturbations in the system equations is considered for a class of systems with uniformly globally bounded solutions, for which the origin is uniformly globally asymptotically stable when no perturbations are present. We also consider the case when the parameters can only be estimated when the controlled state is bounded away from the origin, and show that we may still be able to achieve convergence of the controlled state. We illustrate the method through examples, and apply it to the problem of downhole pressure estimation during oil well drilling. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： nonlinear system control Uncertain nonlinear systems Adaptive control nonlinear observer design

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Generating Dynamically Stable Walking Patterns for Humanoid Robots Using Quadratic system Model

Generating Dynamically Stable Walking Patterns for Humanoid ...

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IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)

作者： Suleiman, Wael Kanehiro, Fumio Miura, Kanako Yoshida, Eiichi Natl Inst Adv Ind Sci & Technol Intelligent Syst Res Inst Joint Robot Lab CRT UMI3218CNRS AIST JRL Tsukuba Ibaraki 3058568 Japan

ISBN: (纸本)9781424480302

The approximation of humanoid robot by an inverted pendulum is one of the most used model to generate a stable walking motion. Many studies have been carried out to improve the reliability of this model. One of the proposed models is the quadratic system model, which has been validated by conducting real experiments on the humanoid robot. In this paper, we propose several controlling algorithms for the quadratic system. Some of these algorithms are devoted for on-line and off-line walking pattern generation algorithms for the humanoid robot. Dynamically stable walking patterns have been generated in order to validate these algorithms. The stability and feasibility of walking patterns have been confirmed using dynamical simulation and conducting real experiments on the humanoid robot HRP-4C.

关键词： Humanoid robot ZMP control Optimization nonlinear system control

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Generating Dynamically Stable Walking Patterns for Humanoid Robots Using Quadratic system Model

Generating Dynamically Stable Walking Patterns for Humanoid ...

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2010 IEEE/ASME international conference on advanced intelligent mechatronics (AIM 2010)

作者： Wael Suleiman Fumio Kanehiro Kanako Miura Eiichi Yoshida CNRS-AIST JRL (Joint Robotics Laboratory) UMI3218/CRT Intelligent Systems Research Institute National Institute for Advanced Industrial Science and Technology Tsukuba Ibaraki Japan

关键词： Humanoid robot ZMP control Optimization nonlinear system control

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