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29th International Conference on Technologies and Applications of Artificial Intelligence, TAAI 2024

作者： Matsuzaki, Kiminori Kochi University of Technology Kochi Kami782-8502 Japan

ISBN: (纸本)9789819645954

N-tuple networks are simple and efficient methods for generating evaluation functions and have been used with an increasing number of N-tuples to develop state-of-the-art 2048 computer players. Although performance is expected to improve as the number of N-tuples increases, a systematic method is needed to find a good combination of many N-tuples. In this study, we propose the third systematic method that can be applied to any number of N-tuples and take into account the interdependency between them. Our approach has two main steps: first, we formulate the problem with a function from a combination of N-tuples to an evaluation value;second, we use neural networks to approximate this function. We compare the combinations selected by existing methods and proposed methods as well as those designed manually. Although the combinations selected by the proposed method did not outperform those from our previous work, they achieved better results than the manually designed combinations. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： function approximator Game 2048 N-tuple networks

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Speed tracking control for hydraulic transformer system based on active regulating common pressure rail

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IET CONTROL THEORY AND APPLICATIONS 2020年第20期14卷 3547-3556页

作者： Shen, Wei Shen, Chao Su, Xiaoyu Univ Shanghai Sci & Technol Dept Mechatron Engn Shanghai 200093 Peoples R China Zhejiang Univ State Key Lab Fluid Power & Mechatron Syst Hangzhou 310000 Peoples R China Shanghai Univ Engn Sci Coll Elect & Elect Engn Shanghai 201620 Peoples R China

This study investigates the speed tracking control problem for the new hydraulic transformer controlled system based on the active regulating common pressure rail with both input delay and strong interference. Firstly, the Pade approximation approach is adopted to approximate and compensate for the input delay of the valve plate rotating controlled by the hydraulic swing motor. Then, the Levant filter is introduced to solve the 'explosion of complexity' caused by repeated differentiation of the virtual control signals, and the compensation mechanism is constructed to compensate for the filtering errors. Besides, due to the strong non-linear characteristics of the proposed system, the pi-sigma fuzzy neural network is employed as the function approximator to address the non-linear terms. Based on finite-time command control technology and the barrier Lyapunov functionals, a robust controller is synthesised to ensure a better error convergence performance while without violating the state constraint. Finally, the effectiveness of the controller is verified by comparison with the traditional control method and simulation result.

关键词： valves adaptive control Lyapunov methods function approximation nonlinear control systems neurocontrollers robust control closed loop systems delays control system synthesis tracking fuzzy neural nets virtual control signals compensation mechanism nonlinear characteristics pi-sigma fuzzy neural network function approximator finite-time command control technology robust controller hydraulic transformer system active regulating common pressure rail speed tracking control problem hydraulic transformer controlled system input delay Pade approximation approach hydraulic swing motor

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SPSA-based data-driven control strategy for load frequency control of power systems

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IET GENERATION TRANSMISSION & DISTRIBUTION 2018年第2期12卷 414-422页

作者： Dong, Na Han, Xue-Shuo Gao, Zhong-Ke Chen, Zeng-Qiang Wu, Ai-Guo Tianjin Univ Sch Elect & Informat Engn 92 Weijin Rd Tianjin Peoples R China Nankai Univ Coll Comp & Control Engn Tianjin Peoples R China

To meet the demands of the modern power system for satisfactory operation and control, here, a novel data-driven control strategy is proposed to solve the load frequency control (LFC) problems of power systems, with complete convergence analysis. This data-based LFC approach is designed based on the simultaneous perturbation stochastic approximation (SPSA) method and neural network ensemble. The data-based controller is constructed using a function approximator, which is fixed as a neural network. Being the control parameters, the connection weights of the neural network controller are updated at each iteration step. In order to improve the overall control accuracy and get more stable control performance, the idea of neural network ensemble is introduced for the data-based controller structure design. The proposed data-based controller takes past and current system information as input and generates a control signal that can affect future system performance as output, and during the whole process, it is not necessary to build mathematical model for the controlled plant. A one-area LFC problem with system parametric uncertainties as well as a typical two-area LFC problem have been introduced for simulation tests, and the feasibility and effectiveness of this newly proposed data-based LFC strategy is well revealed through simulation results.

关键词： neurocontrollers power system control frequency control load regulation iterative methods SPSA-based data-driven control strategy load frequency control complex dynamic characters mathematic model data-driven control strategy power systems data-based LFC approach complete convergence analysis simultaneous perturbation stochastic approximation data-based controller function approximator connection weights neural network controller iteration step neural network ensemble data-based controller structure design mathematical model one-area LFC problem system parametric uncertainties two-area LFC problem data-based LFC strategy

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Data-driven control based on simultaneous perturbation stochastic approximation with adaptive weighted gradient estimation

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IET CONTROL THEORY AND APPLICATIONS 2016年第2期10卷 201-209页

作者： Dong, Na Wu, Chun-Ho Gao, Zhong-Ke Chen, Zeng-qiang Ip, Wai-Hung Tianjin Univ Sch Elect Engn & Automat Tianjin 300072 Peoples R China Hong Kong Polytech Univ Dept Ind & Syst Engn ISE Kowloon Hong Kong Peoples R China Nankai Univ Dept Automat Tianjin 300071 Peoples R China

This study proposes one novel data-driven control strategy based upon the simultaneous perturbation stochastic approximation method with adaptive weighted gradient estimation for general discrete non-linear systems. A function approximator is used to construct the controller, and here, it is fixed as a neural network (NN), whose structure is fixed previously, while allowing its connecting weights to be updated. The control parameters are then the connecting weights of the NN controller. The biggest advantage of this data-driven control approach is that it can generate a control signal to affect system's future performance without establishing the plant's mathematical model first. In this novel approach, to improve the control ability and accuracy, an adaptive weighted gradient estimation method is designed to do the parametric estimation with convergence analysis. Non-linear tracking problems for typical discrete-time non-linear plants are introduced for simulation comparison tests, and the convergence and feasibility of this newly proposed data-driven control strategy are well demonstrated through the simulation results. Finally, empirical study on a simulated wastewater treatment system is carried out to further illustrate the effectiveness of this newly proposed approach.

关键词： perturbation techniques stochastic processes adaptive estimation gradient methods function approximation neurocontrollers convergence discrete time systems nonlinear control systems simultaneous perturbation stochastic approximation data-driven control strategy general discrete nonlinear systems function approximator neural network NN controller adaptive weighted gradient estimation method parametric estimation convergence analysis nonlinear tracking problems discrete-time nonlinear plants wastewater treatment system

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Multilayer perceptrons as function approximators for analytical solutions of the diffusion equation

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COMPUTATIONAL GEOSCIENCES 2015年第4期19卷 769-780页

作者： Campisi, Laura D. Univ E Anglia Sch Environm Sci Norwich NR4 7TJ Norfolk England

A novel method using neural networks to analyse diffusion profiles is presented. Multilayer perceptrons are used to approximate the analytical solution of the diffusion equation and find within it any unknown parameter that best fits a given data set. An example based on published data of diffusion of helium is examined to illustrate the main steps of the method. The exercise shows that it is possible to refine the value of diffusion coefficients up to two orders of magnitude in terms of precision. A particular feature of the method is that calibration curves can be taken into account when choosing the best setup of a network, which allows minimization of instrument specific error. A general version of the method giving the opportunity to define a local diffusion coefficient is also discussed, which could be considered for cases where the analytical solution of the diffusion equation cannot be identified a priori or does not exist.

关键词： Diffusion Neural network function approximator Inverse modelling Nonlinear regression

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Adaptive Fuzzy-PID and Neural Network Based Object Tracking Using a 3-Axis Platform 2

Adaptive Fuzzy-PID and Neural Network Based Object Tracking ...

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2nd IEEE International Conference on Engineering and Technology (ICETECH)

作者： Singh, Govind Nandakumar, M. P. Ashok, S. NIT Calicut Elect Engn Calicut Kerala India

ISBN: (纸本)9781467399166

A 3-Axis Gimbal structure is used to inertially stabilize a platform which can be used to track afixed or moving point in space with the help of optical sensors such as Laser, IR or camera installed on the platform. This paper presents a solution to the problem of non-linear indirect relationship between the platform's Euler angles, gimbals' orientation angles and disturbance angles by using a Neural network as a function approximator. Angular disturbances are incorporated in the model to make the system's control robust in a certain range of disturbances. Three gimbals are controlled by three DC motors using their respective Fuzzy tuned PID controllers for a fast response. The Neural network calculates the respective orientation angles for the gimbals solving the problem of loop interaction of three gimbals control. The whole system is modelled in Matlab/Simulink and comparative results are shown for different disturbances and for varying number of neurons in the hidden layer. Based on the results for different disturbances, problem of loop interaction is solved and system tracks object despite kinematic disturbances and indirect kinematic relationships between gimbals and platform.

关键词： 3-Axis Stabilization System Fuzzy controller Self tuned PID controller Neural Network function approximator Object tracking

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Adaptive variable structure hierarchical fuzzy control for a class of high-order nonlinear dynamic systems

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ISA TRANSACTIONS 2015年 56卷 28-41页

作者： Mansouri, Mohammad Teshnehlab, Mohammad Shoorehdeli, Mandi Aliyari KN Toosi Univ Technol Fac Elect Engn Dept Control Engn Tehran Iran KN Toosi Univ Technol Fac Elect Engn Ind Control Ctr Excellence Tehran Iran KN Toosi Univ Technol Fac Elect Engn Dept Mechatron Engn Tehran Iran

In this paper, a novel adaptive hierarchical fuzzy control system based on the variable structure control is developed for a class of SISO canonical nonlinear systems in the presence of bounded disturbances. It is assumed that nonlinear functions of the systems be completely unknown. Switching surfaces are incorporated into the hierarchical fuzzy control scheme to ensure the system stability. A fuzzy soft switching system decides the operation area of the hierarchical fuzzy control and variable structure control systems. All the nonlinearly appeared parameters of conclusion parts of fuzzy blocks located in different layers of the hierarchical fuzzy control system are adjusted through adaptation laws deduced from the defined Lyapunov function. The proposed hierarchical fuzzy control system reduces the number of rules and consequently the number of tunable parameters with respect to the ordinary fuzzy control system. Global boundedness of the overall adaptive system and the desired precision are achieved using the proposed adaptive control system. In this study, an adaptive hierarchical fuzzy system is used for two objectives;it can be as a function approximator or a control system based on an intelligent-classic approach. Three theorems are proven to investigate the stability of the nonlinear dynamic systems. The important point about the proposed theorems is that they can be applied not only to hierarchical fuzzy controllers with different structures of hierarchical fuzzy controller, but also to ordinary fuzzy controllers. Therefore, the proposed algorithm is more general. To show the effectiveness of the proposed method four systems (two mechanical, one mathematical and one chaotic) are considered in simulations. Simulation results demonstrate the validity, efficiency and feasibility of the proposed approach to control of nonlinear dynamic systems. (C) 2014 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： Adaptive control function approximator Hierarchical fuzzy system Switching surface Variable structure control

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Q-Learning in Continuous State-Action Space with Noisy and Redundant Inputs by Using a Selective Desensitization Neural Network

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JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS 2015年第6期19卷 825-832页

作者： Kobayashi, Takaaki Shibuya, Takeshi Morita, Masahiko Univ Tsukuba Fac Engn Informat & Syst 1-1-1 Tennodai Tsukuba Ibaraki 3058573 Japan

When applying reinforcement learning (RL) algorithms such as Q-learning to real-world applications, we must consider the influence of sensor noise. The simplest way to reduce such noise influence is to additionally use other types of sensors, but this may require more state space - and probably increase redundancy. Conventional value-function approximators used to RL in continuous state-action space do not deal appropriately with such situations. The selective desensitization neural network (SDNN) has high generalization ability and robustness against noise and redundant input. We therefore propose an SDNN-based value-function approximator for Q-learning in continuous state-action space, and evaluate its performance in terms of robustness against redundant input and sensor noise. Results show that our proposal is strongly robust against noise and redundant input and enables the agent to take better actions by using additional inputs without degrading learning efficiency. These properties are eminently advantageous in real-world applications such as in robotic systems.

关键词： reinforcement learning function approximator continuous state-action sensor noise redundant inputs

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Approximation-based adaptive control of uncertain non-linear pure-feedback systems with full state constraints

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IET CONTROL THEORY AND APPLICATIONS 2014年第17期8卷 2070-2081页

作者： Kim, Bong Su Yoo, Sung Jin Chung Ang Univ Sch Elect & Elect Engn Seoul 156756 South Korea

This study proposes an adaptive approximation-based control approach for non-linear pure-feedback systems in the presence of full state constraints. Completely non-affine non-linear functions are considered and assumed to be unknown. The dynamic surface design based on integral barrier Lyapunov functionals is provided to achieve both the desired tracking performance and the constraints satisfaction, in consideration of the full-state-constrained non-affine non-linearities. In this design procedure, simple sufficient conditions for choosing control gains, which can be checked off-line, are established to guarantee the feasibility of the controller. The function approximation technique is employed to estimate unknown non-linearities induced from the controller design procedure where the adaptive laws using the projection operator are designed to ensure the boundedness of the function approximators in the feasibility conditions. It is shown that all the signals in the closed-loop system are uniformly ultimately bounded and the tracking error converges to an adjustable neighbourhood of the origin while all state variables always remain in the constrained state space.

关键词： adaptive control nonlinear control systems uncertain systems constraint theory Lyapunov methods constraint satisfaction problems control system synthesis function approximation closed loop systems state-space methods approximation-based adaptive control uncertain nonlinear pure-feedback system full state constraint nonaffine nonlinear function dynamic surface design integral barrier Lyapunov functional tracking performance constraints satisfaction full-state-constrained nonaffine nonlinearity sufficient condition control gain function approximation technique unknown nonlinearity controller design procedure adaptive law projection operator function approximator feasibility condition closed-loop system tracking error state variable constrained state space

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Learning Compact Parameterized Skills with a Single Regression 13

Learning Compact Parameterized Skills with a Single Regressi...

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13th IEEE-RAS International Conference on Humanoid Robots (Humanoids)

作者： Stulp, Freek Raiola, Gennaro Hoarau, Antoine Ivaldi, Serena Sigaud, Olivier ENSTA ParisTech Robot & Comp Vis Paris France INRIA Bordeaux Sud Ouest FLOWERS Team Talence France Pal Robot SL Barcelona Spain Univ Pierre Marie Curie ISIR CNRS UMR 7222 Paris France

ISBN: (纸本)9781479926190

One of the long-term challenges of programming by demonstration is achieving generality, i. e. automatically adapting the reproduced behavior to novel situations. A common approach for achieving generality is to learn parameterizable skills from multiple demonstrations for different situations. In this paper, we generalize recent approaches on learning parameterizable skills based on dynamical movement primitives (DMPs), such that task parameters are also passed as inputs to the function approximator of the DMP. This leads to a more general, flexible, and compact representation of parameterizable skills, as demonstrated by our empirical evaluation on the iCub and Meka humanoid robots.

关键词： function approximation humanoid robots regression analysis robot dynamics DMP Meka humanoid robot compact parameterized skill demonstration programming dynamical movement primitive function approximator iCub humanoid robot regression reproduced behavior task parameter Kernel Robots Shape Training Trajectory Vectors Visualization

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