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neural dynamic programming for Event-Based Nonlinear Adaptive Robust Stabilization 1

23rd International Conference on neural Information Processing (ICONIP)

作者： Wang, Ding Ma, Hongwen Liu, Derong Wang, Huidong Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China Shandong Univ Finance & Econ Sch Management Sci & Engn Jinan 250014 Peoples R China

ISBN: (数字)9783319466873

ISBN: (纸本)9783319466873;9783319466866

In this paper, we develop an event-based adaptive robust stabilization method for continuous-time nonlinear systems with uncertain terms via a self-learning technique called neural dynamic programming. Through system transformation, it is proven that the robustness of the uncertain system can be achieved by designing an event-triggered optimal controller with respect to the nominal system under a suitable triggering condition. Then, the idea of neural dynamic programming is adopted to perform the main controller design task by building and training a critic network. Finally, the effectiveness of the present adaptive robust control strategy is illustrated via a simulation example.

关键词： Adaptive dynamic programming Adaptive robust stabilization Event-based control neural dynamic programming neural network

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neural dynamic programming in Reactive Navigation of Wheeled Mobile Robot

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11th International Conference on Artificial Intelligence and Soft Computing (ICAISC)

作者： Hendzel, Zenon Szuster, Marcin Rzeszow Univ Technol Dept Appl Mech & Robot PL-35959 Rzeszow Poland

ISBN: (纸本)9783642293498;9783642293504

In the article a new approach to a reactive navigation of a wheeled mobile robot (WMR), using a neural dynamic programming algorithm (NPD), is presented. A proposed discrete hierarchical control system consists of a trajectory generator and a tracking control system. In the trajectory generator we used a sensor-based approach to path design for the WMR in an unknown 2-D environment with static obstacles. The main part of the navigator is an action dependant heuristic dynamic programming algorithm (ADHDP), that generates control signals used to design a collision-free trajectory, that makes reaching a goal possible. ADHDP is the discrete algorithm of actor-critic architecture, that works on-line and does not require a preliminary learning or a controlled system knowledge. The tracking control system realises the generated trajectory, it consists of dual-heuristic dynamic programming (DHP) structure, PD controller and the supervisory term derived from the Lyapunov stability theorem. Computer simulations have been conducted to illustrate the performance of the algorithm.

关键词： neural dynamic programming Reactive Navigation Wheeled Mobile Robot

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Helicopter trimming and tracking control using direct neural dynamic programming

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IEEE TRANSACTIONS ON neural NETWORKS 2003年第4期14卷 929-939页

作者： Enns, R Si, J Arizona State Univ Dept Elect Engn Tempe AZ 85287 USA

This paper advances a neural-network-based approximate dynamic programming control mechanism that can be applied to complex control problems such as helicopter flight control design. Based on direct neural dynamic programming (DNDP), an approximate dynamic programming methodology, the control system is tailored to learn to maneuver a helicopter. The paper consists of a comprehensive treatise of this DNDP-based tracking control framework and extensive simulation studies for an Apache helicopter. A trim network is developed and seamlessly integrated into the neural dynamic programming (NDP) controller as part of a baseline structure for controlling complex nonlinear systems such as a helicopter. Design robustness is addressed by performing simulations under various disturbance conditions. All designs are tested using FLYRT, a sophisticated industrial scale nonlinear,validated model of the Apache helicopter. This is probably the first time that an approximate dynamic programming methodology has been systematically applied to, and evaluated on, a complex, continuous state, multiple-input-multiple-output nonlinear system with uncertainty. Though illustrated for helicopters, the DNDP control system framework should be applicable to general purpose tracking control.

关键词： approximate dynamic programming helicopter flight control helicopter trim neural dynamic programming

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Apache helicopter stabilization using neural dynamic programming

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JOURNAL OF GUIDANCE CONTROL AND dynamicS 2002年第1期25卷 19-25页

作者： Enns, R Si, J Arizona State Univ Dept Elect Engn Tempe AZ 85287 USA

A new form of neural control is introduced, neural dynamic programming (NDP), a model-free online learning control scheme. NDP is shown to perform exceedingly well as a learning controller for practical systems of higher dimension, such as helicopters. The discussion is focused on providing a viable alternative helicopter control system design approach rather than providing extensive comparisons among various available controllers. A comprehensive treatise of NDP and extensive simulation studies of NDP designs for controlling an Apache helicopter under different flight conditions is presented. Design robustness is addressed by performing simulations under various disturbance conditions. All of the designs are based on FLYRT, a sophisticated industry-scale nonlinear validated model of the Apache helicopter.

关键词： Apache Helicopter neural dynamic programming Helicopter Blade Actuators Control System Design Yaw Feedforward neural Network Proportional Integral Derivative Linear Quadratic Gaussian Reinforcement Learning

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Discrete neural dynamic programming in wheeled mobile robot control

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COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION 2011年第5期16卷 2355-2362页

作者： Hendzel, Zenon Szuster, Marcin Rzeszow Univ Technol Dept Appl Mech & Robot PL-35959 Rzeszow Poland

In this paper we propose a discrete algorithm for a tracking control of a two-wheeled mobile robot (WMR), using an advanced Adaptive Critic Design (ACD). We used Dual-Heuristic programming (DHP) algorithm, that consists of two parametric structures implemented as neural Networks (NNs): an actor and a critic, both realized in a form of Random Vector Functional Link (RVFL) NNs. In the proposed algorithm the control system consists of the DHP adaptive critic, a PD controller and a supervisory term, derived from the Lyapunov stability theorem. The supervisory term guaranties a stable realization of a tracking movement in a learning phase of the adaptive critic structure and robustness in face of disturbances. The discrete tracking control algorithm works online, uses the WMR model for a state prediction and does not require a preliminary learning. Verification has been conducted to illustrate the performance of the proposed control algorithm, by a series of experiments on the WMR Pioneer 2-DX. (C) 2010 Elsevier B.V. All rights reserved.

关键词： neural dynamic programming neural networks Tracking control Wheeled mobile robot

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Friction coefficient estimation in servo systems using neural dynamic programming inspired particle swarm search

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APPLIED INTELLIGENCE 2015年第1期43卷 1-14页

作者： Lu, Yongzhong Yan, Danping Levy, David Huazhong Univ Sci & Technol Sch Software Engn Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Coll Publ Adm Wuhan 430074 Peoples R China Univ Sydney Fac Engn & Informat Technol Sydney NSW 2006 Australia

Parameter estimation of static friction torques in servo control systems is of great significance to their robust control. Many researchers are devoted to pursuing the solutions to estimating the coefficients of the static friction torques. In order to tackle the troublesome matter more effectively, in this paper, we address a neural dynamic programming inspired particle swarm search algorithm. We call the algorithm direct BP neural dynamic programming inspired PSO (NDPSO) since we incorporate direct back propagation (BP) and neural dynamic programming (NDP) into particle swarm optimization (PSO). In NDPSO, critic BP neural network is trained to balance the Bellman equation while action BP neural network is used to train the inertia weight, the cognitive coefficient, and the social coefficient of the PSO algorithm. The training target is to enable the critic BP neural network output to approach the ultimately successful objective. Successively, NDPSO, together with standard PSO (SPSO) and genetic algorithm (GA), is applied to the parameter identification of the static friction torque in a servo control system with single input and single output (SISO). The experimental results clearly demonstrate that NDPSO is effective and outperforms SPSO and GA in identifying the parameters of the static friction torque in the servo control system.

关键词： Parameter identification Servo control system neural dynamic programming Static friction torque Particle swarm optimization

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An Adaptive Path Tracking Method for Autonomous Land Vehicle based on neural dynamic programming 13

An Adaptive Path Tracking Method for Autonomous Land Vehicle...

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IEEE International Conference on Mechatronics and Automation

作者： Zhu, Qi Huang, Zhenhua Liu, Daxue Dai, Bin Natl Univ Def Technol Coll Mechatron Engn & Automat Changsha Hunan Peoples R China

ISBN: (纸本)9781509023967

Since the nonlinear properties of the autonomous land vehicles (ALVs) and the time-varying relationship between ego-vehicle and the desired path, it is difficult to tune the parameters of a path tracking controller for the autonomous driving of ALVs. Aiming at this problem, a novel learning based path tracking method is proposed in this paper, which is composed of the Stanley control structure and a learning based module. The input of the learning module is the relationship between current vehicle state and the desired path, and the learning output is the parameter k in the Stanley control structure. What we want to learn is to adaptive tune k according to current vehicle state. A near-optimal policy is obtained by neural dynamic programming (NDP), which is an online and model-free algorithm. The learning based module online tunes the parameter k of the Stanley control structure. The simulation results show that the proposed path tracking method possesses attractive performance.

关键词： path tracking autonomous land vehicle neural dynamic programming Stanley method

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An adaptive longitudinal control method for autonomous follow driving based on neural dynamic programming and internal model structure

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INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS 2017年第6期14卷

作者： Zhu, Qi Dai, Bin Huang, Zhenhua Sun, Zhenping Liu, Daxue Natl Univ Def Technol Coll Mechatron Engn & Automat 109 Deya Rd Changsha 410073 Hunan Peoples R China

Autonomous vehicles are considered to have great potentials in improving transportation safety and efficiency. Autonomous follow driving is one of the highly probable application forms of autonomous vehicles in the near future. In this article, we aim at the basic autonomous following form with one follower and one leader. Proper longitudinal regulation of the follower vehicle is essential for the driving quality of the two-vehicle platoon. Focusing on this problem, a novel longitudinal control method composing of a learning-based acceleration decision phase and an internal model-based acceleration tracking phase is proposed for the follower vehicle. In the acceleration decision phase, proper acceleration commands of the follower that adjusts the following distance converging to the target value are determined by a near-optimal acceleration policy which is obtained through an online reinforcement learning algorithm named neural dynamic programming. In the acceleration tracking phase, throttle and brake control commands that drive the vehicle as the decided acceleration are derived by an internal model control structure. The performance of our proposed method is verified by simulation experiments conducted with CarSim, an industry recognized vehicle dynamic simulator.

关键词： Autonomous following acceleration decision acceleration tracking neural dynamic programming internal model structure

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Realization of robust optimal control by dynamic neural-programming

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IFAC-PapersOnLine 2018年第13期51卷 468-473页

作者： Ballesteros-Escamilla, Mariana Chairez, Isaac Boltyanski, Vladimir G. Poznyak, Alexander Department of Automatic Control CINVESTAV-IPN Av. IPN 2508 San Pedro Zacatenco Mexico City07360 Mexico Department of Bioprocess UPIBI-IPN Av. Acueducto 550 La Laguna Ticoman Mexico City07340 Mexico CIMAT Guanajuato Mexico

This study solves a finite horizon optimal problem for linear systems with parametric uncertainties and bounded perturbations. The control solution considers the uncertain part of the system in the sub-optimal control solution by proposing a min-max problem solved by a dynamic neural programming approximate solution. The structure of the neural network was proposed to satisfy the charcateristics of the value function including possitiveness and continuity. The impact of the presence of bounded perturbation over the Hamiltonian maximization was analyzed in detail. The explicit learning law used to adjust the weights was obtained directly from the Hamilton-Jacobi-Bellman (HJB) approximate solution. The weights adjustment to the proposed algorithm is based on an on-line state dependent Riccati-like equation. A numerical simulation is presented to illustrate the results of the sub-optimal algorithm including its comparison against the classical linear regulator solved considering the non-perturbed system. © 2018

关键词： dynamic programming Linear systems neural networks dynamic neural networks Hamilton jacobi bellman Hamilton Jacobi Bellman equation neural dynamic programming Parametric uncertainties Robust optimal control Sub optimal algorithms Suboptimal control

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Realization of robust optimal control by dynamic neural-programming

Realization of robust optimal control by dynamic neural-prog...

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2nd IFAC Conference on Modelling, Identification and Control of Nonlinear Systems (MICNON)

作者： Ballesteros-Escamilla, Mariana Chairez, Isaac Boltyanski, Vladimir G. Poznyak, Alexander IPN CINVESTAV Dept Automat Control AV IPN 2508 Mexico City 07360 DF Mexico IPN UPIBI Dept Bioproc Av Acueducto 550 Mexico City 07340 DF Mexico CIMAT Guanajuato Mexico

关键词： neural dynamic programming dynamic neural Networks Hamilton Jacobi Bellman equation Sub-optimal control

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