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Reinforcement-Based Robust Variable Pitch Control of Wind Turbines

IEEE ACCESS 2020年 8卷 20493-20502页

作者： Chen, Peng Han, Dezhi Tan, Fuxiao Wang, Jun Shanghai Maritime Univ Dept Comp Sci & Technol Shanghai 200120 Peoples R China Univ Cent Florida Dept ECE Orlando FL 32816 USA

Due to the influence of wind speed disturbance, there are some uncertain phenomena in the parameters of the nonlinear wind turbine model with time in an actual working environment. In order to mitigate the side effects of uncertainties in speed models of wind turbines, researchers have designed a variety of controllers in recent years. However, traditional control methods require more knowledge of dynamics. Therefore, based on reinforcement learning and system state data, a robust wind turbine controller that adopts adaptive dynamic programming (ADP) is proposed. The ADP algorithm is a combination of Temporal-Difference (TD) algorithm and actor-critic structure, which can guarantee the rotor speed is stable around the rated value to indirectly adjust the wind energy utilization coefficient by changing the pitch angle in the area of high wind speed and achieve online learning in real-time. In addition, the variation of the pitch angle command of the proposed controller is relatively gradual, which can reduce the energy consumption of the variable pitch actuator, and extend the service life of the equipment. Finally, the wind speed model is simulated by combined wind speed based on Weibull distribution, the comprehensive simulation results show that the proposed controller has better control effect than some existing ones.

关键词： neural dynamic programming reinforcement learning robust control wind turbine system

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ε-Nash Equilibrium of Pursuer-Evader-Defender Missile Navigation dynamic Games

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UNMANNED SYSTEMS 2024年第0期

作者： Noriega-Marquez, Sebastian Hernandez-Sanchez, Alejandra Chairez, Isaac Poznyak, Alexander Ctr Invest & Estudios Avanzados IPN CINVESTAV IPN Dept Control Automat Mexico City 47368 Mexico Tecnol Monterrey Inst Adv Mat Sustainable Mfg Mexico City 14380 Mexico Tecnol Monterrey Inst Adv Mat Sustainable Mfg Zapopan 45210 JA Mexico

This research is dedicated to developing a min-max robust control strategy for a dynamic game involving pursuers, evaders, and defenders in a multiple-missile scenario. The approach employs neural dynamic programming, utilizing multiple continuous differential neural networks (DNNs). The competitive controller devised addresses the robust optimization of a joint cost function that relies on the trajectories of the pursuer-evader-defender system, accommodating an uncertain mathematical model while adhering to control restrictions. The dynamic programming min-max formulation facilitates robust control by accounting for bounded modeling uncertainties and external disturbances for each game component. The value function of the Hamilton-Jacobi-Bellman (HJB) equation is approximated by a DNN, enabling the estimation of the closed-loop formulation for the joint dynamic game with state restrictions. The controller's design is grounded in estimating the state trajectory under the worst possible uncertainties and perturbations, providing a robustness factor through the robust neural controller. The learning law class for the time-varying weights in the DNN is generated by studying the HJB partial differential equation for the missile motion for each player in the dynamic game. The controller incorporates the solution of the obtained learning laws and a time-varying Riccati equation, offering an online solution to the control implementation. A recurrent algorithm, based on the Kiefer-Wolfowitz method, adjusts the initial conditions for the weights to satisfy the final condition of the given cost function for the dynamic game. A numerical example is presented to validate the proposed robust control methodology, confirming the optimization solution based on the DNN approximation for Bellman's value function.

关键词： dynamic games missile unmanned operation neural dynamic programming navigation dynamics

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Renewable Energy Management Using Action Dependent Heuristic dynamic programming

Renewable Energy Management Using Action Dependent Heuristic...

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IEEE International Smart Cities Conference (ISC2)

作者： Sterling, Gulnaz Tyler, Benjamin Nazarbayev Univ Dept Comp Sci Astana Kazakhstan

ISBN: (纸本)9781538659595

With increases in global energy demand and the rapid consumption of fossil fuels, the use of green energy and more efficient energy management approaches are receiving serious attention. Our focus is on improving energy resource scheduling in smart buildings and homes to minimize cost, while meeting energy demand. Here, we present an approach using Action Dependent Heuristic dynamic programming (ADHDP) optimization for a smart home set-up using solar panels, wind turbines, and a storage battery. In this work, we trained and evaluated our ADHDP approach using different simulation scenarios with various amounts of available renewable energy. We then demonstrated via computer simulation that our approach is more effective in cost minimization compared to a standard rule-based method. A correlation between optimization improvement and available renewable energy was also confirmed by computer simulation in all scenarios.

关键词： Action Dependent Heuristic dynamic programming Smart Homes Smart Buildings Energy Resource Scheduling Renewable Energy neural dynamic programming

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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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A neural-network-based iterative GDHP approach for solving a class of nonlinear optimal control problems with control constraints

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neural COMPUTING & APPLICATIONS 2013年第2期22卷 219-227页

作者： Wang, Ding Liu, Derong Zhao, Dongbin Huang, Yuzhu Zhang, Dehua Chinese Acad Sci Inst Automat Key Lab Complex Syst & Intelligence Sci Beijing 100190 Peoples R China Univ Illinois Dept Elect & Comp Engn Chicago IL 60607 USA

In this paper, a novel neural-network-based iterative adaptive dynamic programming (ADP) algorithm is proposed. It aims at solving the optimal control problem of a class of nonlinear discrete-time systems with control constraints. By introducing a generalized nonquadratic functional, the iterative ADP algorithm through globalized dual heuristic programming technique is developed to design optimal controller with convergence analysis. Three neural networks are constructed as parametric structures to facilitate the implementation of the iterative algorithm. They are used for approximating at each iteration the cost function, the optimal control law, and the controlled nonlinear discrete-time system, respectively. A simulation example is also provided to verify the effectiveness of the control scheme in solving the constrained optimal control problem.

关键词： Adaptive critic designs Adaptive dynamic programming Approximate dynamic programming neural dynamic programming neural networks Optimal control Reinforcement learning

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Reinforcement Learning in Discrete neural Control of the Underactuated System

Reinforcement Learning in Discrete Neural Control of the Und...

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12th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2013

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

ISBN: (纸本)9783642386572

The article presents a new approach to the problem of a discrete neural control of an underactuated system, using reinforcement learning method to an on-line adaptation of a neural network. The controlled system is of the ball and beam type, which is the nonlinear dynamical object with the number of control signals smaller than the number of degrees of freedom. The main part of the neural control system is the actor-critic structure, that comes under the neural dynamic programming algorithms family, realised in the form of Dual Heuristic dynamic programming structure. The control system includes moreover the PD controller and the supervisory therm, derived from the Lyapunov stability theorem, that ensures stability. The proposed neural control system works on-line and does not require a preliminary learning. Computer simulations have been conducted to illustrate the performance of the control system.

关键词： Ball and Beam System Dual Heuristic dynamic programming neural dynamic programming neural Network Underactuated System Control Reinforcement Learning

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Online Learning Control for Hybrid Electric Vehicle

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Chinese Journal of Mechanical Engineering 2012年第1期25卷 98-106页

作者： LI Weimin XU Guoqing XU Yangsheng Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China Shenzhen Institutes of Advanced Technology Chinese Academy of SciencesShenzhen 518055China Department of Mechanical and Automation Engineering The Chinese University of Hong KongHong KongChina

Improvements in hybrid electric vehicle (HEV) fuel economy and emissions heavily depend on an efficient energy management strategy (EMS). However, the uncertainty of future driving conditions generally cannot be easily tackled in EMS design. Most existing EMSs act upon fixed parameters and cannot adapt to varying driving conditions. Therefore, they usually fail to fully explore the potential of these advanced vehicles. In this paper, a novel EMS design procedure based on neural dynamic programming (NDP) is proposed. The NDP is a generic online learning algorithm, which combines stochastic dynamic programming (SDP) and the temporal difference (TD) method. Instead of computing the utility function and optimal control actions through Bellman equations, the NDP algorithm uses two neural networks to approximate them. The weights of these neural networks are updated online by the TD method. It avoids the high computational cost that SDP suffers from and is suitable for real-time implementation. The main advantages of NDP EMS is that it does not rely on prior information related to future driving conditions, and can self-tune with a wide variance in operating conditions. The NDP EMS has been applied to “Qianghua-I”, a prototype of a parallel HEV, using a revolving drum test bench for verification. Experiment results illustrate the potential of the proposed EMS in terms of fuel economy and in keeping state of charge (SOC) deviations at a low level. The proposed research ensures the optimality of NDP EMS, as well as real-time applicability.

关键词： hybrid electric vehicle neural dynamic programming energy management strategy

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Adaptive dynamic programming for Finite-Horizon Optimal Control of Discrete-Time Nonlinear Systems with ε-Error Bound

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IEEE TRANSACTIONS ON neural NETWORKS 2011年第1期22卷 24-36页

作者： Wang, Fei-Yue Jin, Ning Liu, Derong Wei, Qinglai Chinese Acad Sci Inst Automat Key Lab Complex Syst & Intelligence Sci Beijing 100190 Peoples R China Univ Illinois Dept Elect & Comp Engn Chicago IL 60607 USA

In this paper, we study the finite-horizon optimal control problem for discrete-time nonlinear systems using the adaptive dynamic programming (ADP) approach. The idea is to use an iterative ADP algorithm to obtain the optimal control law which makes the performance index function close to the greatest lower bound of all performance indices within an epsilon-error bound. The optimal number of control steps can also be obtained by the proposed ADP algorithms. A convergence analysis of the proposed ADP algorithms in terms of performance index function and control policy is made. In order to facilitate the implementation of the iterative ADP algorithms, neural networks are used for approximating the performance index function, computing the optimal control policy, and modeling the nonlinear system. Finally, two simulation examples are employed to illustrate the applicability of the proposed method.

关键词： Adaptive critic designs adaptive dynamic programming approximate dynamic programming learning control neural control neural dynamic programming optimal control reinforcement learning

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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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