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DHP Adaptive Critic Motion Control of Autonomous Wheeled Mobile Robot

DHP Adaptive Critic Motion Control of Autonomous Wheeled Mob...

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ieee symposium on Adaptive dynamic programming and reinforcement learning, (ADPRL)

作者： Wei-Song Lin Ping-Chieh Yang Department and Institute of Electrical Engineering National Taiwan University Taipei Taiwan

Autonomous drive of wheeled mobile robot (WMR) needs implementing velocity and path tracking control subject to complex dynamical constraints. Conventionally, this control design is obtained by analysis and synthesis of the WMR system. This paper presents the dual heuristic programming (DHP) adaptive critic design of the motion control system that enables WMR to achieve the control purpose simply by learning through trial. The design consists of an adaptive critic velocity neuro-control loop and a posture neuro-control loop. The neural weights in the velocity neuro-controller (VNC) are corrected with the DHP adaptive critic method. The designer simply expresses the control objective with a utility function. The VNC learns by sequential optimization to satisfy the control objective. The posture neuro-controller (PNC) approximates the inverse velocity model of WMR so as to map planned positions to desired velocities. Supervised drive of WMR in variant velocities supplies training samples for the PNC and VNC to setup the neural weights. In autonomous drive, the learning mechanism keeps improving the PNC and VNC. The design is evaluated on an experimental WMR. The excellent results make it certain that the DHP adaptive critic motion control design enables WMR to develop the control ability autonomously.

关键词： Programmable control Adaptive control Motion control Mobile robots Velocity control Control design Control system synthesis Robot programming Control systems Design methodology

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Bayesian active learning with basis functions

Bayesian active learning with basis functions

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ieee symposium on Adaptive dynamic programming and reinforcement learning, (ADPRL)

作者： Ilya O. Ryzhov Warren B. Powell Operations Research and Financial Engineering Princeton University Princeton NJ USA

A common technique for dealing with the curse of dimensionality in approximate dynamic programming is to use a parametric value function approximation, where the value of being in a state is assumed to be a linear combination of basis functions. Even with this simplification, we face the exploration/exploitation dilemma: an inaccurate approximation may lead to poor decisions, making it necessary to sometimes explore actions that appear to be suboptimal. We propose a Bayesian strategy for active learning with basis functions, based on the knowledge gradient concept from the optimal learning literature. The new method performs well in numerical experiments conducted on an energy storage problem.

关键词： Tin Function approximation Bayesian methods Covariance matrix Mathematical model dynamic programming

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Adaptive Neural Network Observer-Based Fault Tolerant Control for Partially Unknown Nonlinear Systems via Adaptive dynamic programming 13

Adaptive Neural Network Observer-Based Fault Tolerant Contro...

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13th ieee international Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2023

作者： Zeng, Chujian Zhao, Bo Liu, Derong School of Automation Guangdong University of Technology Guangzhou510006 China School of Systems Science Beijing Normal University Beijing100875 China School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen518000 China

ISBN: (纸本)9798350315196

This article presents an adaptive neural network (NN) observer-based fault tolerant control (FTC) method for partially unknown nonlinear systems with actuator faults via adaptive dynamic programming. By using an adaptive NN observer, the system states, unknown system dynamics and faults are estimated accurately. An augmented system is constructed to transform the FTC problem into an optimal control problem. Through adopting a critic NN, the Hamilton-Jacobi-Bellman equation is solved for the augmented system. Then, the approximate optimal control policy is obtained, i.e., the FTC policy is acquired. The Lyapunov's direct method is utilized to ensure that the closed-loop nonlinear system is uniformly ultimately bounded. A simulation example shows the FTC performance of the proposed method. © 2023 ieee.

关键词： reinforcement learning

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2022 ieee 33rd Annual international symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2022

2022 IEEE 33rd Annual International Symposium on Personal, I...

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33rd ieee Annual international symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2022

ISBN: (纸本)9781665480536

The proceedings contain 237 papers. The topics discussed include: robust device position and pose detection using visible light without model knowledge: a branch-structured residual learning method;access point clustering in cell-free massive MIMO using multi-agent reinforcement learning;joint data and model driven channel-free signal detection based learned factor graph;sum-rate maximization in RIS-aided wireless-powered D2D communication networks;physical layer security in spherical-wave channel using massive MIMO;spatially-coupled faster-than-Nyquist signaling;indoor localization with CSI fingerprint utilizing depthwise separable convolution neural network;a hybrid machine learning based model for congestion prediction in mobile networks;mobile traffic forecasting for network slices: a federated-learning approach;a vector-based dynamic programming approach for small cell placement in dense urban;spatiotemporal graph attention networks for urban traffic flow prediction;deep learning based minimum length scheduling for half duplex wireless powered communication networks;on the effectiveness of semantic addressing for wake-up radio-enabled wireless sensor networks;and physical layer authentication based on continuous channel polarization response in low snr scenes.

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PhD Forum Abstract: Diffusion-based Task Scheduling for Efficient AI-Generated Content in Edge Networks 23

PhD Forum Abstract: Diffusion-based Task Scheduling for Effi...

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23rd ACM/ieee international Conference on Information Processing in Sensor Networks, IPSN 2024

作者： Xu, Changfu Hong Kong Baptist University Hong Kong and BNU-HKBU United International College Zhuhai China

ISBN: (纸本)9798350362015

The Artificial Intelligence-Generated Content (AIGC) technique has gained significant popularity in creating diverse content. However, the current deployment of AIGC services is a centralized framework, thus leading to high response times. To address this issue, we propose a diffusion-based task scheduling method that considers the integration of the diffusion model, Deep reinforcement learning (DRL), and Mobile Edge Computing (MEC) technique to improve the AIGC efficiency. This challenges efficient server selection without prior information in dynamic MEC systems. We formulate our problem as an online integer linear programming problem aiming to minimize task offloading delay. Furthermore, we propose a novel AIGC Task Scheduling (DDRL-ATS) algorithm based on Diffusion DRL (DDRL) that effectively addresses this problem. The DDRL-ATS algorithm achieves efficient AIGC tailored for heterogeneous MEC environments. Additionally, an online Adaptive Multi-server Selection and Allocation (DDRL-AMSA) algorithm based on DDRL is proposed to further enhance the AIGC efficiency. Moreover, our DDRL-AMSA algorithm achieves near-optimal solutions within approximate linear time complexity bounds. Finally, experimental results validate the effectiveness of our method by showcasing at least a reduction of 13.54% in task offloading delay compared to state-of-the-art methods. © 2024 ieee.

关键词： Deep reinforcement learning

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Neural-network-based reinforcement learning controller for nonlinear systems with non-symmetric dead-zone inputs

Neural-network-based reinforcement learning controller for n...

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ieee symposium on Adaptive dynamic programming and reinforcement learning, (ADPRL)

作者： Xin Zhang Huaguang Zhang Derong Liu Yongsu Kim School of Information Science and Engineering Northeastern University Shenyang Liaoning China Department of Electrical and Computer Engineering University of Illinois Chicago Chicago IL USA

A novel adaptive-critic-based NN controller using reinforcement learning is developed for a class of nonlinear systems with non-symmetric dead-zone inputs. The adaptive critic NN controller uses two NNs: the critic NN is used to approximate the strategic utility function, and the output of action NN is used to approximate the unknown nonlinear function and to minimize the strategic utility function. The tuning of the NNs is performed online without an explicit offline learning phase. The uniformly ultimate boundedness of the close-loop tracking error is derived by using using the Lyapunov method. Finally, a numerical example is included to show the effectiveness of the theoretical results.

关键词： learning Nonlinear control systems Control systems Nonlinear systems Neural networks Adaptive control Programmable control Lyapunov method Actuators Servomechanisms

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An Autonomous Search Utility for Pervasive Storage Virtualization

An Autonomous Search Utility for Pervasive Storage Virtualiz...

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ieee international Conference on System of Systems Engineering

作者： Liu, Lei Sun Microsyst Inc Santa Clara CA 95054 USA

ISBN: (纸本)9781424421725

Storage virtualization provides abstraction to pervasive storage graphs. Data management operations require pervasive search utilities to discovery entities and services. In search methods, algorithmic complexity, such as memory bound problems, error convergence issues and supervised training is prohibitive for large state and solution spaces or high dimensional state spaces. In addition, among popular search strategies, heuristic algorithms may not guarantee search optimality or are hard to approximate it without close formed utility functions. Furthermore, model-building algorithms require large computation per iteration since every update needs to compute sums over the entire state space. Hence, dynamic search function and control optimization are major primitives to construct search utilities for stochastic system processes to ensure converged resource accesses. This research focuses on optimization of general search solution methods and proposes a formal search utility framework and algorithms rooted from reinforcement learning (RL) and dynamic programming (DP) techniques. To reduce space complexity within large dimension search spaces, a memory-less Q learning is augmented with self-organized index structure and algorithms for exact state-action value function mapping to optimize search procedures for optimal state actions. Data parallelization is ensured with this page-based index value mapping function. Hence, time complexity is reduced with threaded search parallelism. Convergence analysis and error estimation are presented for numeric and information evaluation. Finally, simulation and learning results are presented and discussed.

关键词： State-space methods Error analysis Search methods Management training Heuristic algorithms Control systems Stochastic systems Optimization methods learning dynamic programming

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A comparison of approximate dynamic programming techniques on benchmark energy storage problems: Does anything work?

A comparison of approximate dynamic programming techniques o...

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ieee symposium on Adaptive dynamic programming and reinforcement learning, (ADPRL)

作者： Daniel R. Jiang Thuy V. Pham Warren B. Powell Daniel F. Salas Warren R. Scott Department of Electrical & Electronics Enzineering Dehradun India Graphic Era University Dehradun India School of Rlectronics Dehradun India Graphic Era Hill University Bhimtal India

As more renewable, yet volatile, forms of energy like solar and wind are being incorporated into the grid, the problem of finding optimal control policies for energy storage is becoming increasingly important. These sequential decision problems are often modeled as stochastic dynamic programs, but when the state space becomes large, traditional (exact) techniques such as backward induction, policy iteration, or value iteration quickly become computationally intractable. approximate dynamic programming (ADP) thus becomes a natural solution technique for solving these problems to near-optimality using significantly fewer computational resources. In this paper, we compare the performance of the following: various approximation architectures with approximate policy iteration (API), approximate value iteration (AVI) with structured lookup table, and direct policy search on a benchmarked energy storage problem (i.e., the optimal solution is computable).

关键词： Function approximation Energy storage Benchmark testing Mathematical model Approximation algorithms Equations

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High-order local dynamic programming

High-order local dynamic programming

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ieee symposium on Adaptive dynamic programming and reinforcement learning, (ADPRL)

作者： Yuval Tassa Emanuel Todorov Interdisciplinary Center of Neural Computation Hebrew University Jerusalem Israel Applied Mathematics and Computer Science & Engineering University of Washington Seattle USA

We describe a new local dynamic programming algorithm for solving stochastic continuous Optimal Control problems. We use cubature integration to both propagate the state distribution and perform the Bellman backup. The algorithm can approximate the local policy and cost-to-go with arbitrary function bases. We compare the classic quadratic cost-to-go/linear-feedback controller to a cubic cost-to-go/quadratic policy controller on a 10-dimensional simulated swimming robot, and find that the higher order approximation yields a more general policy with a larger basin of attraction.

关键词： Approximation methods Heuristic algorithms Mathematical model Equations Trajectory dynamic programming Noise

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Near-optimality bounds for greedy periodic policies with application to grid-level storage

Near-optimality bounds for greedy periodic policies with app...

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ieee symposium on Adaptive dynamic programming and reinforcement learning, (ADPRL)

作者： Yuhai Hu Boris Defourny Department of Industrial & Systems Engineering Lehigh University USA

This paper is concerned with periodic Markov Decision Processes, as a simplified but already rich model for nonstationary infinite-horizon problems involving seasonal effects. Considering the class of policies greedy for periodic approximate value functions, we establish improved near-optimality bounds for such policies, and derive a corresponding value-iteration algorithm suitable for periodic problems. The effectiveness of a parallel implementation of the algorithm is demonstrated on a grid-level storage control problem that involves stochastic electricity prices following a daily cycle.

关键词： Silicon Markov processes Approximation algorithms Approximation methods Modeling Electricity dynamic programming

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