Adaptive PI Hermite neural control for MIMO uncertain nonlinear systems

[[abstract]]This paper presents an adaptive PI Hermite neural control (APIHNC) system for multi-input multi-output (MIMO) uncertain nonlinear systems. The proposed APIHNC system is composed of a neural controller and a robust compensator. The neural controller uses a three-layer Hermite neural network (HNN) to online mimic an ideal controller and the robust compensator is designed to eliminate the effect of the approximation error introduced by the neural controller upon the system stability in the Lyapunov sense. Moreover, a proportional–integral learning algorithm is derived to speed up the convergence of the tracking error. Finally, the proposed APIHNC system is applied to an inverted double pendulums and a two-link robotic manipulator. Simulation results verify that the proposed APIHNC system can achieve high-precision tracking performance. It should be emphasized that the proposed APIHNC system is clearly and easily used for real-time applications.[[notice]]補正完畢[[incitationindex]]SCI[[booktype]]紙本[[booktype]]電子

A Practical and Conceptual Framework for Learning in Control

We propose a fully Bayesian approach for efficient reinforcement learning (RL) in Markov decision processes with continuous-valued state and action spaces when no expert knowledge is available. Our framework is based on well-established ideas from statistics and machine learning and learns fast since it carefully models, quantifies, and incorporates available knowledge when making decisions. The key ingredient of our framework is a probabilistic model, which is implemented using a Gaussian process (GP), a distribution over functions. In the context of dynamic systems, the GP models the transition function. By considering all plausible transition functions simultaneously, we reduce model bias, a problem that frequently occurs when deterministic models are used. Due to its generality and efficiency, our RL framework can be considered a conceptual and practical approach to learning models and controllers whe

Nonlinear Systems: Asymptotic Methods, Stability, Chaos, Control, And Optimization

[No abstract available]201

Stable and robust fuzzy control for uncertain nonlinear systems

Author name used in this publication: F. H. F. LeungAuthor name used in this publication: P. K. S. Tam2000-2001 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Human-Machine Co-Learning Design in Controlling a Double Inverted Pendulum

Effective human-machine interaction is an essential goal of the design of human-machine systems. This, however, is often constrained by the fundamental limitation of the human neural control and inability of the machine’s control system in adapting to the time-varying characteristics of the human operator. It is desirable that the control system of the machine can learn to optimize its performance under the behavior change of the human operator. This thesis is aimed at enhancing the machine’s control system with learning capabilities. Specifically, an adaptive control framework is proposed that enables human-machine co-learning through the interaction between the machine and the human operator. A dual inverted pendulum system is introduced as an experimental platform. Simulations are performed to implement the control of the two-joint inverted pendulum using the human-machine co-learning controller. The results are compared with those using a controller without learning ability. The parameters of the two controllers are adjusted to explore the effect of the value changing of each parameter on the control performance. Simulation results indicate the superior performance of the proposed adaptive controller design framework

Deep neural network approximations for the stable manifolds of the Hamilton-Jacobi equations

As the Riccati equation for control of linear systems, the Hamilton-Jacobi-Bellman (HJB) equations play a fundamental role for optimal control of nonlinear systems. For infinite-horizon optimal control, the stabilizing solution of HJB equation can be represented by the stable manifold of the associated Hamiltonian system. In this paper, we study the neural network (NN) semiglobal approximation of the stable manifold. The main contribution includes two aspects: firstly, from the mathematical point of view, we rigorously prove that if an approximation is sufficiently close to the exact stable manifold of the HJB equation, then the corresponding control derived from this approximation is near optimal. Secondly, we propose a deep learning method to approximate the stable manifolds, and then numerically compute optimal feedback controls. The algorithm is devised from geometric features of the stable manifold, and relies on adaptive data generation by finding trajectories randomly in the stable manifold. The trajectories are found by solving two-point boundary value problems (BVP) locally near the equilibrium and extending the local solution by initial value problems (IVP) for the associated Hamiltonian system. A number of samples are chosen on each trajectory. Some adaptive samples are selected near the points with large errors after the previous round of training. Our algorithm is causality-free basically, hence it has a potential to apply to various high-dimensional nonlinear systems. We illustrate the effectiveness of our method by stabilizing the Reaction Wheel Pendulums.Comment: The algorithm is modified. The main point is that the trajectories on stable manifold are found by a combination of two-point BVP near the equilibrium and initial value problem far away from the equilibrium. The algorithm becomes more effectiv

Meta Reinforcement Learning with Latent Variable Gaussian Processes

Learning from small data sets is critical in many practical applications where data collection is time consuming or expensive, e.g., robotics, animal experiments or drug design. Meta learning is one way to increase the data efficiency of learning algorithms by generalizing learned concepts from a set of training tasks to unseen, but related, tasks. Often, this relationship between tasks is hard coded or relies in some other way on human expertise. In this paper, we frame meta learning as a hierarchical latent variable model and infer the relationship between tasks automatically from data. We apply our framework in a model-based reinforcement learning setting and show that our meta-learning model effectively generalizes to novel tasks by identifying how new tasks relate to prior ones from minimal data. This results in up to a 60% reduction in the average interaction time needed to solve tasks compared to strong baselines.Comment: 11 pages, 7 figure

Dynamic response of an inverted pendulum system in water under parametric excitations for energy harvesting : a conceptual approach

In this paper, we have investigated the dynamic response, vibration control technique, and upright stability of an inverted pendulum system in an underwater environment in view point of a conceptual future wave energy harvesting system. The pendulum system is subjected to a parametrically excited input (used as a water wave) at its pivot point in the vertical direction for stabilization purposes. For the first time, a mathematical model for investigating the underwater dynamic response of an inverted pendulum system has been developed, considering the effect of hydrodynamic forces (like the drag force and the buoyancy force) acting on the system. The mathematical model of the system has been derived by applying the standard Lagrange equation. To obtain the approximate solution of the system, the averaging technique has been utilized. An open loop parametric excitation technique has been applied to stabilize the pendulum system at its upright unstable equilibrium position. Both (like the lower and the upper) stability borders have been shown for the responses of both pendulum systems in vacuum and water (viscously damped). Furthermore, stability regions for both cases are clearly drawn and analyzed. The results are illustrated through numerical simulations. Numerical simulation results concluded that: (i) The application of the parametric excitation control method in this article successfully stabilizes the newly developed system model in an underwater environment, (ii) there is a significant increase in the excitation amplitude in the stability region for the system in water versus in vacuum, and (iii) the stability region for the system in vacuum is wider than that in water

Shimyureta to jikki o mochiita haiburiddo-gata kikai gakushuho ni kansuru kenkyu

制度:新 ; 報告番号:甲2816号 ; 学位の種類:博士(工学) ; 授与年月日:2009/2/25 ; 早大学位記番号:新503