3. Design of neural network robust controller In this section, a NN compensator controller serving as a compensator for CTC is considered and designed in detail. Using control law (8), closed-loop system becomes:(9)e¨+Kve˙+Kpe=ρ(xe)+d¯+M0-1τc.Supposed that state vector is define...
The neural network L-two-gain robust control for a class of uncertain systems一类不确定系统的神经网络L2--增益鲁棒控制L2--增益鲁棒控制非线性系统反步法动态面控制方法神经网络对于一类具有三角结构的单输入单输出的不确定非线性系统, 用反步法(backstepping)和动态面控制方法(dynamic surface control technique)...
BAI Ping. Robust Neural-Network Compensating Control for Robot Manipulator Based on Computed Torque Control[J].Control Theory and Applications,2001,(06):897-901.doi:10.3969/j.issn.1000-8152.2001.06.018.Bai P,Fang T J,Ge Y J. Robust neural-network compensating control for robot manipulator based...
A robust neural network controller (NNC) is presented for tracking control of underwater vehicles with uncertainties. The controller is obtained by using backstepping technique and Lyapunov function design in combination with neural network identification. Modeling errors and environmental disturbances are co...
This paper presents a neural network-based robust finite-time H ∞ control design approach for a class of nonlinear Markov jump systems (MJSs). The syste... X Luan,L Fei,S Peng - 《Circuits Systems & Signal Processing》 被引量: 75发表: 2010年 Control of Uncertain Plants with Unknown De...
This paper proposes a neural network-based (NN-based) data-driven iterative learning control (ILC) algorithm for the tracking problem of nonlinear single-input single-output (SISO) discrete-time systems with unknown models and repetitive tasks. The control objective is to make the output of the ...
27、r below. Do a few iterations of sampling in the top level RBM- Adjust the weights in the top-level RBM. Do a stochastic top-down pass2.Adjust the bottom-up weights to be good at reconstructing the feature activities in the layer above.Show the movie of the network generating digits...
This paper proposes a new robust control method for space robot by using neural network. A radial-basis-function (RBF) neural network is included to compensate for the system uncertainties. The parameters of the neural network are adapted on-line according to derived learning algorithms using Lyapu...
control strategy. This means that no explicit model of the state-transition dynamics is estimated during computation of the policy. Thus for Q-learning, particular importance is placed on finding a good estimator of the Q-function, and in this paper, we use a deep neural network to estimate ...
A neural network walks into a lab: towards using deep nets as models for human behavior. Preprint at http://arxiv.org/abs/2005.02181 (2020). Peterson, J., Battleday, R., Griffiths, T. & Russakovsky, O. Human uncertainty makes classification more robust. In 2019 IEEE/CVF International ...