A learning controller of nonhonolomic robot in real-time based on support vector machine(SVM)is presented.The controller includes two parts:one is kinematic controller based on nonlinear law,and the other is dynamic controller based on SVM.The kinematic controller is aimed to provide desired velocity which can make the steering system stable.The dynamic controller is aimed to transform the desired velocity to control torque.The parameters of the dynamic system of the robot are estimated through SVM learning algorithm according to the training data of sliding windows in real time.The proposed controller can adapt to the changes in the robot model and uncertainties in the environment.Compared with artificial neural network(ANN)controller,SVM controller can converge to the reference trajectory more quickly and the tracking error is smaller.The simulation results verify the effectiveness of the method proposed.
Conventional Pd/γ-A12O3 methane sensors are easily poisoned in a sulfur-containing atmosphere, with a subsequent decrease in sensitivity and the working life of methane sensors. We mainly investigated the effect of nanotechnology and a cerium co-catalyst on the stability and anti-sulfur performance of methane sensors. In our experiment, an anti-sulfur methane sensor was prepared by immersing cerium-containing γ-alumina nanometer elements into a Pt-Pd bimetallic nanometer catalyst. The experi- ment about the sensitivity and stability performance of different catalytic methane sensors indicate that sensitivity, decreased by catalyst sulfur poisoning, is improved significantly by adding cerium to the vector. As well, the long-term operational stability of methane sensors increased significantly.
WANG Ying, TONG Minming, ZHANG Tao School of Information and Electrical Engineering, China University of Mining & Technology, Xuzhou 221008, China
