MODEL‐REFERENCE OUTPUT‐FEEDBACK SLIDING MODE CONTROLLER FOR A CLASS OF MULTIVARIABLE NONLINEAR SYSTEMS

This paper proposes an output‐feedback sliding mode control design for a class of uncertain multivariable plants with nonlinear disturbances. The approach used here is based on the control parameterization employed in model‐reference adaptive control. The disturbances are allowed to be unmatched and to depend not only on the plant output but also on its unmeas‐urable state. A less restrictive condition on the uncertainty of the high frequency gain matrix is also obtained.     

基于模糊逻辑系统的输出跟踪控制问题

针对一类未知的非线性互联大系统，设计间接自适应模糊控制器以实现跟踪控制，采用模糊控制，模糊逻辑逼近和模糊滑模控制相结合的方法，对维数较低的子系统未知动态和维数较高的互联项未知动态分别采用两类模糊规则进行逼近，对系统的外部干扰及模糊逼近误差采用模糊滑模控制予以抵消，基于Lyapunov方法实现模糊系统中的参数自适应律并在线调节，所设计的间接自适应控制器使系统在Lyapunov意义下稳定，且跟踪误差趋近于0，仿真结果表明了该设计方法的正确性。     

Sliding mode control for two-time scale systems: stability issues

The paper presents some results on global exponential stability of linear time invariant systems with different time scales. The full system is decomposed in two reduced ones by means of singular perturbations and a smooth approximation of a Variable Structure Control is synthesized for each of them using Reaching Law approach. The global closed loop stability is then proved for the whole system using Lyapunov methods and a particular state space decomposition. Moreover a literal form for ε∗ (a parameter which measures the minimum separation between time scales) is derived.     

非线性系统的神经网络自适应滤波器

针对一类具有未知非线性函数和未知虚拟系数的二阶非线性系统，提出了一种神经网络自适应滤波跟踪方法，用RBF神经网络估计出系统中不确定项，将神经网络与滑模控制方法相结合，用李雅普诺夫稳定性分析方法证明了滤波器内的所有信号均有界，选择的神经网络权值调整规律可以消除颤振，抑制外部随机干扰。     

一类非线性系统基于滑模原理的模糊自适应控制

针对一类非线性系统，设计出了一种自适应控制方案，用模糊逻辑逼近非线性函数，根据滑模原理及李雅普诺夫函数设计出自适应控制器。然后，用模糊推理进行修正，以克服一般滑模控制具有的抖动现象，保证闭环系统的稳定性和跟踪误差收敛于零的某邻域内。     

Adaptive sliding mode approach for learning in a feedforward neural network

An adaptive learning algorithm is proposed for a feedforward neural network. The design principle is based on the sliding mode concept. Unlike the existing algorithms, the adaptive learning algorithm developed does not require a prioriknowledge of upper bounds of bounded signals. The convergence of the algorithm is established and conditions given. Simulations are presented to show the effectiveness of the algorithm.     

Recent Advances in Disordered and Nanostructured Carbon Coatings for Superl Ubricity and Wearless Sliding

Increasingly more demanding and very stringent operating conditions envisioned for future mechanical and tribological systems will certainly require new materials and coatings that are superhard and at the same time self-lubricating.For example, dry machining is a much desired practice in manufacturing sector, but it is currently very difficult to realize mainly because of high friction and severe wear losses. However, recent advances in surface engineering and coating technologies may enable design and production of novel coatings architectures that can combine superhardness with self-lubricating properties in both the disordered or nanostructured forms. Recently developed nearly frictionless carbon films, ultrananocrystalline diamond and carbide derived carbon films can dramatically lower friction and at the same time reduce wear under very harsh sliding conditions. These coatings can be formulated in such a way that they can substantially increase the load-bearing capacity of sliding surfaces and hence improve their resistance to scuffing. It is also possible to design nano-composite coatings that can form self-replenishing and-lubricating tribofilms on their sliding surfaces and thus help increase the overall lubricity of these surfaces. In this paper, an overview of recent advances in disordered and nanostructured carbon films will be presented. Specific examples will be given to demonstrate the superior performance and durability of such novel coatings under a very wide range of tribological conditions. The major emphasis is placed on super low friction carbon films. The fundamental tribological mechanisms that control their exceptional friction and wear behaviors are also discussed.     

Variable structure control of a class of uncertain systems

This brief paper proposes a method for tuning the parameters of a variable structure controller. The approach presented extracts the error at the output of the controller and applies a nonlinear tuning law using this error measure. The adaptation mechanism drives the state tracking error vector to the sliding hypersurface and maintains the sliding mode. In the simulations, the approach presented has been tested on the control of Duffing oscillator and the analytical claims have been justified under the existence of measurement noise, uncertainty and large nonzero initial errors.     

A study of contact-start-stop wear tracks by TOF SIMS

In magnetic hard disk drive system, an ultra thin layer of lubricant is coated to the thin film media surface to prevent wear. Under the condition of relative motion, the displacement and replenishment of the lubricant at the head and media contact area are the factors that control the friction and wear behavior of the system. In this study, we investigate the sliding wear disk surface prepared by contact-start-stop (CSS) test using TOF SIMS (Time of Flight Secondary Ion Mass Spectrometry). TOF SIMS is a power tool for surface analysis with both high spatial and high mass resolution. Our investigations show that the lubricant thickness variation of the disk media at the contact area can be captured by sharp ion map images of TOF SIMS, and the thickness can be inferred based on the relative ion fragment intensity. In addition, the composition variation of the slider material and the magnetic layer materials can also be monitored. Finally the sliding effect is analyzed.     

Improved single neuron controller for multivariable stochastic systems with non-Gaussianities and unmodeled dynamics

In this paper, a new adaptive control approach is presented for multivariate nonlinear non-Gaussian systems with unknown models. A more general and systematic statistical measure, called (h,?)$(h,?)$-entropy, is adopted here to characterize the uncertainty of the considered systems. By using the “sliding window” technique, the non-parameter estimate of the (h,?)$(h,?)$-entropy is formulated. Then, the improved neuron based controllers are developed for multivariate nonlinear non-Gaussian systems by minimizing the entropies of the tracking errors in closed loops. The condition to guarantee the strictly decreasing entropy of tracking error is presented. Moreover, the convergence in the mean-square sense has been analyzed for all the weights in the neural controllers. Finally, the comparative simulation results are presented to show that the performance of the proposed algorithm is superior to that of PID control strategy.