一类复式自适应Bang—Bang控制系统的研究

提出由Bang-Bang控制器、PID控制器、辨识装置、参考模型组成的复式自适应Bang-Bang控制系统,可用于参数未知或时变的时滞对象.具体介绍控制系统结构和数字仿真.     

基于Bang—Bang的PID参数自整定算法及其应用

在Astrom提出的PID参数自整定方法的基础上,经整理、完善后,得出一类新的不对称有均值的Bang-Bang控制来识别系统的临界点,并算出调节器的PID参数。文未介绍了用于工业锅炉控制的情况。     

弹载电液舵机的Bang—Bang＋PID双模控制研究

本文结合一弹载电液舵机系统,介绍了电液位置伺服系统的工作原理。经过推导,建立了该舵机的数学模型。在MATLAB／Simulink中搭建了舵机的仿真模型,分别对PID控制和Bang—Bang控制进行了仿真,仿真结果表明这两种控制方法无法获得满意的控制效果。为取得良好的控制效果,将PID控制和Bang－Bang控制相结合,设计TBang-Bang＋PID双模控制器,提高了系统的控制效果。     

天才的IT用语大补贴 IT English in The Big Bang Theory

四个天才宅男科学家和一个性感美女服务生之间会发生怎样的故事呢？就让《生活大爆炸》（The Big Bong Theory）来告诉你吧。     

Bang–bang control model with optimistic value criterion for uncertain switched systems

This paper studies an optimal control problem for uncertain switched linear systems with subsystems perturbed by uncertainty. A model for this problem is investigated with optimistic value criterion. The goal is to jointly design a deterministic switching law and a continuous feedback to optimize an uncertain objective function. A two-stage algorithm is applied to handle such model. In the first stage, the maximum value of the objective function and the bang–bang control are obtained under fixed switching instants, and in the second stage, GA and PSO algorithm are used to get the optimal switching instants, respectively. An example is shown to validate the method.     

Optimal PID Control of Spatial Inverted Pendulum With Big Bang – Big Crunch Optimization

As the extension of the linear inverted pendulum (LIP) and planar inverted pendulum (PIP), this paper proposes a novel spatial inverted pendulum (SIP). The SIP is the most general inverted pendulum (IP) than any existing IP. The model of the SIP is presented for the first time. The SIP inherits all the characteristics of the LIP and the PIP, which is a nonlinear, unstable and underactuated system. The SIP has five degrees of motion freedom and three control forces. Thus, it is a multiple-input and multiple-output (MIMO) system with nonlinear dynamics. To realize the spatial trajectory tracking of the SIP, the control structure with five PID controllers will be designed. The parameter tuning of the multiple PIDs is a challenging work for the proposed SIP model. To alleviate the difficulties of the parameter tuning for the multiple PID controllers, optimal PIDs can be achieved with the help of Big Bang-Big Crunch (BBBC) optimization. The BBBC algorithm can successfully optimize the parameters of the multiple PID controllers with high convergence speed. The optimization performance index of the BBBC algorithm is compared with that of the particle swarm optimization (PSO). Simulation results certify the rightness and effectiveness of the proposed control and optimization methods.      

情定七夕“音”你而在 Bang & Olufsen甄选七夕佳礼

正情定织女牛郎相会之时,纵隔千里亦相知。一"声"一世一双人,丹麦奢华生活品牌BangOlufsen为有情人甄选七夕佳礼,见证心动与相守的每时每刻。当温柔的粉色加入了Beoplay E8无线充电款(即2.0)耳机的豪华套餐。无线耳机、无线充电盒、无线充电板三者相合,是真无线产品的完美诠释,也是现代独立女性的挚爱之选。     

豪华车“爱上”高级音响系统——2007年奥迪S8配置Bang & Olufsen高级音响系统

一直以来,caraudio杂志都在坚持做原车音响系统测评。不管其配置有多高级,较之汽车售后市场的同类产品,它总是略逊一筹。我们能够轻而易举看出它们二者之间的差异,但随着时间的推移,它们之间的差距正在日益缩少。     

State-local optimality of a bang–bang trajectory: a Hamiltonian approach

We give a sufficient condition for a bang–bang extremal to be a strong local optimizer for the minimum time problem with fixed endpoints. We underline that the conditions imply that the optimum is local with respect to the state and not necessarily to the final time. Moreover, it is given through a finite-dimensional minimization problem, hence is suited for numerical verification. A geometric interpretation through the projection of the Hamiltonian flow on the state space is also given.     

Optimal strategies for biomass productivity maximization in a photobioreactor using natural light

We address the question of the optimization of the microalgal biomass long term productivity in the framework of production in photobioreactors under the influence of day/night cycles. For that, we propose a simple bioreactor model accounting for light attenuation in the reactor due to biomass density and we obtain the control law that optimizes productivity over a single day through the application of Pontryagin’s maximum principle. The dilution rate is the main control, the input concentration being only used as the secondary control to maintain the substrate concentration high. An important constraint on the obtained solution is that the biomass in the reactor should be at the same level at the beginning and at the end of the day so that the same control can be applied everyday and optimizes some form of long term productivity. Several scenarios are possible depending on the microalgae’s strain parameters and the maximal admissible value of the dilution rate: bang–bang or bang–singular–bang control or, if the growth rate of the algae is very strong in the presence of light, constant maximal dilution. A bifurcation diagram is presented to illustrate for which values of the parameters these different behaviors occur. Finally, a simple sub-optimal bang–bang strategy is proposed that numerically achieves productivity levels that almost match those of the optimal strategy.     

A modified Elman neural network-based power controller in mobile communications systems

It has been well recognized that power control is an important technique to combat with the harmful near-far effect as well as increase the maximum user capacity of direct sequence code division multiple access (DS/CDMA) cellular systems. In this paper, we propose a modified Elman neural network (MENN)-based power control scheme, which can regulate the received power level at the base station. Unlike the conventional “bang–bang” and fuzzy logic power control, our MENN-based controller first identifies the inverse dynamical characteristics of mobile channel by adaptive on-line learning. The inverse channel model is then employed for power regulation to reduce large overshoots and shorten long rise time. Simulations show that the fluctuation of controlled received power levels can be smoothed with small channel tracking errors. Acknowledgments The authors would like to thank the anonymous reviewers for their insightful comments and constructive suggestions that have improved this paper.     

Improvements in graphite-based X-ray mask fabrication for ultradeep X-ray lithography

Hard x-ray masks for ultradeep x-ray lithography (UDXRL) at synchrotron radiation sources, such as the Advanced Photon Source, require a gold absorber thickness of 20–100 m on a low-Z substrate, such as silicon, graphite, or beryllium. Graphite sheets of 0.5 mm were used for the fabrication of x-ray masks by standard optical lithography using a SU-8 photoresist. The conductivity of graphite is sufficient to perform electroplating directly without the need for a metal-plating base layer. Gold electroforming was used to deposit a 85-m-thick patterned absorber layer. The masks were used for UDXRL using hard x-rays at the Advanced Photon Source.This work has been supported by the U.S. Department of Energy, Office of Science, under Contract No. W-31–109-ENG-38, by AFOSR Grant 49620–00–1-0088, and by DUSD (S&T) under the Innovative Microwave Vacuum Electronics MURI program managed by the AFOSR under grant F49620–99–1-0297.     

Quantum correlations of identical particles subject to classical environmental noise

In this work, we propose a measure for the quantum discord of indistinguishable particles, based on the definition of entanglement of particles given in Wiseman and Vaccaro (Phys Rev Lett 91:097902, 2003. doi: 10.1103/PhysRevLett.91.097902). This discord of particles is then used to evaluate the quantum correlations in a system of two identical bosons (fermions), where the particles perform a quantum random walk described by the Hubbard Hamiltonian in a 1D lattice. The dynamics of the particles is either unperturbed or subject to a classical environmental noise—such as random telegraph, pink or brown noise. The observed results are consistent with those for the entanglement of particles, and we observe that on-site interaction between particles have an important protective effect on correlations against the decoherence of the system.     

Analysis of the Hodgkin-Huxley equations with noise: the effects of noise on chaotic neurodynamics

The chaotic behavior of Hodgkin-Huxley equations modulated by noise was studied by power spectra, correlation exponents, and information entropies by changing noise amplitude and components. The power spectra of the membrane potential calculated by the equations showed two major peaks at 105 and 115Hz. The noise component of compressing dynamics (CDN) enhanced the amplitude of the 105Hz component and also increased its frequency. The correlation exponent of the correlation integral, which was calculated with a scale size below the noise amplitude, was almost the same as the degree of freedom of the system (=4) when CDN was applied. Contrary to this, the noise component of extending dynamics (EDN) increased the exponent only up to 2.5–3.0. The information entropy shows that both CDN and EDN produced noise-induced order (NIO). The effect of EDN was slightly larger than that of CDN. When comparing the effects of noise imposed on the variables of the Hodgkin-Huxley equations, V and m appeared to be related to CDN, while h and n appeared to be related to EDN. These results suggest that the fluctuations induced by EDN were restricted within some parts of submanifolds comprising the strange attractor, while those induced by CDN shook the orbit away from the submanifolds. This shake-out mechanism may have a significant role on real neurodynamics.This work was presented, in part, at the 8th International Symposium on Artificial Life and Robitics, Oita, Japan, January 24–26, 2003     

The Overdetermination Argument Revisited

In this paper I discuss a famous argument for physicalism – which some authors indeed regard as the only argument for it – the overdetermination argument. In fact it is an argument that does not establish that all the entities in the world are physical, but that all those events that enter into causal transactions with the physical world are physical. As mental events seem to cause changes in the physical world, the mind is one of those things that fall within the scope of the argument. Here I analyze one response to the overdetermination argument that has acquired some popularity lately, and which consists in saying that what mental events cause are not physical effects. I try to show that recent attempts to develop this response are not successful, but that there may be a coherent way of doing so. I also try to show that there seems to be a philosophical niche in which this way might fit.     

Inferring Finite Automata with Stochastic Output Functions and an Application to Map Learning

It is often useful for a robot to construct a spatial representation of its environment from experiments and observations, in other words, to learn a map of its environment by exploration. In addition, robots, like people, make occasional errors in perceiving the spatial features of their environments. We formulate map learning as the problem of inferring from noisy observations the structure of a reduced deterministic finite automaton. We assume that the automaton to be learned has a distinguishing sequence. Observation noise is modeled by treating the observed output at each state as a random variable, where each visit to the state is an independent trial and the correct output is observed with probability exceeding 1/2. We assume no errors in the state transition function.Using this framework, we provide an exploration algorithm to learn the correct structure of such an automaton with probability 1 – , given as inputs , an upper bound m on the number of states, a distinguishing sequence s, and a lower bound > 1/2 on the probability of observing the correct output at any state. The running time and the number of basic actions executed by the learning algorithm are bounded by a polynomial in ^–l, m, |s|, and (1/2-)^–1.We discuss the assumption that a distinguishing sequence is given, and present a method of using a weaker assumption. We also present and discuss simulation results for the algorithm learning several automata derived from office environments.