深海机器人推进电机系统的混沌控制

深海机器人推进电机系统中出现的混沌现象,直接影响深海机器人稳定性、可靠性和安全性.采用自适应控制技术对其混沌行为加以控制,对该方法的可行性和有效性进行了证明.设计和构造了易于工程实施的混沌控制器,用于深海机器人推进电机系统混沌控制.仿真实验表明,推进电机系统在自适应控制器的作用下可迅速脱离混沌状态,并进入持续稳定状态,控制效果明显.可以为深海机器人推进电机系统中可能出现的混沌运行行为提供控制策略和抑制预案,有利于混沌控制嵌入软件的开发,确保深海机器人稳定、可靠和安全地运行,具有一定的实用价值.

降雨触发滑坡的非线性模型与斜坡演化的混沌效应

This paper analyzes landsliding process by nonlinear theories, especially the influence mechanism of external factors (such as rainfall and groundwater) on slope evolution. The author investigates landslide as a consequence of the catastrophic slide of initially stationary or creeping slope triggered by a small perturbation. A fully catastrophe analysis is done for all possible scenarios when a continuous change is imposed to the control parameters. As the slip surface continues and erosion due to rainfall occurs, control parameters of the slip surface may evolve such that a previously stable slope may become unstable (e.g. catastrophe occurs), when a small perturbation is imposed. Thus the present analysis offers a plausible explanation to why slope failure occurs at a particular rainfall, which is not the largest in the history of the slope. It is found, by analysis on the nonlinear dynamical model of the evolution process of slope built, that the relationship between the action of external environment factors and the response of the slope system is complicatedly nonlinear. When the nonlinear action of slope itself is equivalent to the acting ability of external environment, the chaotic phenomenon appears in the evolution process of slope, and its route leading to chaos is realized with bifurcation of period-doublings. On the basis of displacement time series of the slope, a nonlinear dynamic model is set up by improved Backus generalized linear inversion theory in this paper. Due to the equivalence between autonomous gradient system and catastrophe model, a standard cusp catastrophe model can be obtained through variable substitution. The method is applied to displacement data of Huangci landslide and Wolongsi landslide, to show how slopes evolve before landsliding. There is convincing statistical evidence to believe that the nonlinear dynamic model can make satisfied prediction results. Most important of all, we find that there is a sudden fall of D, which indicates the occurrence of catastrophe (when D=0).