Titlebook: Handbuch Historisches Mauerwerk; Untersuchungsmethode Josef Maier Book 2012Latest edition Springer-Verlag Berlin Heidelberg 2012 Bauphysik.

滲透

Josef MaierI tend to think about control in terms of knowledge. When you talk about tuning or hill climbing, the system is changing its state, and you can say that it knows different things after it’s got whatever it’s controlling under control. If you’re trying to have adaptive control, then the knowledge of

共同生活

Josef Maierge in local dynamics prevents a single linear controller to yield a good performance, or even to globally stabilize the system. In order to tackle this issue, the approach followed in the present chapter consists of the identification of a bank of linear models that represent the plant dynamics in d

夸張

Josef Maier point explored in this chapter is to show that, by making a change in the manipulated variable, together with a change in the timescale, it is possible to obtain exactly linear plant models, either of input-output or state-space type. In practice, the change in the timescale is implemented in discr

progestin

Josef Maierpulated variable, a quadratic cost is minimized in a receding horizon sense. The fact that hard inequality constraints are not imposed, allows to express the control law in closed form, either as a feedback from a nonminimum plant state made of input/output samples or a discrete transfer function. T

Boycott

MOTTO

Josef Maierlants with unknown actuator failures. Model reference adaptive control (MRAC) requires that the controlled plant is minimum phase. However, many physical plants are nonminimum phase, and the adaptive control schemes based on MRAC cannot be applied to such systems. For example, the linearized aircraf

mitral-valve

Josef Maiereach some prespecified production requirements, by means of control input commands. Based on a recurrent high-order neural-network model of the buffer states, an adaptive continuous-time neural-network controller was developed. Stable control and update laws guaranteeing system stability, boundednes

Terminal

Josef Maierwn nonlinearities. The recurrent high-order neural networks are used as models of the unknown plant, practically transforming the original unknown system into a RHONN model which is of known structure, but contains a number of unknown constant-value parameters, known as synaptic weights. When the RH

CLOUT

Josef Maiert estimates of unknown systems that are near optimal from both the stochastic and worst case points of view. This paper studies some algorithms that produce such estimates. The algorithms combine a classical least squares or maximum likelihood estimate with a projection. It is shown that the modifie

considerable

akes mandatory the actions dangerous from the driver in complex situations such as in urban places. Second the driver shifts more and more his/her task to the monitoring and stays just as a back-up for the technological solution. Unfortunately he/she does not master adequate request as the human lea