1,720,999 research outputs found
Implementation of Input Design for Structured Nonlinear System Identification via Linear IMC Control
No full textFoundations of control and estimation over lossy networks
No full textThis paper considers control and estimation problems where the sensor signals and the actuator signals are transmitted to various subsystems over a network. In contrast to traditional control and estimation problems, here the observation and control packets may be lost or delayed. The unreliability of the underlying communication network is modeled stochastically by assigning probabilities to the successful transmission of packets. This requires a novel theory which generalizes classical control/estimation paradigms. The paper offers the foundations of such a novel theory. The central contribution is to characterize the impact of the network reliability on the performance of the feedback loop. Specifically, it is shown that for network protocols where successful transmissions of packets is acknowledged at the receiver (e.g., TCP-like protocols), there exists a critical threshold of network reliability (i.e., critical probabilities for the successful delivery of packets), below which the optimal controller fails to stabilize the system. Further, for these protocols, the separation principle holds and the optimal LQG controller is a linear function of the estimated state. In stark contrast, it is shown that when there is no acknowledgement of successful delivery of control packets (e.g., UDP-like protocols), the LQG optimal controller is in general nonlinear. Consequently, the separation principle does not hold in this circumstanc
The Filter Design From Data (FD2) Problem: Parametric-Statistical Approach
No full textA large body of literature exists on the filter design problem, assuming that the system to be filtered is known. However, in most practical situations, the system is not known, but a set of measured data is available. In such situations, a two-step procedure is typically adopted: a model is identified from this data set, and a filter is designed based on the identified model. In this paper, we consider an alternative approach, which uses the available data not for the identification of a model, but for the direct design of the filter. Such a direct design is investigated within a parametric-statistical framework for both the cases of linear time-invariant and nonlinear systems. The noise is assumed to be stochastic, and optimality refers to minimizing the estimation error variance. It is shown that the direct design has superior features with respect to the two-step design, especially in the presence of modeling errors. Another relevant advantage of the direct design over the two-step procedure is that minimum variance (Kalman) filters for nonlinear systems are, in general, difficult to derive and/or to implement. On the contrary, the direct approach allows for a very efficient filter design. To demonstrate the effectiveness of the proposed direct design, two examples are presented: the first is related to estimation of the Lorentz chaotic attractor; the second, involving real data, is related to estimation of vehicle yaw rate
Optimal linear LQG control over lossy networks without packet acknowledgment
No full textThis paper is concerned with control applications over lossy data networks. Sensor data is transmitted to an estimation-control unit over a network, and control commands are issued to subsystems over the same network. Sensor and control packets may be randomly lost according to a Bernoulli process. In this context, the discrete-time linear quadratic Gaussian (LQG) optimal control problem is considered.
It is known that in the scenario described above, and for protocols for which there is no acknowledgment of successful delivery of control packets (e.g. UDP-like protocols), the LQG optimal controller is in general nonlinear. However, the simplicity of a linear sub-optimal solution is attractive for a variety of applications. Accordingly, this paper characterizes the optimal linear static controller and compares its performance to the case when there is acknowledgment of delivery of packets (e.g. TCP-like protocols)
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