Equivalent Continuous-time Model Research Articles

RETRACTED: Simulation of police shooting stability training based on FPGA and sensors

Stability training refers to the practice, while the surface is unstable and activation stabilizer muscles of the trunk and goals, which may ignore other forms of exercise. For stability, the train network control system is caused by network latency and packet drop out of school. The current study aimed to analyze the accuracy of necessary shooting skills and check the balanced posture, stability, and rifle shooting performance bracelets. There is no burden on the body in different performance groups. No. Equivalent continuous-time model, proposed account. The train network control system model uses Romanov theory, some new analytical techniques to get in, and less conservative results. It will be completed. The effectiveness of the proposed method of numerical display. The proposed training in the correct use of architecture to shape muscle memory training interns position signal trainee lead to incorrect behavior. Students warned that the recent shooting would be suspended after the retry using the selected function when needed for phase detection of unnecessary or excessive exercise pistol. Combined with real-time biofeedback applications, it can lead to accelerated learning, training the number of shots that poverty can be reduced, thereby saving time and costs.

Identification of continuous-time systems from samples of input-output data: An introduction

This paper presents an introductory survey of the methods that have been developed for identification of continuous-time systems from samples of input-output data. The two basic approaches may be described as (i) the indirect method, where first a discrete-time model is estimated from the sampled data and then an equivalent continuous-time model is calculated, and (ii) the direct method based on concepts of approximate numerical integration, where a continuous-time model is obtained directly without going through the intermediate step of first determining a discrete-time model.

Hybrid state-space self-tuning control using dual-rate sampling

This paper presents a hybrid state-space self-tuning control scheme using dual-rate sampling for suboptimal digital adaptive control of linear time-invariant continuous-time multivariable stochastic systems with unknown parameters. An equivalent fast-rate discrete-time state-space innovation model (with estimated states) of the continuous-time system is constructed by using the estimated system parameters and Kalman gain. To utilize the existing optimal regional-pole assignment method developed in the continuous-time domain, the constructed fast-rate discrete-time model is converted into an equivalent continuous-time model for the development of a state-feedback optimal control law with pole placement in a specific region. The developed analog optimal control law is then converted into an equivalent pseudo-slow-rate digital control law via the proposed digital redesign technique, which can be realized via slow-rate digital electronics. The proposed method enables the development of a digitally implementable advanced control algorithm for digital adaptive control of continuous-time multivariable stochastic systems which may be unstable and/or have nonminimum phase.