Papers by Keyword: Internal Model

Abstract: The cooperative output regulation problem for a class of nonlinear uncertain multi-agent systems is considered. Based on the distributed internal model, the problem is firstly transformed into a global stabilization problem of the augmented system. Then, using the backstepping design method, a distributed control law with its stability analysis is proposed to solve the global stabilization problem of the augmented system. Finally, a numerical simulation is made to show the efficacy of the analytic results.

Abstract: According to the characteristics of the internal model control and feed-forward control and Combining the both advantages, the compound control system of the internal model add feed-forward compensator was designed. In order to improve the dynamic performance of the control system, online identification method is adopted to establish the internal model. The designs of the internal model controller and feed-forward compensator were detailed instructions. The simulation shows that the compound control system have not only good dynamic performance, high tracking precision and strong anti-jamming capability, but also have the change of system parameters with strong robustness.

Abstract: In this paper, stability analysis of an optical fiber amplifier gain controller is presented. The mathematical model of an EDFA (Eribium-Doped Fiber Amplifier) control system is described by two-time scaled system and thus singular perturbation approach can be applied to the design and analysis of the control system. In order to handle the channel variations such as add/drop, we adopt an internal model. The performance of the control system is theoretically analyzed based on a novel singular-perturbation-like approach.

Abstract: We hypothesized that multi-sensory processing at the central nervous system (CNS) in human postural control can be described using an optimal estimator model. The estimates on body dynamics from multi-sensory signals contain sensory noise, transmission delays, and process disturbances. The state estimates approximate actual body movement. Erroneous estimates degrade the performance of feedback control and could cause a loss of balance if distorted severely. To test the hypothesis, we examined the frequency response of a visually-induced postural sway with stimulus frequency ranging from 0.075 to 1Hz and established an optimal estimator model. Two healthy young (33yrs ± 1) subjects stood on a force platform located 1.25m behind a projection screen with their arms crossed over their chests. They were asked to maintain an upright posture against the sinusoidal visual field stimuli. Each sinusoidal visual stimulus was generated by a projector for 200secs in pitch direction with a maximum pitch angle of 20o. Kinematics data was recorded to calculate the frequency response function of the center of mass (COM). There were three components in the modeling procedure: a biomechanical model of body and sensor dynamics, a linear feedback control model to stabilize the biomechanical model, and a state estimator to estimate body dynamic states based on multi-sensory outputs. We modeled the sensor dynamics of the semicircular canal, otolth, vision, and muscle spindles at the ankle and hip joint. We used the Kalman filter and linear quadratic regulator to determine feedback gains. Results showed that the frequency response function of a visually-induced postural sway decreased as stimulus frequency increased, and this low-pass filtering characteristic with an approximate cutoff frequency of 0.2Hz was also simulated by the postural feedback control model with optimal estimator. Low-pass filtering characteristics of the frequency response are mainly due to body and sensor dynamics, which show reduced responses for high frequency stimulus. The Kalman filter represents that the CNS utilizes redundant sensory information in a way that minimizes discrepancies between actual body dynamics and estimated body dynamics based on sensory output and an internal model. The results suggest that the CNS may make use of an internal representation of body dynamics, and can integrate sensory information in an optimal way to best estimate human postural responses.