Robust Controller Design of Networked Control Systems with Nonlinear Uncertainties

We address robust stabilization problem for networked control systems with nonlinear uncertainties and packet losses by modelling such systems as a class of uncertain switched systems. Based on theories on switched Lyapunov functions, we derive the robustly stabilizing conditions for state feedback stabilization and design packet-loss dependent controllers by solving some matrix inequalities. A numerical example and some simulations are worked out to demonstrate the effectiveness of the proposed design method.

Controller design for variable-speed permanent magnet wind turbine generators interfaced with Z-source inverter

Increased awareness of environmental concerns has caused greater interest in developing power sources based on renewable technologies, such as wind. Due to the intermittent nature of the wind speed, output voltage and frequency of the direct driven permanent magnet synchronous generators (PMSG) are normally unsteady. Recently proposed Z-source inverter has been considered as a potential solution for grid interfacing wind power generators, thanks to buck-boost function that the single stage Z-source inverter can offer. Two control methodologies, namely unified controller for isolated operation and a multi-loop controller for grid interfaced operation are investigated in this paper. Theoretical analysis of these two control schemes is presented and experimental results to verify the effectiveness of the control method are also included.

A direct adaptive neural command controller design for an unstable helicopter

This paper presents an off-line (finite time interval) and on-line learning direct adaptive neural controller for an unstable helicopter. The neural controller is designed to track pitch rate command signal generated using the reference model. A helicopter having a soft inplane four-bladed hingeless main rotor and a four-bladed tail rotor with conventional mechanical controls is used for the simulation studies. For the simulation study, a linearized helicopter model at different straight and level flight conditions is considered. A neural network with a linear filter architecture trained using backpropagation through time is used to approximate the control law. The controller network parameters are adapted using updated rules Lyapunov synthesis. The off-line trained (for finite time interval) network provides the necessary stability and tracking performance. The on-line learning is used to adapt the network under varying flight conditions. The on-line learning ability is demonstrated through parameter uncertainties. The performance of the proposed direct adaptive neural controller (DANC) is compared with feedback error learning neural controller (FENC).

A controller design method for 3 phase 4 wire grid connected VSI with LCL filter

Closed loop control of a grid connected VSI requires line current control and dc bus voltage control. The closed loop system comprising PR current controller and grid connected VSI with LCL filter is a higher order system. Closed loop control gain expressions are therefore difficult to obtain directly for such systems. In this work a simplified approach has been adopted to find current and voltage controller gain expressions for a 3 phase 4 wire grid connected VSI with LCL filter. The closed loop system considered here utilises PR current controller in natural reference frame and PI controller for dc bus voltage control. Asymptotic frequency response plot and gain bandwidth requirements of the system have been used for current control and voltage controller design. A simplified lower order model, derived for closed loop current control, is used for the dc bus voltage controller design. The adopted design method has been verified through experiments by comparison of the time domain response.