Multirate modeling and control design for switched-mode powerconverters

A class of pulse-width modulated (PWM) systems that includes many switched-mode power converters is considered. A new multirate low-frequency sampling PWM scheme is introduced that not only provides the possibility for new types of operation, but also unifies the switching logic corresponding to established PWM schemes. An exact nonlinear discrete-time model is used to develop multirate nonlinear controllers and observers based on map-inversion concepts. This nonlinear design methodology is widely applicable, requiring only that the continuous-time small-signal average model be controllable and observable     

TS-based sampled-data model predictive controller for continuous-time nonlinear systems

This paper proposes a new fuzzy model predictive control approach for continuous-time nonlinear systems in terms of linear matrix inequalities (LMIs). The proposed approach is based on the Takagi–Sugeno fuzzy modeling, a quadratic Lyapunov function, and a sampled-data parallel distributed compensation controller with constant sampling time. The goal is designing the sampled-data controller such that at each sampling time, the stability of the closed-loop system is guaranteed and an infinite horizon cost function is minimised. The main advantage of the proposed approach is to eliminate the approximations induced from discretizing the original system and cost function upper bound minimisation. Consequently, a lower bound of the cost function is obtained and the performance of the proposed model predictive controller is improved compared to the recently published papers in the same field of interest. In addition, the Euclidean norm constraint of the control input vector is derived in terms of LMIs. To illustrate the merits of the proposed approach, the proposed technique is applied to a continuous stirred tank reactor system.     

Experimental Study of Dynamic Based Feedback Linearization for Trajectory Tracking of a Four-Wheel Autonomous Ground Vehicle

In this paper, experimental study of dynamic based trajectory tracking of an autonomous ground vehicle is presented.The vehicle with two front (steering) and two rear (driving) wheels and also an on-board computer, two DC motors, two batteries and two measurement systems is a good example of an autonomous ground vehicle. The dynamic model of this vehicle is presented in the state-space form with steering and driving torques as inputs; kinematic and dynamic parameters of the model and also electrical parameters of the motors are identified, measurement systems are calibrated and the simulation of controlling this model by feedback linearization method is compared with the experiments.The results of simulations and experiments for the feedback linearization technique are compared with those of a simple PID controller and also the results for sharp turn trajectory tracking illustrate the validity of the method used and the usefulness of the built autonomous ground vehicle.     

Uncertainty decomposition of quantum networks in SLH framework

This paper presents a systematic method to decompose uncertain linear quantum input‐output networks into uncertain and nominal subnetworks, when uncertainties are defined in SLH representation. To this aim, two decomposition theorems are stated, which show how an uncertain quantum network can be decomposed into nominal and uncertain subnetworks in cascaded connection and how uncertainties can be translated from SLH parameters into state‐space parameters. As a potential application of the proposed decomposition scheme, robust stability analysis of uncertain quantum networks is briefly introduced. The proposed uncertainty decomposition theorems take account of uncertainties in all three parameters of a quantum network and bridge the gap between SLH modeling and state‐space robust analysis theory for linear quantum networks.