PERFORMANCE ANALYSIS OF LINEAR QUADRATIC REGULATOR AND MODEL PREDICTIVE CONTROL FOR DC-DC CONVERTER SYSTEMS

Abstract

This Project presents performance analysis of linear quadratic regulator and model predictive control for DC-DC Converter Systems. A DC-to-DC converter receives a DC input voltage and outputs another DC voltage. The applied input voltage may be higher or lower than the DC output voltage. These days, laptops and cell phones frequently employ DC to DC converters. To Electrical and electronic engineering has a wide range of applications where optimization can be used to reduce the goal function. Almost all in Electrical Engineering battery charging, operation cost in HRS, Filter design, controller design. In the thesis a common problem in electronic measurements and electrical engineering: Reducing “steady state error” or obtaining the “closed loop response” of the given system when no constraints or some constraints is applied to the system. The solution to this problem is the introduction of an advance controller in the control system, specifically the linear-quadratic regulator and model predictive controller. Linear-quadratic regulators (LQR) have several noteworthy properties in terms of control techniques. For instance, they can be employed methodologically independent of the system's order and they are fundamentally stable. They can also make the system behave "optimally" in accordance with the designer's needs. A new and promising control strategy for power converters and drives is model predictive control (MPC). The literature has offered a number of theoretical and practical problems that demonstrate how well this technique works. The result of a converter simulation without any regulating parameters does not quite match our rated value. To obtain an accurate outcome, a variety of optimization approaches as well as intelligent strategies might be applied. According to simulation results, we applied LQR and MPC, and the system operates satisfactorily.