Speed Tracking of Linear Induction Motor : an Analytical Nonlinear Model Predictive Controller

• Direct Torque Control (DTC) is considered as one of the latest and most efficient techniques that can be used for the speed and/or position tracking control problem of induction motor drives. However, the main drawbacks of classical DTC are the variable switching frequency that could exceed the maximum allowable switching frequency of inverters and also the ripples it has over the current and torque, especially at low speed tracking. It has been shown that applying Model Predictive Control (MPC) to a Linear Induction Motor (LIM) leads to a much better speed tracking performance. MPC provides the optimal 3-phase primary voltages necessary for speed tracking using a Pulse Width Modulation (PWM) inverter. The main inherent drawbacks of the MPC strategy are its high switching frequency and also its heavy computational load which makes it inapplicable in real-time. This paper presents a new analytical approach based on the MPC strategy. The new analytical approach controls directly the inverter switches. Hence the PWM inverter is not needed. It computes the optimal position transitions sequence of the inverter switches to track the speed reference trajectory. The proposed analytical nonlinear MPC controller includes an integral action to reduce the steady state error. The proposed controller admits real-time implementation. Simulation results show that the new analytical approach has good tracking properties at the same time as it reduces the average inverter switching frequency by 93 % as compared to classical DTC.

Subject headings

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Reglerteknik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Control Engineering (hsv//eng)

Keyword

Nonlinear control systems

Position control

Predictive control

Speed tracking

TECHNOLOGY

TEKNIKVETENSKAP

Publication and Content Type

ref (subject category)

kon (subject category)