Jean Thomas

Speed Tracking of a Linear Induction Motor-Enumerative Nonlinear Model Predictive Control

Direct torque control (DTC) is considered as one of the most efficient techniques for speed and/or position tracking control of induction motor drives. However, this control scheme has several drawbacks: the switching frequency may exceed the maximum allowable switching frequency of the inverters, and the ripples in current and torque, especially at low speed tracking, may be too large. In this brief, we propose a new approach that overcomes these problems. The suggested controller is a model predictive controller, which directly controls the inverter switches. It is easy to implement in real time and it outperforms all previous approaches. Simulation results show that the new approach has as good tracking properties as any other scheme, and that it reduces the average inverter switching frequency about 95% as compared to classical DTC.

Complexity reduction of Robust Model Predictive Controller for uncertain Piecewise Affine Systems

This paper considers discrete-time, uncertain Piecewise Affine (PWA) systems affected by both polytopic parameter variations and bounded disturbances. We are interested in the Robust Model Predictive Control (RMPC) for uncertain PWA systems where the uncertainty can be presented in polytopes framework. RMPC is known as a complex problem and indeed for PWA systems, the on-line computation becomes computationally burdensome and inapplicable. In this paper we develop a new algorithm that consists of three different phases, based on the system states location. The proposed algorithm gives a simple and fast sub-optimal solution which considerably reduces the on-line computation and guarantee to drive the system states to the target region in spite of the considered uncertainties. The proposed algorithm is applied in simulation to a two tanks example.

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.

Integrating Particle Swarm Optimization with Analytical Nonlinear Model Predictive Control for Nonlinear Hybrid Systems

The computation load remains the main challenge facing the control techniques of hybrid systems with discrete and continuous control signals. In this paper, a new hybrid controller based on Analytical Nonlinear Model Predictive Control (ANMPC) and Particle Swarm Optimization (PSO) for nonlinear hybrid systems is presented. The proposed controller offer sub-optimal solution in reasonable time while respecting the given constraints. The new developed technique is not considered as a computation burden, thus real-time implementation is possible for many hybrid systems. Besides, it can be applied directly to the nonlinear models, avoiding linearization which may lead to inaccurate model and unexpected behaviour. An application of the proposed controller to a three tanks example is presented.

Enumerative nonlinear model predictive control for linear induction motor using load observer

Enumerative nonlinear model predictive control for speed tracking problem of linear induction motors has been presented in [1], where the authors show that this control scheme has better performance as compared to direct torque control. In this paper, the authors show that using a load observer for integral action, the performance can be further improved. Specifically simulation results show that a load observer results in better tracking properties and offers more robust control.

Model-Based Power Plant Master Control

In this paper a coordinated master control for a solid fuel power plant has been developed and the performance evaluated in terms of tracking capability, stability and robustness. The control strategy has been model-based predictive control (MPC) and it was evaluated on a nonlinear process model of the Vattenfall power plant Idbäcken in Nyköping, Sweden. The developed master MPC with gain scheduling has better performance compared to the existing PID controller which has been thoroughly studied and tuned in a previous project. The robustness of the proposed master MPC controller against common disturbances and parameter variation has been investigated and it shows that the proposed controller is more robust than the existing PID controller.