Control of a direct matrix converter with modulated model predictive control

This paper investigates the use of a model- predic-tive control strategy to control a direct matrix converter. The proposed control method combines the features of the classical Model Predictive Control and the Space Vector Modulation technique into a Modulated Model Predictive Control. This new solution maintains all the characteristics of Model Predictive Control (such as fast transient response ,multi-objective control using only one feedback loop, easy inclusion of non-linearities and constraints of the system, the flexibility to include other system requirements in the controller) adding the advantages of working at fixed switching frequency and improving the quality of the controlled waveforms. Simulation and experimental results employing the control method to a direct matrix converter are presented

Concurrent Learning Adaptive Model Predictive Control with Pseudospectral Implementation

This paper presents a control architecture in which a direct adaptive control technique is used within the model predictive control framework, using the concurrent learning based approach, to compensate for model uncertainties. At each time step, the control sequences and the parameter estimates are both used as the optimization arguments, thereby undermining the need for switching between the learning phase and the control phase, as is the case with hybrid-direct-indirect control architectures. The state derivatives are approximated using pseudospectral methods, which are vastly used for numerical optimal control problems. Theoretical results and numerical simulation examples are used to establish the effectiveness of the architecture.Comment: 21 pages, 13 figure

Data-driven adaptive model-based predictive control with application in wastewater systems

This study is concerned with the development of a new data-driven adaptive model-based predictive controller (MBPC) with input constraints. The proposed methods employ subspace identification technique and a singular value decomposition (SVD)-based optimisation strategy to formulate the control algorithm and incorporate the input constraints. Both direct adaptive model-based predictive controller (DAMBPC) and indirect adaptive model-based predictive controller (IAMBPC) are considered. In DAMBPC, the direct identification of controller parameters is desired to reduce the design effort and computational load while the IAMBPC involves a two-stage process of model identification and controller design. The former method only requires a single QR decomposition for obtaining the controller parameters and uses a receding horizon approach to process input/output data for the identification. A suboptimal SVD-based optimisation technique is proposed to incorporate the input constraints. The proposed techniques are implemented and tested on a fourth order non-linear model of a wastewater system. Simulation results are presented to compare the direct and indirect adaptive methods and to demonstrate the performance of the proposed algorithms

Finite control set and modulated model predictive flux and current control for induction motor drives

The paper presents a new implementation of direct flux and current vector control of an induction motor drive using the techniques of model predictive control. The advantages offered by predictive control are used to enhance the dynamics of direct flux vector control. To minimize the problems of variable switching frequency inherent to finite control set predictive control, an alternative approach using pulse width modulation is studied for command execution as occurs in the so-called modulated model predictive control. A comparison between finite control set and modulated model predictive control is presented and the results are also compared with the control implementation through traditional proportional-integral regulators to highlight the advantages and drawbacks of predictive control based strategies. Apart from a greater harmonic content in stator currents, the predictive control can offers control dynamics comparable with proportional-integral control while maintaining immunity against machine parameter variations and excluding the need for controller tunin

Grid Parameter estimation using Model Predictive Direct Power Control

This paper presents a novel Finite Control Set Model Predictive Control (FS-MPC) approach for grid-connected converters. The control performance of such converters may get largely affected by variations in the supply impedance, especially for systems with low Short Circuit Ratio (SCR) values. A novel idea for estimating the supply impedance variation, and hence the grid voltage, using an algorithm embedded in the MPC is presented in this paper. The estimation approach is based on the difference in grid voltage magnitudes at two consecutive sampling instants, calculated on the basis of supply currents and converter voltages directly within the MPC algorithm, achieving a fast estimation and integration between the controller and the impedance estimator. The proposed method has been verified, using simulation and experiments, on a 3-phase 2-level converter

Adaptive-Optimal Control of Spacecraft near Asteroids

Spacecraft dynamics and control in the vicinity of an asteroid is a challenging and exciting problem. Currently, trajectory tracking near asteroid requires extensive knowledge about the asteroid and constant human intervention to successfully plan and execute proximity operation. This work aims to reduce human dependency of these missions from a guidance and controls perspective. In this work, adaptive control and model predictive control are implemented to generating and tracking obstacle avoidance trajectories in asteroid’s vicinity. Specifically, direct adaptive control derived from simple adaptive control is designed with e modification to track user-generated trajectories in the presence of unknown system and sensor noise. This adaptive control methodology assumes no information on the system dynamics, and it is shown to track trajectories successfully in the vicinity of the asteroid. Then a nonlinear model predictive control methodology is implemented to generate obstacle avoidance trajectories with minimal system information namely mass and angular velocity of the asteroid. Ultimately, the adaptive control system is modified to include feed-forward control input from the nonlinear model predictive control. It is shown through simulations that the new control methodology names direct adaptive model predictive control (DAMPC), is able to generate sub-optimal trajectories. A comparative study is done with Asteroid Bennu, Kleopatra and Eros to show the benefits of DAMPC over adaptive control and MPC. A study on effect of noisy measurements and model is also conducted on adaptive control and direct adaptive model predictive control

Indirect predictive control techniques for a matrix converter operating at fixed switching frequency

The following paper presents a novel indirect model predictive control strategy for a direct matrix converter (DMC). The direct matrix converter has a large number of available switching states and therefore the implementation of predictive control techniques require high computational resources. In addition, the simultaneous selection of weighting factors for the control of input and output variables of the converter complicates the system tuning. In this paper, two indirect model predictive control strategies are proposed in order to reduce the computational cost and by doing so avoid the use of weighting factors. The proposal is enhanced with a fixed switching frequency strategy in order to improve the performance of the full system. Results confirm the feasibility of the proposal by demonstrating that it is an alternative to classical predictive control strategies for the direct matrix converter.CONACYT – Consejo Nacional de Ciencia y Tecnologí

Assessment of a Universal Reconfiguration-less Control Approach in Open-Phase Fault Operation for Multiphase Drives

Multiphase drives have been important in particular industry applications where reliability is a desired goal. The main reason for this is their inherent fault tolerance. Di erent nonlinear controllers that do not include modulation stages, like direct torque control (DTC) or model-based predictive control (MPC), have been used in recent times to govern these complex systems, including mandatory control reconfiguration to guarantee the fault tolerance characteristic. A new reconfiguration-less approach based on virtual voltage vectors (VVs) was recently proposed for MPC, providing a natural healthy and faulty closed-loop regulation of a particular asymmetrical six-phase drive. This work validates the interest in the reconfiguration-less approach for direct controllers and multiphase drives

Indirect predictive control strategy with mitigation of input filter resonances for a direct matrix converter

In this paper an indirect model predictive current control strategy is proposed. The proposed method simplifies the computational cost while avoiding the use of weighting factors. Weighting factors are an issue for model predictive control in a direct matrix converter due to the large number of available switching states and necessity to control both input and output sides of the converter

Indirect model predictive current control techniques for a direct matrix converter

The direct matrix converter has twenty-seven available switching states which implies that the implementation of predictive control techniques in this converter requires high computational cost while an adequate selection of weighting factors in order to control both input and output sides of the converter. In this paper, two indirect model predictive current control strategies are proposed in order to simplify the computational cost while avoiding the use of weighting factors. Both methods are based on the fictitious dc-link concept, which has been used in the past for the classical modulation and control techniques of the direct matrix converter. Simulated results confirm the feasibility of the proposed techniques demonstrating that they are an alternative to classical predictive control strategies for the direct matrix converter