This book, written by two major figures in adaptive control, provides a wealth of material for researchers, practitioners, and students. While some researchers in adaptive control may note the absence of a particular topic, the book‘s scope represents a high-gain instrument. It can be used by designers of control systems to enhance their work through the information on many new theoretical developments, and can be used by mathematical control theory specialists to adapt their research to practical needs. The book is strongly recommended to anyone interested in adaptive control.

Adaptive Control

This paper studies a number of quantized feedback design problems for linear systems. We consider the case where quantizers are static (memoryless). The common aim of these design problems is to stabilize the given system or to achieve certain performance with the coarsest quantization density. Our main discovery is that the classical sector bound approach is nonconservative for studying these design problems. Consequently, we are able to convert many quantized feedback design problems to well-known robust control problems with sector bound uncertainties. In particular, we derive the coarsest quantization densities for stabilization for multiple-input-multiple-output systems in both state feedback and output feedback cases; and we also derive conditions for quantized feedback control for quadratic cost and H/sub /spl infin// performances.

The sector bound approach to quantized feedback control | NOVA. The University of Newcastle's Digital Repository

The speed regulation problem for permanent magnet synchronous motor (PMSM) servo system is studied in this paper. In order to optimize the control performance of the PMSM servo system, the predictive functional control (PFC) method is introduced in the control design of speed loop. The PFC-based speed control design consists of two steps. A simplified model is employed to predict the future q -axis current of PMSM. Then, an optimal control law is obtained by minimizing a quadratic performance index. However, it is noted that the standard PFC method does not achieve a satisfying effect in the presence of strong disturbances. To this end, an improved PFC method, called the PFC+ESO method, is developed. It introduces extended state observer (ESO) to estimate the lumped disturbances and adds a feedforward compensation item based on the estimated disturbances to the PFC speed controller. Simulation and experiment comparisons are made for these PFC methods and proportional-integral method with antiwindup control method to verify the effectiveness of the proposed methods.

Speed Control for PMSM Servo System Using Predictive Functional Control and Extended State Observer

A novel type of Model Predictive Direct Torque Control (MP-DTC) is proposed in this paper The future behavior of the plant is predicted using a discrete-time state-space model in the dq reference frame for a finite set of voltage vectors, which is pre-selected by a graph algorithm The preferable input is chosen based on a cost function Contrarily to conventional MP-DTC (and DTC), the stator flux is not explicitly controlled nor hysteresis bounds are used The cost function is designed to meet multiple demands: torque reference tracking, Maximum Torque per Ampere (MTPA) tracking for high electrical efficiency, and limitation of the states to their maximum admissible values

Model Predictive Direct Torque Control With Finite Control Set for PMSM Drive Systems, Part 1: Maximum Torque Per Ampere Operation

The predictive control method deals with the prediction of the motor behavior based on a mathematical model of the motor. This model is dependent on the motor parameters. However, these parameters may not match with their actual values due to the measurement error or they may change during the operation of the motor. All these uncertainties and model inexactitude lead to inaccurate prediction of the motor behavior and deteriorate the performance of the predictive algorithm. This paper presents an extension of the predictive current control (PCC) method to improve the prediction accuracy for a permanent-magnet synchronous motor (PMSM). The proposed strategy not only reduces the current ripple but also improves the robustness of the system against parameter uncertainties. Simulation and experimental results that confirm the good performance of the proposed method are presented.

Robustness Improvement of Predictive Current Control Using Prediction Error Correction for Permanent-Magnet Synchronous Machines

Permanent magnet synchronous motors (PMSM) have been receiving much attention due to their high efficiency. Usually, an inverter is used to drive the PMSM. In conventional current control systems, the inverter is regarded as an ideal amplifier. However, there exist various constraints such as voltage saturation in current transient response and output nonlinearity in overmodulation. In addition, an output of the inverter is limited to discrete values in a precise sense. Thus, the system has a nature of a hybrid dynamical system. In this paper, we propose a novel model predictive control (MPC) based current control system to improve performance in cases where the inverter cannot provide the appropriate output. In MPC, a steady state error often occurs due to the modeling error. In order to overcome the problem, we extend the model in which the accumulation of the steady state error is included. The effectiveness of the proposed method is shown through simulations and experiments.

Optimal control of PMSMs using model predictive control with integrator

The direct torque control (DTC) of ac motors leads to a faster torque response with a small number of switching operations in inverters when compared with a conventional approach such as vector control. DTC exhibits a hybrid nature in the sense that the system is composed of continuous variables of torque and flux and involves discrete switching in the inverter. The output of the inverter is limited to the finite discrete values at each instance of sampling. Model predictive control (MPC) is applied to the system so that an optimal switching sequence is derived subject to the given constraints. The proposed MPC-based approach reduces the torque ripple with a small number of switching operations. The effectiveness of the proposed MPDTC approach is verified through simulations and experiments by comparing the proposed approach with conventional DTC.

Model Predictive Direct Torque Control for PMSM with Discrete Voltage Vectors

Model predictive instantaneous-current control (MPIC), which was proposed in our earlier works, enables us to achieve better instantaneous current control using mathematical models of an inverter and permanent magnet synchronous motors (PMSM). However, the dead-time to avoid the short breakdown in the inverter is the main reason in the modeling error. If the modeling error is not ignorable, it is not possible to predict the current evolution using the model. Thus, in this paper, we analyze the influence of the dead-time in MPIC and propose a compensation technique for the dead-time so that the error of the predicted current is reduced. The effectiveness of the proposed method is verified through simulation and experiments.

Dead-time compensation in model predictive instantaneous-current control

Permanent-magnet synchronous motors have attracted much attention due to their high efficiency and high-torque density. For higher control performance, finite control set model predictive control (FCS-MPC) and continuous control set MPC (CCS-MPC) have been developed. However, the former requires high computing power, whereas inverter voltage saturation is not considered in the latter. Therefore, this study proposes an optimal current control law taking into consideration the inverter output voltage. The effectiveness of the proposed method is verified by comparison with the FCS-MPC and the standard CCS-MPC through experiments.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-epa.2019.0225

Optimal current control for PMSM considering inverter output voltage limit: model predictive control and pulse-width modulation

We have proposed a novel approach for permanent magnet synchronous motors based on model predictive control. This paper describes a frequency analysis based on a single sinusoidal correlation in order to clarify the control performance of the model predictive instantaneous current control (MPIC). For MPIC, the proposed analysis method allows us to analyze the gain and phase characteristics which has not been discussed explicitly so far in other works.

Tadanao Zanma

Papers

Optimal control of PMSMs using model predictive control with integrator

Model Predictive Direct Torque Control for PMSM with Discrete Voltage Vectors

Dead-time compensation in model predictive instantaneous-current control

Optimal current control for PMSM considering inverter output voltage limit: model predictive control and pulse-width modulation

Instantaneous-current control of PMSM using MPC: Frequency analysis based on sinusoidal correlation