# Machine Learning for the Control and Monitoring of Electric Machine Drives:
Advances and Trends

Shen Zhang, Oliver Wallscheid, and Mario Porrmann Shen Zhang was with the
School of Electrical and Computer Engineering, Georgia Institute of
Technology, Atlanta, GA 30332, USA. He is now with Joby Aviation, San Carlos,
CA 94070, USA (e-mail: shenzhang@gatech.edu).Oliver Wallscheid is with the
Department of Automatic Control, Paderborn University, 33098 Paderborn,
Germany (e-mail: oliver.wallscheid@uni-paderborn.de).Mario Porrmann is with
the Institute of Computer Science, Osnabrüeck University, Wachsbleiche 27,
49090 Osnabrück, Germany (email: mario.porrmann@uni-osnabrueck.de).

###### Abstract

This review paper systematically summarizes the existing literature on
utilizing machine learning (ML) techniques for the control and monitoring of
electric machine drives. It is anticipated that with the rapid progress in
learning algorithms and specialized embedded hardware platforms, machine
learning-based data-driven approaches will become standard tools for the
automated high-performance control and monitoring of electric drives.
Additionally, this paper also provides some outlook toward promoting its
widespread application in the industry with a focus on deploying ML algorithms
onto embedded system-on-chip (SoC) field-programmable gate array (FPGA)
devices.

###### Index Terms:

Machine learning; electric machine drives; deep learning; reinforcement
learning; embedded systems; FPGA.

##  I Introduction

The motor control community is well-informed on the boom of machine learning
(ML) after the modern back-propagation paper was first published in 1986 [1],
which is evident by the work that appeared three years later on training a
neural network offline to mimic the behavior of hysteresis current controllers
in a three-phase PWM inverter [2]. This work is later followed by a series of
pioneering efforts in the early 1990s on general voltage-fed AC machines [3,
4], induction machines [5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], DC machines
[16, 17], synchronous machines [18], and switched reluctance machines [19]. In
addition to the broad interest in applying ML to motor drive control, such
technologies, especially concerning classification or regression techniques,
have also found their presence in the condition monitoring and fault diagnosis
on various types of electric machines [20, 21, 22, 23, 24, 25, 26, 27].

Around that time, the frontier of power electronics gradually advanced with
the advent of ML models such as neural networks, which have emerged as the
most important area for complex system identification, control, and estimation
in power electronics and motor drives [28]. However, it was also concluded
that “in spite of the technology advancement, currently, industrial
applications of neural networks in power electronics appear to be very few”
[29].

While ML applications always targeted the fastest available hardware
platforms, especially focusing on (massively) parallel architectures, many
existing ML implementations in electric machine drives were based on slow and
sequentially executed digital signal processors (DSP) prior to the deep
learning era, although in some cases multiple DSPs were also used to increase
the execution speed. Embedded platforms such as field-programmable gate arrays
(FPGA) that excel at parallel processing, were not matured technologies at the
time and had limited use.

It is worth noting that hardware limitations are still the main bottleneck for
ML applications in electric machine drives even today. This remains a major
problem particularly in the industrial world due to the high-frequency update
rates111While ML algorithms for real-time video processing only require to run
at a couple of ten hertz, typical motor control update frequencies are in the
range of ten kilohertz, so multiple orders of magnitude higher. of ML
algorithms required for motor drive online applications in combination with
cost-oriented, computationally-constrained embedded hardware. This hardware
constraint has further impeded the deployment of ML algorithms in machine
drives and resulted in insufficient performance in their identification and
control. It is envisioned in [30] that with the present trend of FPGA
development and as the ML technology matures, “intelligent control and
estimation (particularly based on neural networks) will find increasing
acceptance in power electronics, particularly in the robust control of drives”
[31], and they are expected to have widespread applications in the industry
[32, 33, 34].

Figure 1: A vision of future electric machine drives powered by machine
learning.

The past decade has marked an incredibly fast-paced and innovative period in
the history of ML [35]. Spurred by the development of ever-more-powerful
computing platforms and the increased availability of big data, ML has
successfully tackled many previously intractable problems, especially in
computer vision with the convolutional neural network (CNN) [36] and in
natural language processing with the Transformer architecture [37]. ML has
also been applied to and is in the process of transforming many real-world
applications, including entertainment, healthcare, fraud detection, virtual
assistants, and autonomous vehicles. Hardware platforms including GPUs and
FPGA fabric can also achieve very good parallel computing performance with
architecture customization [38], which is intrinsically well-suited for the
parallel characteristics inherent in neural networks and hence their
widespread applications in power electronics and motor drives.

However, the entire field of electric machine drives has remained pretty much
silent on the resurgence of ML over the past decade, when compared with its
continued success and widespread application in condition monitoring [39, 40,
41, 42, 43, 44, 45], design optimization [46, 47, 48, 49, 50, 51, 52, 53, 54,
55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65], and manufacturing [66, 67] of
various types of electric machines. It was not until in the last few years
that research efforts have begun to gradually catch up with the trend [68, 69,
70, 71, 72, 73, 74, 75, 76, 77, 78]. It is anticipated that with the rapid
progress in ML models and embedded systems, the data-driven approach will
become increasingly popular for the high-performance control of electric
machine drives, as envisioned in Fig. 1. While most ML models require some
complex offline training processes, the online inference process can be made
relatively simple through various pruning and quantization methods [38], such
that the groups of parameters with insignificant impact for the artificial
neural network’s (ANN) input-output characteristics are removed and the
numeric precision of the weights is reduced, resulting in reduced model size
and faster computation at the cost of minimal reductions in predictive
accuracy [79].

###  I-A Machine Learning Algorithms

There are three main classes of ML: supervised learning, unsupervised
learning, and reinforcement learning. A brief description of each class and
its classic applications are illustrated in Fig. 2 and in the paragraphs
below.

Figure 2: Disciplines of machine learning [80].

####  I-A1 Supervised Learning

Supervised learning is the ML task of developing models to map input and
output data. The term “supervised” refers to the use of labeled data to train
models for classification or regression problems. Some of the most widely used
supervised learning algorithms include ordinary least squares, ANN, support
vector machines, etc. As will be discussed in later sections, these algorithms
have been extensively applied to the control and monitoring of electric
machine drives, as well as the estimation of models or model parameters
associated with the drives.

####  I-A2 Unsupervised Learning

Unsupervised learning is a type of algorithm that processes and interprets
data based solely on input. Classical tasks in unsupervised learning include
clustering, dimensionality reduction, and anomaly detection. In contrast to
supervised learning, unsupervised learning models work on their own to
discover the inherent structure of unlabeled data. Unsupervised learning can
also be used as an auxiliary pre-processing step in order to apply feature
engineering for supervised learning [80].

####  I-A3 Reinforcement Learning

As a subcategory of ML, reinforcement learning (RL) aims to solve a variety of
decision-making and control problems in a data-driven manner. Specifically, RL
is able to learn in a trial-and-error way and does not require explicit human
labeling or supervision of each data sample. Instead, it requires a well-
defined reward function to obtain reward signals throughout the learning
process. The core of RL is to learn optimal control actions in an environment
to maximize the long-term cumulative reward [80], i.e., it can be considered
the model-free counterpart of model predictive control.

###  I-B Scope

The scope of this paper is to provide a comprehensive overview of the
pertinent literature that applies ML techniques to electric machine drives
from the 1980s to the state of the art. Despite the widespread application of
classical artificial intelligence (AI) techniques in the field of electric
machine drives, such as expert systems [81], fuzzy logic systems [6, 82, 83,
84, 85, 86, 87, 88, 89, 90], and evolutionary algorithms applied to tuning
classical controller/estimator parameters [83, 91, 92, 93, 94, 95, 96], it is
in the authors’ humble opinion that AI is not used here by definition, since
the usual procedures are rather simple compared to the cutting-edge research
within AI computer science, and resulting algorithms do not stand up to the
usual definitions of “intelligence” that mimic “cognitive” functions such as
perception, attention, memory or language processing [97], as illustrated in
Fig. 4. On the other hand, “machine learning” is defined as the study of
(computer) algorithms that can improve automatically through experience and by
the use of data, which is considered a more appropriate summary of the
majority of literature included in this review paper. Therefore, “machine
learning” will be used for the remainder of this manuscript, even if some
authors might have used the exact wording of AI in the titles of their papers.

Furthermore, it is also shown in Fig. 4 that the general understanding of
“deep” ML or deep learning is typically characterized by using data-driven
models of (very) large depths (e.g., many ANN layers with millions of tunable
parameters) such that the manual feature engineering is automatically handled
within the ML pipeline (e.g., using pooling). In contrast, classical (shallow)
ML comes along with manual feature engineering in expert-driven or heuristic
pre-processing steps and ML models of very limited depth. Because “genuine”
deep ML models are dramatically computationally expensive, they will remain
non-real-time capable for years or even decades with regard to embedded
applications in motor drives. Therefore, the scope of deep learning will also
not be covered in this paper.

Figure 3: Simplified block diagram on ML applications in a generic motor drive
system. Every part of the motor drive control scheme could be ML-based, while
also the entire control framework could be just one large ML model. The
control depiction utilizing rotating $dq$-coordinates is only of illustrative
purpose as the ML-based method is not limited to this coordinate system.

###  I-C Contribution

The contribution of this paper is to comprehensively summarize the recent
advances in applying ML-based methods to the control and monitoring of
electric machine drives and to identify suitable embedded systems for
deploying such ML algorithms in real time. Essentially, every part of a
generic motor drive system, such as the core current/speed controller, the
higher-level controller generating the optimal torque, flux, or speed
commands, the flux estimator, the inverter nonideality compensation, and the
modulation scheme, can be substituted by ML-based models as shown in Fig. 3.
Additionally, the entire control framework could also be accomplished using
just one large ML model. These ML-based models should normally be executed at
the same task frequency as the motor control software, i.e., in the micro- to
millisecond range. Classical field-oriented approaches or model-predictive
control are typically used in this context. However, most ML methods do not
rely on a specific control scheme.

###  I-D Outline

The paper is organized as follows: Sections LABEL:sec:IM_drives and
LABEL:sec:PM_drives introduce specific applications of ML methods developed
for induction machines and permanent magnet synchronous machines. As the
inverter and sensors are important parts of any drive system, Section
LABEL:sec:Drive_components discusses state-of-the-art ML techniques applied to
those drive components. In Section LABEL:sec:Future_drives, the future trend
of electric machine drives enabled by state-of-the-art reinforcement learning
algorithms is introduced. Section LABEL:sec:Embedded presents an in-depth
comparative study on the potential embedded platforms to host such ML
applications in electric machine drives for optimal cost and performance.

Figure 4: The broader scope around ML [80].

Due to the widespread popularity of ML and the abundant resources regarding
its fundamentals, this review paper assumes that readers have a sufficient
understanding of the basic concepts of ML, as outlined in [81, 29], so it can
be more pivoted to introducing their successful and diverse applications in
electric machine drives.

##  II Future Challenges and Trends

This paper provides a comprehensive state-of-the-art review of ML-based
solutions addressing the control and monitoring of electric drives. Despite
the continued progress of relevant publications in this field, there are still
some unresolved issues that need to be addressed as future work:

  1. $\mathchoice{\mathbin{\vbox{\hbox{\scalebox{0.5}{$\displaystyle\bullet$}}}}}{\mathbin{\vbox{\hbox{\scalebox{0.5}{$\textstyle\bullet$}}}}}{\mathbin{\vbox{\hbox{\scalebox{0.5}{$\scriptstyle\bullet$}}}}}{\mathbin{\vbox{\hbox{\scalebox{0.5}{$\scriptscriptstyle\bullet$}}}}}$

_Development effort_ : Although FPGAs can offer better energy efficiency,
connectivity, and flexibility, one major challenge of using FPGAs is the
engineering effort in development. Unlike GPU development which requires only
software engineering skills, the development of FPGAs requires hardware
configuration skills as well. The complexity of implementing ML models on
FPGAs makes their manual design processes very time-consuming, even for a
seasoned FPGA engineer. In addition, although many researchers have focused on
ML inference on FPGAs, very few research papers have explored their training
on FPGAs, or how to optimize the architecture design on FPGAs for training.
This is particularly needed for deploying RL algorithms to motor control
applications as the core of RL is to be able to have the agent interact with
the environment in real time and learn a policy (control law) in a trial-an-
error way. Therefore, an automated design workflow from the RL’s neural
network architecture to the hardware design is necessary to enable efficient
and effective training of RL control on FPGAs (i.e., to not only utilize the
FPGA for policy inference but also for online policy learning). If an
effective automated design workflow is developed, researchers and engineers
can quickly develop various ML models for motor control applications without
the need to possess deep knowledge about hardware design.

  2. $\mathchoice{\mathbin{\vbox{\hbox{\scalebox{0.5}{$\displaystyle\bullet$}}}}}{\mathbin{\vbox{\hbox{\scalebox{0.5}{$\textstyle\bullet$}}}}}{\mathbin{\vbox{\hbox{\scalebox{0.5}{$\scriptstyle\bullet$}}}}}{\mathbin{\vbox{\hbox{\scalebox{0.5}{$\scriptscriptstyle\bullet$}}}}}$

_Application effort_ : ML is data-hungry and normally one needs to train an ML
model individually for each drive system at an expensive test bench, hence the
speedy transfer of an ML method between different applications is an issue for
the industrial mass production usage. This issue can be addressed from both
the software and the hardware emulation perspectives. In terms of software,
some ML algorithms are specifically designed to enable transfer learning with
strong domain adaptation capabilities. Additionally, the hardware platform of
different electric drive systems can be emulated in the hardware-in-the-loop
environment, making it a lot easier to collect a sufficient amount of emulated
data to train ML models for any industry application.

  3. $\mathchoice{\mathbin{\vbox{\hbox{\scalebox{0.5}{$\displaystyle\bullet$}}}}}{\mathbin{\vbox{\hbox{\scalebox{0.5}{$\textstyle\bullet$}}}}}{\mathbin{\vbox{\hbox{\scalebox{0.5}{$\scriptstyle\bullet$}}}}}{\mathbin{\vbox{\hbox{\scalebox{0.5}{$\scriptscriptstyle\bullet$}}}}}$

_Safety_ : Since ML is always subject to some kind of stochastic learning, a
method’s output should be also considered a stochastic one. Therefore, the
probability of failing is intrinsic to an ML model, which can cause trouble if
an ML technique produces outliers for estimation or control action. As a
result, there could be negative impacts on the behaviors of mechatronic
systems, thereby compromising their prospects in safety-critical applications.

  4. $\mathchoice{\mathbin{\vbox{\hbox{\scalebox{0.5}{$\displaystyle\bullet$}}}}}{\mathbin{\vbox{\hbox{\scalebox{0.5}{$\textstyle\bullet$}}}}}{\mathbin{\vbox{\hbox{\scalebox{0.5}{$\scriptstyle\bullet$}}}}}{\mathbin{\vbox{\hbox{\scalebox{0.5}{$\scriptscriptstyle\bullet$}}}}}$

_Interpretability_ : ML models are very complex and difficult to understand or
explain, as it is reported in [226] that a recent ML model proposed for
electric drive applications can have close to ten thousand parameters, not to
mention those commercially deployed ML models for natural language processing
or image recognition tasks that could have millions or billions of parameters.
Although interpretability does not ensure safety by itself, it is important
for monitoring functional safety and for understanding where the models are
failing. Therefore, more in-depth investigations regarding the
interpretability and explainability of ML models are necessary for their
commercial deployment in drive applications.

Upon resolving many of the practical issues mentioned above, it is anticipated
that the ML-based data-driven control and monitoring schemes will be able to
deliver unparalleled performance in terms of quick exploration and domain
adaptation. Therefore, it has great potential to become the next-generation
electric machine drive technology over the existing model-driven methods
currently implemented in low-cost microcontrollers.

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