This paper presents a discrete-time design methodology for an angle tracking observer based on algebraic controller tuning techniques. A piecewise model of the estimated angular velocity is first introduced to approximate its dynamic behavior across sampling intervals. The corresponding angular position is then obtained analytically through integration, leading to a discrete-time system that captures the relationship between the velocity and the angle. A one-step delay is incorporated into the feedback path and compensated by a one-step angle prediction to avoid algebraic loops in the closed-control structure. Finally, an algebraic controller is designed to assign the closed-loop poles to the selected positions, ensuring desirable dynamics and filtering of the observer. The designed observer is experimentally validated and benchmarked against the conventional structures.

The challenges of fault detection and condition monitoring in powertrain systems have become increasingly prominent, particularly with the widespread adoption of fail-operational systems. These systems are pivotal in diverse sectors, including the robotics, automotive industry, and various industrial applications. A critical attribute of such systems lies in their capability to identify non-standard behaviour of the system. This study describes a inovative conditional convolutional autoencoder-based fault detection algorithm for the permanent magnet synchronous motor. The study compares a train process of conditional convolutional autoencoder with a classical convolutional autoencoder. The presented autoencoder structure was designed to be implementable into the target microcontroller AURIX TC397 while providing sufficient recognition capabilities of the interturn short-circuit. Autoencoders are trained on data obtained during healthy motor operation and subsequently used to detect interturn short-circuit faults on the experimental dual three-phase permanent magnet synchronous motor with the possibility of emulating an interturn short-circuit fault. The paper provides insights into the achieved autoencoder inference times and the sensitivity in detecting the fault.

This paper extends the previously published parameter estimation-based approach to interturn short circuit diagnostics in permanent magnet synchronous motors by real-time monitoring of hidden machine states after fault occurrence. The designed monitoring method relies on an adaptive formulation of the Kalman filter, which assumes interdependence between measurement and process noise variables. A variable forgetting factor not only mitigates the impact of the process model uncertainty but also facilitates the simultaneous operation of the monitoring algorithm and fault indicator estimation. Furthermore, contributions of fault current and healthy machine model to stationary reference frame currents are estimated from an advanced discrete-time motor description reflecting a stator winding arrangement inside a motor’s case. The monitoring algorithm is validated in steady state, torque load transient, and velocity transient laboratory experiments with diverse fault severity values.

Intelligent motion control is integral to modern cyber-physical systems. However, smart integration of intelligent motion control with commercial and industrial systems requires domain expertise, industrial ‘know-how’ of the production processes, and resilient adaptation for the various engineering phases. The challenge is amplified with the adoption of advanced digital twin approaches, big data and artificial intelligence in the various industrial domains. This paper proposes the IMOCO4.E reference framework for the smart integration of intelligent motion control with commercial platforms (e.g. from SMEs) and industrial systems. The IMOCO4.E reference framework brings together the architecture, data management, artificial intelligence and digital twin viewpoints from the industrial users of the large-scale ‘Intelligent Motion Control under Industry4.E’ (IMOCO4.E) consortium. The framework envisions a generic platform for designing, developing, and implementing novice and complex motion-controlled industrial systems. Refinements and instantiations of the framework for the IMOCO4.E industrial cases validate the framework’s applicability for various industrial domains throughout the engineering phases and under different constraints imposed on the industrial cases.

The problem of condition monitoring and fault detection in powertrain systems becomes more critical with the increasing use of fail-operational systems. These systems are essential in the automotive industry, robotics, and other industrial applications. One of the critical features of such a system is recognizing the fault and suppressing its influence. The paper describes a feed-forward artificial neural network-based diagnostic of interturn short-circuit faults in a dual three-phase permanent magnet synchronous motor. The paper focuses on using multi-layer perceptron network (MLPN) and convolutional neural network (CNN) for interturn short-circuit detection and, more importantly, their real implementation into the automotive AURIX TC397 microcontroller. The paper presents the achieved neural network inference times as well as data preprocessing computation time. The behavior of the artificial neural networks (ANNs) is tested on an experimental configurable multi-phase permanent magnet synchronous motor (PMSM) with the possibility to emulate interturn short-circuit fault using prepared winding taps. The paper includes the essential aspects that should be respected during ANN design and implementation into the microcontroller.

This paper presents novel implicit and explicit discrete-time permanent magnet synchronous motor models. Both derived models solve the problem of numerical instability and poor precision of motor currents' discrete-time prototypes formed using the forward Euler method and preserve the tolerable complexity of resulting descriptions. Discrete-time models of currents are derived based on the linear time-varying systems approach, considering the electrical angular velocity time-varying parameter. Angular velocity and angle are discretized by using the linear multistep methods. The implicit variant of the model is dedicated to parametric estimation tasks, and the explicit variant is to model predictive control. The derived descriptions are validated within the simulation by comparing the original continuous-time model and Euler approximation with the explicit model. Furthermore, the prediction capabilities of the explicit model and Euler approximation are compared as well.

In this study, model of a multi-phase permanent magnet synchronous motor is demonstrated. The model is designed to allow simulations of inter-turn short circuit faults of various severity. The considered multi-phase motor is connected as a dual three-phase system. In this case, stator windings are divided into two electrically separated sub-systems. Nevertheless, the magnetic coupling between sub-systems needs to be taken into account in general. The motor model is designed to take into account the exact stator coils distribution. In this case motor uses combination of serial and parallel connections. Model is implemented in MATLAB/Simulink using SimScape environment. The paper also shows a comparison of the behavior of a real PMS motor and derived model behavior.

Permanent magnet synchronous machines (PMSM) are widely used for their high efficiency and power density. High machine reliability is increasingly required nowadays. PMSMs can't be simply disconnected from the power source unlike asynchronous machines if any fault appears in the system. Many motor faults lead to malfunction of the whole system. This limitation can be solved by using multi-phase structures instead of commonly used three-phase structures. Multi-phase machines have many advantages in terms of high reliability. The power density of properly designed multi-phase system is also higher. The multi-phase PMSM can operate even during a fault, under certain conditions, depending on the motor parameters. The control algorithm must be capable to detect the fault and apply proper control method according to the detected fault.This paper demonstrates the behaviour of the dual three-phase PMSM motors under various inter-turn short circuit faults. An experimental PMSM was prepared, having stator windings with multiple taps. These winding taps are utilised to emulate inter-turn short circuit faults. The winding short-circuiting is realized by a solid-state relay. The fault influence is analysed on the experimental machine.Three compensation strategies are applied to reduce the fault influence. All compensation strategies are based on field weakening of the damaged motor part. The motor behaviour during the fault without compensation method is compared with the behaviour when using compensation strategies. Realised experiments demonstrate that a properly constructed dual three-phase PMSM under control with compensation strategies can continuously operate under the fault condition.

Importance of the condition monitoring and predictive maintenance in motion systems is growing up as motion systems quantum and their complexity (number of axes, performance parameters) increases with increasing the automation of huge range of human activities and manufacturing processes. Probability of failures increases with the system complexity. Many faults and indication of their propagation in the electric drives would require additional sensors or hardware, higher bandwidth and sampling frequencies of feedback sensors, high computing power etc. for development of sophisticated methods to detect specific faults with good sensitivity, robustness and reliability under any operating condition. This paper presents an approach to the condition monitoring and prognosis applicable into the existing systems. These methods use the information available in the traditional electric drives - especially the information from the individual sensors in a voltage source inverter (VSI) and/or an electric motor. Condition indicators for these methods are based on application specific operating states or actions, which generates typical patterns in the signals. The condition monitoring is based on observing the deviations of these patterns between the healthy system and the system with fault propagating. The implementation strategy is described in the paper and some demonstration examples are shown as well.

AC induction motors became very popular for motion control applications due to their simple and reliable construction. Control of drives based on AC induction motors is a quite complex task. In most high-performance applications classical vector control is currently used. While this control method is usually reliable it has some limitations especially in controllers tuning and constraints handling. New control methods like Model Predictive Control become feasible in connection with increasing computational power of controller hardware. The paper deals with enhanced discrete time AC induction machine model which can be used for efficient predictive control implementation. The other objective of the paper is discussion of prediction horizon length on the drive control performance.