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The paper is concerned about parameter adaptation of a novel, simplified and nonlinear switched reluctance motor (SRM) model. The purpose of the presented on-line procedure is to give an opportunity to set the model parameters’ values to obtain a relatively good convergence with the real control object. This is important when a reference model is used for control (e.g. optimal) or object state classification (e.g. fault detection) purposes. The more convergent the real object model is, the better operation quality may be expected.

In the paper, a 12/8 pole’s SRM as a control object is analyzed. The model equations were verified experimentally by comparing phase current model estimations with reference (measured) ones at different operational points. Differential equations of motor winding currents were chosen as an approximation function in the fitting (parameter adaptation) process using the Newton and Gauss–Newton methods. The structure of the adaptation system is presented along with the implementation in simulation environment.

It was confirmed in the simulation tests that Newton and Gauss–Newton methods of nonlinear model parameters’ adaptation may be used for the SRM. The introduced fitting structure is well suited for implementation in real-time, embedded systems. The proposed approximation function could be used in process as an expansion to Jacobian and Hessian matrices. The χ² (chi²) coefficient (commonly used to measure the quality of the signal fitting) reduced to a low value during the adaptation process. Another introduced quality coefficient shows that the Newton method is slightly better in scope of the entire adaptation process time; however, it needs more computational power.

The proposed structure and approximation function formula in the parameters’ adaptation system is appropriate for sinusoidal distribution of the motor phase inductance value along the rotor angle position. The inductance angular shape is an implication of the mechanical construction – with appropriate dimensions and materials used. In the presented case, the referenced model is a three-phase SRM in 12/8 poles configuration used as a main drive part of Maytag Neptune washing machine produced by Emerson Motors.

The presented method of parameter adaptation for novel, simplified and nonlinear SRM model provides an opportunity for its use in embedded, real-time control systems. The convergent motor model, after the fitting procedure (when the estimations are close to the measurements from real object), may be used for solving many well-known control challenges such as detection of initial rotor position, sensorless control, optimal control, fault-tolerant control end in fault detection (FD) systems. The reference model may be used in FD in the way of deducing signals from the difference between the estimated and measured ones.

The paper proposed a new system of parameter adaptation for the evaluated nonlinear, simplified 12/8 poles SRM model. The relative simplicity of the proposed model equations provides the possibility of implementing an adaptation system in an embedded system that works in a real-time regime. A Two adaptation methods – Newton and Gauss–Newton – have been compared. The obtained results shown that the Newton fitting method is better in the way of the used quality indicator, but it consumes more computational power.

When phase windings of brushless DC motor are switched, additional voltage drops across inductances of main circuit appear. These drops lead to, among other effects, increase of torque‐speed curve slope. The discussed research has been aimed at working out a simple and precise method of identifying torque‐speed characteristic of PM BLDC motor. The elaborated method takes into account the influence of windings switching and motor inductances on motor torque‐speed characteristic. In order to assess the results, extensive test simulations of models implemented in Matlab/Simulink software have been run. Results of analysis and test simulations have been compared with lab test results of two real PM BLDC motors.

Analytical calculations take into consideration phenomena occurring during windings switch‐overs and impact of inductance on emerging voltage and rotational speed drops. It has been pointed out that on account of main circuit inductance, the average value of source current is less than average value of equivalent current generating electromagnetic torque. For analysis sake it has been assumed when windings are being switched‐over the current is kept constant; the motor parameters have also been assumed to be constant.

A novel and accurate method of determining torque‐speed characteristics of PM BLDC motor has been worked out. This method has been investigated with the help of motor computer models implemented in Matlab/Simulink software and the obtained results have been subsequently compared with results of laboratory tests of two commercially available PM BLDC motors.

The object of the research was brushless DC motor with permanent magnet excitation. The impact of windings switch‐overs on torque‐speed curves of the motor has been analysed. Analytical method which makes it possible to determine torque‐speed curve of this motor very easily has been elaborated. Computer model of PM BLDC motor for Matlab/Simulink software has also been worked out. Extensive simulations helping to verify the proposed method have been run. Results of analysis and simulation tests have been verified by means of laboratory tests of two commercially available PM BLDC motors.

PM BLDC motors are used more and more widely. The new method of determining PM BLDC motors torque‐speed curves will facilitate analysis and design of drive systems utilizing these motors and will also speed up calculations.

The presented method of determining torque‐speed curves of PM BLDC motor is novel and much more precise than methods commonly used nowadays. Recognized methods usually neglect impact of inductance on motor properties.

The main purpose of the paper is to develop model basing on the modified and properly‐adopted Fermi‐Dirac equation which combines proper accuracy with adequate simplicity as well as to show how steady state and transient curves resulting from this model can be applied for solving design task.

The standard Fermi‐Dirac equation was modified and extended. Full performance cycle for the SMA actuator was characterized by double‐valued function describing the actuator activation and the actuator deactivation. All these functions and parameters can be easily determined by analysis of measurement data or with use of Hooke‐Jeeves optimization algorithm.

SMA linear actuator can be used in mechatronic systems as a special non‐standard drive when ultra‐light mass and very simple mechanical construction of power feed system is required. The proposed steady‐state and transient performance curves as well as operation diagram constitute sufficient base for effective designing SMA drive systems.

The greatest disadvantage of a SMA actuator is long time of deactivation resulting from slow self‐cooling process. As far as efficiency is concerned as essential factor for choosing the most suitable linear actuator, there is no sense to take into account a linear SMA actuator because of its very low efficiency.

Designer can use performance curve which determines proper length of SMA actuator and range of its motion. The proposed model can be implemented in SMA drive control unit for controlling position of the actuator.

Similarities between change of martensitic phase during transition process and probability P of electron energy level distribution described by the Fermi‐Dirac two‐variable equation were taken into account. Such an approach seems to express in the most suitable way the physical nature of m‐a transition. The authors decided to extend concept (proposed in Jayender et al.) and to adopt the Fermi‐Dirac equation for describing behaviour of a SMA linear actuator.

The paper proposes to deal with the problems concerning the N‐phase (N+1)‐wire system with sinusoidal voltage sources and nonlinear loads. In the model of the N‐phase voltage source the inner impedance has been included. The problem of the optimization of working conditions of the system is a minimization of RMS value of its line currents as well as their distortions caused by nonlinear loads.

The solution of this problem is based on the frequency domain. It is obtained by means of Lagrange's multipliers and the suitable measurement experiment.

After optimization source currents are sinusoidal with minimized RMS values. After connection of the designed compensator to the system under research the phase currents are equal to determined active currents.

This method can be used for some classes of nonlinear loads, i.e. for systems with inertialess (non‐reactive) elements, which consume the active power of the basic harmonic of the voltage source and where the currents of the system are periodical. The mentioned power is an additional constraint of the presented minimization.

The working point of the system can be obtained by means of the compensator LC, RLC or (RLC,‐R). It will always be a linear one and its structure consists of two components: elements with parameters determined for the basic harmonic and the filter for elimination of the higher harmonics caused by nonlinear loads.

The presented method has been generalized for N‐phase (N+1)‐wire systems.

The purpose of this paper is to elaborate a method of computer analysis of high‐speed motor with specific parameters and verifying the obtained results, i.e. computer models by experimental (laboratory) tests.

In order to determine motor properties from the viewpoint of energy conversion, a model using FEM was worked out with the help of Maxwell software. To determine static and dynamic properties of both motor and drive, Matlab/Simulink models were used; one of these models was a built‐in (library) model, the other one was proposed by the authors.

The new analysis method and model of high‐speed motor have been carried out.

The permanent magnet brushless direct current high‐speed motor was the subject of the research. In the first part of the research, the properties of the motor were determined by using finite element method.

The laboratory prototype can be a starting point in establishing the production of the high‐speed motors with rotational speed in the range of 50,000‐100,000 rpm.

At this moment, there are several possible application of the high‐speed motor and it should be expected that other new applications can appear in near future after the start of the production.

The paper shows that the computer‐based analysis method determines the motor properties accurately. It is also pointed out that a motor with half‐open slots has advantageous properties. The new simulation model of high‐speed motor has been carried out. This model allows taking into account some imperfections caused by slots and rectangular cross‐section magnets.

The purpose of the paper is to find a simple structure of speed controller robust against drive parameter variations. Application of neuro‐fuzzy technique in the controller of PI type creates proper nonlinear characteristics, which ensures controller robustness.

The robustness of the controller is based on its nonlinear characteristic introduced by neuro‐fuzzy technique. The paper proposes a novel approach to neural controller synthesis to be performed in two stages. The first stage consists in training the neuro‐fuzzy system to form the proper shape of the control surface, which represents the nonlinear characteristic of the controller. At the second stage, the PI controller settings are adjusted by means of the random weight change procedure, which optimises the control quality index formulated in the paper. The synthesis is performed using simulation techniques and subsequently the behavior of a laboratory speed control system is validated in the experimental setup. The control algorithms of the system are performed by a microprocessor floating point DSP control system.

The proposed controller structure with proper control surface created by the neuro‐fuzzy technique guarantees expected robustness.

The proposed controller was tested on a single machine under well defined conditions. Further investigations are required before any industrial applications can be made.

The proposed controller synthesis and its results may be very helpful in the robotic system where changing of system parameters is characteristic for many industrial robots and manipulators.

The original method of robust controller synthesis was proposed and validated by simulation and experimental investigations.

The aim of the research was to find out a method of adaptive speed control robust against variation of selected parameters of system like moment of inertia, time constant of torque control loop or torque coefficient of the motor.

The main goal of the research was achieved due to application of artificial neural network (ANN), which was trained on line on the base of speed control error. The good results were gained by elaboration of enough fast and precise training algorithm and proper ANN structure.

The work shows a structure of artificial neural network (ANN), applied as adaptive speed controller, and presents an algorithm of ANN training. Some versions of this algorithm were analysed and verified by simulation and experimental tests.

The research should be continued to determine a final version of training algorithm and its influence on controller properties.

The elaborated adaptive controller can be easily used by applying microprocessor system available now on the market. The proposed control solution is robust against parameters variation as well as their imprecise identification. The controller has ability of self‐tuning which can have great practical advantage.

Social implications are difficult to determine.

The paper presents a new solution of adaptive speed controller, which means a new ANN structure and new training algorithm.

The aim of the paper is to find a simple structure of speed controller robust against drive parameters variations. Application of artificial neural network (ANN) in the controller of PI type creates proper non‐linear characteristics, which ensures controller robustness.

The robustness of the controller is based on its non‐linear characteristic introduced by ANN. The paper proposes a novel approach to neural controller synthesis to be performed in two stages. The first stage consists in training the ANN to form the proper shape of the control surface, which represents the non‐linear characteristic of the controller. At the second stage, the PI controller settings are adjusted by means of the random weight change (RWC) procedure, which optimises the control quality index formulated in the paper. The synthesis is performed using simulation techniques and subsequently the behaviour of a laboratory speed control system is validated in the experimental set‐up. The control algorithms of the system are performed by a microprocessor floating point DSP control system.

The proposed controller structure with proper control surface created by ANN guarantees expected robustness.

The original method of robust controller synthesis was proposed and validated by simulation and experimental investigations.

The purpose of the paper is to find a speed control structure with two degrees of freedom robust against drive parameters variations. Application of structure model following control (MFC) and fuzzy technique in the controller of PI type creates proper non‐linear characteristics, which ensures controller robustness.

The use of proper structure with two degrees of freedom and non‐linear characteristic introduced by fuzzy technique ensures the robustness of the speed control system. The paper proposes a novel approach to MFC synthesis to be performed in two stages. The first stage consists in the set value of P type controller of model and the process controller simultaneously should be designing by fuzzy technique. At the second stage of the synthesis consist in tuning parameters of process fuzzy controller by the swarm of particles method (particle swarm optimization) on the basis of a defined quality index formulated in the paper. The synthesis is performed using simulation techniques and subsequently the behavior of a laboratory speed control system is validated in the experimental setup. The control algorithms of the system are performed by a microprocessor floating point DSP control system.

Use of proper structure with two degrees of freedom of the non‐linear fuzzy controller guarantees expected robustness and improves the dynamics of speed control significantly.

The proposed structure of MFC was tested on a single machine under well‐defined conditions. Further investigations are required before any industrial applications.

The proposed controller synthesis and its results may be very helpful in robotic system where changing of system parameters is characteristic for many industrial robots and manipulators.

The paper proposes an original method of synthesis of robust system with two degrees of freedom system validated by simulation and experimental investigations.

Various control structures and approaches are in use nowadays. Development of new ideas allows to obtain better quality in control of different industrial processes and hence better quality of products. As it may seem that everything in the classical systems has already been discovered, more and more research centres are tending to incorporate fuzzy or neural control systems. The purpose of this paper is to present an application of the adaptive neuro-fuzzy PID speed controller for a DC drive system with a complex nonlinear mechanical part.

The model of the driven object including such elements as nonlinear shaft with backlash and friction has been modelled using Matlab-Simulink software. Afterwards experimental verification has been made using a dSPACE control card and experimental system with two DC motors connected with an elastic shaft.

The presented study shown that the adaptive controller is able to damp the torsional vibration effectively even for the wide range of the system nonlinearities. What is more the design approach for controllers design parameters has been described. Proposed approach is based on requested properties of system. Using proposed tuning scheme no detailed information about the object are needed.

This paper presents for the first time fully an PID adaptive neuro-fuzzy controller. The inputs are the weighted tracking error, error’s derivative and integrated error. What is more the adaptation algorithm consists of a model tracking error its derivative and integer. Also the proposed tuning algorithm in such a form is an original outcome.