Adaptive control for a sensorless permanent-magnet synchronous motor drive

This study explores the integration of the adaptive control approach with the Model Reference Adaptive Control (MRAC) method, with a view to examining the impact of this integration on the performance of dynamic systems. Adaptive control is highlighted as a versatile and robust methodology that enables systems to cope with uncertainties and adapt to rapidly changing conditions in their operating environments. As a branch of adaptive control, the MRAC method focuses on efficiently achieving desired performance objectives by adjusting system parameters based on a predefined reference model. The primary focus is on investigating how these methods can be applied to optimize the functionality of systems characterized by dynamic and variable nature, such as unmanned aerial vehicles. By addressing challenges related to uncertainties, disturbances, and adaptability, the integration of adaptive control with the MRAC method demonstrates significant potential in improving the robustness, responsiveness, and reliability of such systems. This research investigates the performance of adaptive techniques in enhancing the capabilities of modern control systems. The objective of the research is to assess the effectiveness of MRAC in terms of reference signal tracking. Simulation results demonstrate the ability of MRAC to maintain accurate tracking of the desired reference for key vehicle dynamics, including roll, pitch and yaw. The results demonstrate the robustness of the MRAC algorithm in attitude control, which may lead to support for sustainability issues such as energy consumption.

Vector (or, field oriented) control strategy has been accepted as one of the most effective methods in controlling induction motor drives. To employ vector control requires accurate motor parameters such as rotor time constant and mutual inductance. However, the motor drive performance may be degraded due to changing rotor time constant which is caused by rising temperature. In order to counter the parameter sensitivity issue and to develop more robust control methods, this paper proposes a self-tuning adaptive control strategy for vector-controlled induction motor drives. The adaptive control law is derived from a linear perturbation model. A multiprocessor-based experimental prototype is constructed to verify the proposed control method. Experimental and simulation results show superior performance of the adaptive control strategy to the conventional vector control one. >

A New Model Reference Adaptive Aircraft Controller

This paper presents a speed and position sensorless permanent magnet synchronous motor (PMSM) drive using simple and robust model reference adaptive control (MRAC) for light electric vehicle (LEV) application. The sensorless speed and position estimator consists the flux and torque based MRAC, which improves the performance during dynamic as well as steady state condition for wide range of speed operation. Mostly, PMSM drive uses field oriented control for the closed loop speed control operation. Generally, PI controller is used speed and current controller. However, the PI controller gain requirement often changes during various driving conditions of the vehicle. Therefore, this work is focused to eliminate fixed gain PI controller by utilizing online self-tuned PI speed controller and PMSM model based predictive current controller. The self-tuning approach of PI controller for the speed control loop uses the principle of adaptive interaction, which performs tuning by minimizing error objective function. The predictive current controller eliminates the need of modulator and incorporates the inverter nonlinearities. In addition to this, the sensorless approach reduces the complexity and cost of the drive. The combined system of improved controllers and sensorless estimator improves the drive performance for wide range of speed operation.

Model reference adaptive fuzzy control (MRAFC) is proposed to control the active power filter (APF) and model the APF system caused by nonlinear characteristics, variable parameters and external interferences. T-S fuzzy theory is utilized on the approximation of nonlinear model and the current tracking control. APF system based on MRAFC can be guaranteed to be asymptotical stable. Simulation results are included to confirm the effectiveness of the proposed controller and comparisons with APF system based on model reference adaptive control (MRAC) demonstrate that MRAFC method is capable of reducing the modeling error and tracking reference current signal accurately. Moreover, APF based on the proposed method shows the dynamic and static control performance of stronger robustness, better suitability and higher accuracy than the one based on MRAC modus.

Robust Adaptive Control

Recently, a class of adaptive control schemes called L1 Adaptive Control (L1-AC) has been proposed and widely advertised in aerospace control for achieving fast and robust adaptation and better performance than the existing Model Reference Adaptive Control (MRAC) Schemes. The L1-AC scheme is designed mainly for plants with full state measurement even though the name L1-AC has been used as an umbrella name for more general classes of plants. In this paper, we show that the L1-AC for plants with measured states is simply a standard MRAC with a low pass filter inserted in front of the control input. The analysis of the scheme is almost identical to that of MRAC as the same Lyapunov function is used to establish stability. The motivation for using the filter is the fact that for this class of adaptive schemes i.e. MRAC for plants with full state measurement the tracking error can be made arbitrarily small during transient by increasing the adaptive gain. A high adaptive gain however makes the differential equation of the adaptive law or estimator very stiff and leads to numerical problems that cause high oscillations in the estimated parameters leading to loss of adaptivity and deviations from what the theoretical properties dictate. The L1-AC approach mistook these numerical oscillations as properties of the adaptive scheme and inserted an input low pass filter in order to filter them out. While the filter helps reduce the frequency of these oscillations in the control law the price paid is high. First the numerical instability does not go away and the estimated parameters continue to oscillate without converging to the true parameters even in the presence of sufficiently rich signals. Second, due to the filter the tracking error is no longer guaranteed to converge to zero and the transient bounds for the tracking error also depend on the filter. As a result, the tracking properties of the L1-AC scheme are worse than what a simple MRAC scheme can generate with adaptive gains that could be high but away from the region of numerical instability. In addition, the presence of the filter reduces the robust stability margins in the presence of unmodeled dynamics and provides literally no advantage as simple robust MRAC techniques can solve the same problem achieving much better properties. The authors of L1-AC often compare the properties of a numerically unstable MRAC due to extremely high adaptive gains something that is prohibited by robust adaptive control with those of the filtered MRAC aka L1-AC to show that L1-AC performs better. Such comparisons are not only misleading but do not reveal what causes what giving the reader a false impression of a new theory that results to new performance and robustness improvements. The use of filters in adaptive control is not new and they are used to improve the performance and robustness of certain adaptive control schemes without destroying their ideal tracking properties. In this paper we present such a scheme that guarantees good transient performance and robustness and reveals the trade off between transient performance and robust stability.

Distributed model reference adaptive containment control of heterogeneous multi-agent systems with unknown uncertainties and directed topologies

The purpose of this chapter is to redesign the standard adaptive control schemes by using hybrid structure composed by Model Reference Adaptive Control (MRAC) or Adaptive Pole Placement Control (APPC) strategies, associated to Variable Structure (VS) schemes for achieving non-standard robust adaptive control strategies. The both control strategies is now on named VS-MRAC and VS-APPC. We start with the theoretical base of standard control strategies APPC and MRAC, discussing their structures, as how their parameters are identified by adaptive observers and their robustness properties for guaranteeing their stability. After that, we introduce the sliding mode control (variable structure) in each control scheme for simplifying their design procedure. These design procedure are based on stability analysis of each hybrid robust control scheme. With the definition of both hybrid control strategies, it is analyzed their behavior when controlling system plants with unmodeled disturbances and parameter variation. It is established how the adaptive laws compensates these unmodeled dynamics. Furthermore, by using simple systems examples it is realized a comparison study between the hybrid structures VS-APPC and VSMRAC and the standard schemes APPC and MRAC. As the hybrid structures use switching laws due to the sliding mode scheme, the effect of chattering is analyzed on the implementation and consequently effects on the digital control hardware where sampling times are limiting factor. For reducing these drawbacks it is also discussed possibilities which kind of modifications can employ. Finally, some practical considerations are discussed on an implementation on motor drive systems.

This study proposes integrating Large Language Models (LLMs) into the adaptation mechanism of Model Reference Adaptive Control (MRAC) systems to improve their performance. MRAC is a widely used method for controlling systems that involve uncertainty and change over time; however, fixed-gain structures cause problems like slow convergence, overshoot, and instability in rapidly changing dynamics. To overcome these limitations, three different MRAC architectures are considered: classical MRAC, low-pass filter-based MRAC, and Error-Feedback (EF) MRAC. In the proposed approach, the GPT-4o-mini model optimizes adaptation gains and error compensation coefficients in real time using current data obtained from the control loop. The methods were tested on a DC motor, a mass–spring–damper system, and a wing rock aircraft model. Simulation results show that LLM-enhanced MRAC architectures not only improve control performance but also provide a flexible and scalable framework for adaptive control design. These findings open new research avenues at the intersection of control theory and natural language processing.

The paper deals with two concepts of a model reference adaptive control (MRAC) of the induction motor drive with elastic joint. The adaptive speed controller uses fuzzy neural network equipped with additional option for on-line tuning its chosen parameters. In the paper PI-type and sliding-mode fuzzy logic controllers are used as the speed controllers, whose connective weights are trained on-line according to the error between the state variable of the plant and the reference model. The torsional vibrations dumping abilities of the two-mass drive system with the field oriented control of induction motor are tested and compared for both analyzed neuro-fuzzy adaptive controllers. It is shown that torsional oscillations can be successfully suppressed in the proposed control structures, using only one basic feedback from the motor speed. The simulation results are verified in experimental tests, in the wide range of motor speed and parameters changes.

Partial-state feedback multivariable MRAC and reduced-order designs

This paper looks at design of a controller for an induction motor electrical drive useful for high speed railway applications. The design of the controller is based on the cascade control mode using the standard model of the motor referred to d-q axes fixed with the rotor flux vector. It is assumed that the speed and the rotor flux are the variables to be controlled and their dynamics must be decoupled, which is accomplished by means of a nonlinear compensator, which gives decoupling of the direct and in-quadrature currents. According to the aforementioned cascade control mode, the bandwidth of the current inner control loops must be selected higher than that of the speed and rotor flux outer control loops. To take into consideration the remarkable uncertainties in the knowledge of the inertia coefficient, adaptive control techniques are used to design the speed control loop. To cope with uncertainties in electromagnetic parameters, robust control techniques are used for designing the rotor flux, the direct, and the in-quadrature current loops. Simulation experiments are displayed to test the entire controller.

Two new output feedback adaptive control schemes based on model reference adaptive control (MRAC) and adaptive laws for updating the controller parameters are developed for a class of linear multi-input multi-output (MIMO) systems with state delay. A controller structure established on a new error equation parametrization is proposed to achieve tracking with the error tending to zero asymptotically. To achieve exact asymptotical tracking, we introduce, in the standard MRAC structure for plants without delay, a new additional adaptive feedforward control component as an output of a dynamical system driven by the reference signal. Adaptive laws are developed using the SPR-Lyapunov design approach and two assumptions regarding the prior knowledge of the high-frequency matrix K/sub p/. This work is the first asymptotic exact zero tracking results for this class of systems in the framework of the certainty equivalence approach.

In this paper, a new distributed model reference adaptive control (MRAC) design framework is proposed for containment control of heterogeneous uncertain multi-agent systems (MAS). Both groups of leaders and followers are considered to have general linear dynamics with the leaders subject to bounded external inputs and the followers subject to uncertain system dynamics. A novel distributed adaptive control protocol is developed under this new framework, which incorporates robust adaptive control with nonlinear compensator techniques to cope with unmeasurable bounded leaders' inputs and uncertain agent dynamics. Convergence of the containment control errors to an arbitrarily adjustable neighborhood of the origin is guaranteed based on the Lyapunov method. The proposed MRAC framework provides a promising alternative solution over the prevailing cooperative output regulation framework for heterogeneous linear MAS containment control. It enables us to handle more general system settings under more stringent control environments with limited accessibility of leaders' information and uncertain follower dynamics.