Robust model reference adaptive controller for 3-DOF planar manipulator.

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.

This paper investigates the use of neural networks (NNs) in conventional model reference adaptive control (MRAC) to control a nonlinear magnetic levitation system. In the conventional MRAC scheme, the controller is designed to realize plant output convergence to a reference model output based on a plant which is linear. This scheme is effectively for controlling linear plants with unknown parameters. However, using MRAC to control the nonlinear magnetic levitation system in real time is a difficult control problem. In this paper, we incorporate a NN in MRAC to overcome the problem. The control input is given by the sum of the output of the adaptive controller and the output of the NN. The NN is used to compensate the nonlinearity of the plant that is not taken into consideration in the conventional MRAC. From experiment results, it has been shown that the plant output can converge to the reference model output after using NN in MRAC.

Comments on a robust model reference adaptive control for non-minimum phase systems with unknown or time-varying delay

A recently introduced class of the well-posed error systems and the corresponding robust model reference adaptive control (MRAC) laws for systems represented by parabolic or hyperbolic partial differential equations (PDEs) with spatially varying parameters is extended to encompass the disturbance rejection capability. The disturbance is modeled as a space-time-varying signal generated by a parabolic PDE with known parameters or as a spatially-varying time-invariant signal.

With the developments of industrial automation in recent years, vehicle suspension systems have received a great deal of attention in industry and academia due to their critical role in the chassis performance of vehicles [1]. The suspension system is expected to guarantee the vehicle's maneuverability and provide satisfactory ride comfort by absorbing the vibrations arising from the road surface excitations and ensuring road-holding capability and suspension safety. Motivated by the desirable performance of the model reference adaptive control (MRAC) approach, various literature studies have investigated its performance in diverse linear and nonlinear practical systems [2], [3] (for more background review, see Supplementary material-Appendix A). Aiming to improve the conventional MRAC in terms of closed-loop stability, asymptotical tracking, and robustness against uncertainties and external disturbances, authors in [4] developed the tube-based MRAC (T-MRAC) approach. To simultaneously achieve the desired objectives, the developed scheme split the control signal into two main terms; an adaptive part and another part that corrects the control objective. In addition, an optimization problem is formulated to find the newly determined correction control component. However, although the method's performance was verified using some simple single-input single-output unstable systems [5], more extensive investigations on complex practical systems are needed to validate its performance. The main contributions of this paper are to develop a novel tube-based MRAC approach for multi-input multi-output active suspension systems; a realistic design that guarantees ride comfort and suspension safety and achieves better control performance with reduced control effort in the presence of external disturbances compared to conventional methods. Moreover, to validate the method's applicability in practical applications, three different road profiles are conducted to simulate distinct practical road situations, as the bump-dip road, random road, and sinusoidal profiles. Furthermore, the vibration suppression performance of the developed adaptive control scheme is investigated, and superior simultaneous ride comfort, road handling, and suspension safety are guaranteed compared to conventional MRAC [6] and SMC [7] approaches.

In this research, the positioning control of a water hydraulic servo cylinder system with the simple adaptive control (SAC) is discussed. Conventional model reference adaptive control (MRAC) also achieves good tracking performance for cylinder control, but the controller structure is much more complicated and has less robustness for disturbance in real application. Compared with MRAC, SAC has simpler and lower order structure, i.e. higher feasibility. In this paper, we examined and evaluated the control performance of SAC on a water hydraulic servo cylinder system. With the recent increased concerns over global environmental problems, the water hydraulic technique using pure tap water as a pressure medium has become a new drive source comparable to electric, oil hydraulic and pneumatic drive systems. This technique is also preferred due to its high power density, high safety against fire hazard in production plants, and easy availability. However, the main problems for precise control in a water hydraulic system are steady state errors and overshoot due to its large friction torque and considerable leakage flow. To compensate these effects, MRAC had been already applied and the better control performances were obtained. However, no result has been reported on SAC application for water hydraulics. To make clear the merits of SAC, tracking control performance and robustness were discussed via experimental results. It was confirmed that SAC gives (1) better tracking performance compared with PI control, and (2) comparable control precision with MRAC within 70 micrometers to the reference position and higher robustness for parameter change despite simple controller. These results ensure wider application of simple adaptive control in real mechanical system.

This paper investigates a robust model reference adaptive control (MRAC) method for a three-phase constant-voltage constant-frequency (CVCF) inverter with an output LC filter. The proposed MRAC method is designed to stabilize the error dynamics of the system by a feedback control term in the steady state and attenuate the parameter uncertainties of the system by an updated MRAC term. Unlike the conventional proportional-derivative control (PDC) scheme, the proposed MRAC scheme ensures the fast convergence of the output errors to the exponential trajectories predefined by the reference models. Furthermore, the adaptive state-feedback mechanism can guarantee the fast dynamic response in the transient state without using load current sensors or observers. The asymptotic stability is mathematically proven by a Lyapunov theory. The feasibility of the proposed controller is confirmed through extensive experimental studies on a prototype three-phase CVCF inverter with a TI TMS320LF28335 DSP. Finally, comparative experimental results of three control methods (i.e., conventional PDC, feedback linearization control, and proposed MRAC) are provided to validate the superior performance of the proposed method such as fast transient response, low total harmonic distortion, and robustness to parameter uncertainties under critical load conditions (i.e., abrupt load changes, unbalanced loads, and distorted nonlinear loads).

Recently, variable structure design (VSD) has been intensively researched in the literature of model reference adaptive control (MRAC) for its well-behaved transient response and appealing tracking performance. In the recent work by Hsu [6], parameter switching mechanism has been introduced in the design of model reference controller to obtain better transient behavior. By taking a different viewpoint, we apply VSD to the control channel, and develop a new robust model reference adaptive controller for a class of uncertain plants with arbitrary relative degree. It is shown that in the presence of additive, multiplicative unmodeled dynamics, and bounded disturbance, the output tracking error can still be guaranteed to converge to zero with an exponential rate. Since the parameter update law is introduced in the synthesis of this new adaptive control law, the magnitude of the switching control forces can be reduced to a more realistic level for implementation. To highlight the improved performance, simulation results are conducted with an uncertain plant with relative degree two.

For a class of discrete-time systems with unmodeled dynamics and bounded disturbance, the design and analysis of robust indirect model reference adaptive control (MRAC) with normalized adaptive law are investigated. The main work includes three parts. Firstly, it is shown that the constructed parameter estimation algorithm not only possesses the same properties as those of traditional estimation algorithms, but also avoids the possibility of division by zero. Secondly, by establishing a relationship between the plant parameter estimate and the controller parameter estimate, some similar properties of the latter are also established. Thirdly, by using the relationship between the normalizing signal and all the signals of the closed-loop system, and some important mathematical tools on discrete-time systems, as in the continuous-time case, a systematic stability and robustness analysis approach to the discrete indirect robust MRAC scheme is developed rigorously.

On the Discrete Adaptive Posicast Controller

Robust Model Reference Adaptive Control in the Presence of Parasitics

A Robust State Feedback Adaptive Controller with Improved Transient Tracking Error Bounds for Plants with Unknown Varying Control Gain

Neste artigo é apresentado o Controlador em Modo Dual Adaptativo Robusto (DMARC). O DMARC é um sistema de controle que combina as estratégias de Controle Adaptativo por Modelo de Referência (MRAC) com leis integrais de adaptação e o Controle Adaptativo por Modelo de Referência e Estrutura Variável (VS-MRAC) com leis chaveadas de acordo com a teoria de sistemas com estrutura variável. A idéia básica é incorporar as vantagens de desempenho transitório de um VS-MRAC, com as propriedades de regime permanente de um MRAC convencional. Desta forma, obtém-se um sistema de controle robusto a incertezas paramétricas e perturbações externas, com desempenho rápido e pouco oscilatório durante o transitório e um sinal de controle suave em regime permanente.

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.

ABSTRACTIn this paper, we address the robust control design problem for nonlinear dynamical systems tracking unreliable reference signals. Specifically, we present robust model reference adaptive control laws that guarantee uniform ultimate boundedness of the trajectory tracking error for nonlinear plants that are affected by matched, unmatched, and parametric uncertainties, and are subject to constraints on the state space and the measured output. These control laws guarantee satisfactory results even in case the reference trajectory or the reference output signal do not verify the given constraints and hence, may draw the plant's trajectory or measured output outside their constraint sets. A numerical example involving the attitude control of a spacecraft illustrates the feasibility of the theoretical results presented.