A Design of Decentralized Robust Model Reference Adaptive Control System

Decentralized Model Reference Adaptive Control System Based on Robust High-Order Estimator

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.

This paper presents the design of a robust direct model reference adaptive control (DMRAC) algorithm with focus on the satisfaction of an almost strictly positive real (ASPR) condition in the presence of plant uncertainty represented as either variations in plant parameters or unmodelled dynamics in the form of additive or multiplicative norm bounded perturbations in the transfer function. Both the time and frequency domain design conditions for a feedforward compensator have been developed utilizing the robust stability analysis and optimization methods. Illustrative examples are presented to validate the algorithm's use and applicability to control systems with parametric uncertainty or unmodelled dynamics.

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

Design of Robust Model Reference Adaptive Controller for a Wider Class of Nonlinear Systems

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.

Presents the design of a robust direct model reference adaptive control (DMRAC) algorithm with focus on the satisfaction of an almost strictly positive real (ASPR) condition in the presence of plant unmodeled dynamics. These unmodeled dynamics are represented as either additive or multiplicative norm bounded perturbations in the transfer function. Frequency domain design conditions for a feedforward compensator have been developed utilizing the so-called Q parameterization. Illustrative examples are presented to validate the algorithm's use and applicability to control systems with unmodeled dynamics.

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.

This paper presents the development of an equilibrated three-phase power source, which is able to generate sinusoidal waveforms with adjustable amplitudes and frequencies, as well as several arbitrary waveforms. The setup is based on a computer-controlled three-phase PWM inverter system, whose model is rewritten using /spl alpha//spl beta/0 transformation. A robust model reference adaptive controller (RMRAC) is used to assure system robustness and performance. Simulation and experimental results are used to show the closed-loop system performance, when different waveforms are generated under several operation conditions.

In this paper, a robust model reference adaptive controller is presented for robots based on neural network parametrization. The controller is based on applying direct adaptive techniques to a basic fixed controller for better control performance, while a sliding mode control is introduced to guarantee robust closed-loop stability. It is shown that if bounded basis function networks are used for the parallel NN, uniformly stable adaptation is assured and asymptotic tracking of the reference signal is achieved.

This paper proposes the application of a combined adaptive controller for the current control loop of a Shunt Active Power Filter (Shunt APF). The proposed approach uses a Variable Structure Controller (VS) together with a Robust Model Reference Adaptive Controller (RMRAC), leading to a VS-RMRAC algorithm. The control law u = ¿ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</sup> ¿ is comprised by two set of parameters. One set is related with the RMRAC while the other part is related with the VS controller. The VS parameters are used to improve the transient response, and its effect increases when the estimation error increases. The VS-RMRAC is experimentally tested and compared with a Dead Beat current controller in a three-phase four-wire Shunt APF.

Robust Model Reference Adaptive Control in the Presence of Parasitics

A novel model-reference robust adaptive controller that does not require a priori knowledge of the high-frequency-gain sign is proposed. The scheme is applicable to minimum-phase plants of arbitrary relative degree and ensures that in the absence of unmodeled dynamics the tracking error converges to zero and all the signals remain bounded. The control law uses a particular projected parameter vector instead of the current estimates to avoid division by zero. >

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.