Direct model reference adaptive pole placement control with exponential weighting properties

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

This paper proposes a nonlinear fighter aircraft pitch rate tracking control scheme based on a Multiple Fuzzy Reference Model Adaptive Controller. The scheme consists of a direct Model Reference Adaptive Control (MRAC) with the reference model generated online using a fuzzy logic algorithm. The proposed control provides a soft multiple reference model switching for the aircraft multiple modes of operation, without prohibitive computational complexities or explicit system identification. In case of aircraft mode swings or sudden jumps, the reference model will be changed or generated based on the fuzzy logic scheme looking at the plant output and auxiliary parameters. Further the MRAC adaptively controls the plant and tracks the input pitch rate command signal effectively. The main advantage of the proposed scheme is the use of fuzzy generator to change the reference model as opposed to changing the controllers. The effectiveness of the proposed technique is assessed by simulation studies based on a 6- DOF high performance fighter aircraft undergoing pitch rate control at a wide range of operating contingencies.

It is frequently convenient to use specially structured nonminimal models for parameter estimation such as in the case of direct adaptive control where the model is parameterized in terms of the desired control law parameters. This is done, for example, in direct model reference adaptive control and in direct pole assignment adaptive control algorithms. It is shown that the parameters appearing in these nonminimal models can be uniquely estimated if and only if a certain design identity has a unique solution. The result is used to develop persistency of excitation results for these models.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Bivalirudin is a direct thrombin inhibitor used in the cardiac intensive care unit when heparin is contraindicated due to heparin-induced thrombocytopenia. Since it is not a commonly used drug, clinical experience with its dosing is sparse. In earlier work [1], we developed a dynamic system model that accurately predicts the effect of bivalirudin given dosage over time and patient physiological characteristics. This paper develops adaptive dosage controllers that regulate its effect to desired levels. To that end, and in the case that bivalirudin model parameters are available, we develop a Model Reference Control law. In the case that model parameters are unknown, an indirect Model Reference Adaptive Control scheme is applied to estimate model parameters first and then adapt the controller. Alternatively, direct Model Reference Adaptive Control is applied to adapt the controller directly without estimating model parameters first. Our algorithms are validated using actual patient data from a large hospital in the Boston area.

In this paper, we introduce a direct fractional order adaptive control design based on model reference adaptive control (MRAC) structure for a class of commensurate fractional order linear systems with an arbitrary relative degree, and whose parameters are unknown. By generalising the application of standard direct MRAC strategy to plants described by fractional order models, we develop a fractional adaptive control scheme (FOMRAC) based on the output feedback. We also define an adaptation control law ensuring the stability of the closed-loop system and the good tracking of the reference trajectory. The asymptotic stability of the fractional order control system is proven using an extension of the Lyapunov theorem. Simulation results show the effectiveness of the proposed control method even for plants with model parametric variations and additive noises.

A multiple fuzzy reference model adaptive controller (MFRMAC) is proposed for nonlinear aircraft pitch-rate tracking. The controller is developed using a direct model reference adaptive control (MRAC) scheme with variable fuzzy logic reference model. The proposed controller provides a soft reference model switching for the multiple modes of operation of the aircraft without any prohibitive computation or explicit system identification. The effectiveness of the technique is assessed by simulation studies based on a 6-DOF high-performance fighter aircraft model undergoing pitch-rate control at a wide range of operating contingencies. It is demonstrated that the scheme performs extremely well when controlling multimodal dynamic systems, as opposed to conventional MRAC designs.

This paper analyzes a direct adaptive pole placement control for linear stable minimum-phase systems with input magnitude and rate constraints. The key feature is the derivation of a governing equation relating the prediction error to the "input discrepancy" between the adaptive control and corresponding non-adaptive one, independent of how parameter estimates were obtained. With the governing equation, one is able to establish the input matching property of the adaptive constrained control, i.e., asymptotic convergence to zero of the "input discrepancy".

This paper is focused on adaptively controlling a linear infinite-dimensional system to track a finite-dimensional reference model. Given a linear continuous-time infinite-dimensional plant on a Hilbert space with disturbances of known waveform but unknown amplitude and phase, we show that there exists a stabilizing direct model reference adaptive control law with the properties of certain disturbance rejection and robustness. The plant is described by a closed, densely defined linear operator that generates a continuous semigroup of bounded operators on the Hilbert space of states. The central result will show that all errors will converge to a prescribed neighborhood of zero in an infinite-dimensional Hilbert space. The result will not require the use of the standard Barbalat's lemma which requires certain signals to be uniformly continuous. This result is used to determine conditions under which a linear infinite-dimensional system can be directly adaptively controlled to follow a reference model. In particular, we examine conditions for a set of ideal trajectories to exist for the tracking problem. Our results are applied to adaptive control of general linear diffusion systems described by self-adjoint operators with compact resolvent.

This paper describes the development of several controllers to handle a trajectory tracking problem for a differentially wheeled robot. Both simulations and tests on a real robot were performed. A simple kinematic controller has been implemented to calculate desired velocities based on current position and trajectory. In order to also consider the current velocities, i.e. the dynamics of the system, the output of this controller was used as input to a dynamic controller derived from a nonlinear model. The dynamic controller was made adaptive by using an on-line parameter estimation scheme to estimate the unknown parameters of the nonlinear model. Lastly, a direct model reference adaptive controller (MRAC) based on a linear model was derived and implemented as an alternative to the adaptive dynamic controller.

The force tracking error is dependent on both the position tracking error and the estimated parameter error so that, in constrained adaptive robot control, the convergence of the estimated parameter error becomes more important than in unconstrained adaptive robot control. However, in the direct adaptive controller, the parameter adaptation is driven only by the tracking error in the joint motion, while in the indirect adaptive controller, the parameter estimation is driven only by the prediction error in the joint torque. Based on this observation a new combined direct and indirect adaptive controller driven by both the tracking error and the prediction error is developed for constrained robots with inertia parameter uncertainty. Stability of the combined adaptive controllers is established in the Lyapunov sense. The combined control improves both position and force tracking performance compared with direct adaptive control. The computer simulations are presented to demonstrate the proposed schemes.

As control systems become integrated with structures in such applications as vibration suppression and structural shape modification, a damage tolerance/mitigation concern must be addressed. The fundamental issue of this concern is whether the controller is robust enough to remain stable after a step change in the structural parameters as a result of impact damage. A secondary issue is whether the controller can continue to perform its intended function (even if at some reduced level of effectiveness) following impact damage. Due to the complexity of the structural system such a controller must be lower-order than the structure that is controlled. This paper builds on previous research by Waters and Balas to demonstrate the effectiveness and limitations of a reduced- order direct model reference adaptive control (DMRAC) algorithm for damage mitigation. The reduced-order model matching conditions for implementing this algorithm and the theoretical stability results are presented. Also presented are simulation results demonstrating the effectiveness of the DMRAC algorithm in forcing a higher-order damaged plant to track a lower-ordered reference model. These simulation results look at matching a second-order plant to a first-order reference model, and a fourth-order plant to both a first- and second-order reference model. The results of these simulations indicate that a reduced-order DMRAC algorithm can, in some cases, effectively stabilize the system following severe damage to the structure, even if the system become inherently unstable. Additionally, the reduced-order DMRAC algorithm still allows for steady state tracking by the damaged smart structure of a reference input.

The problem of direct model reference adaptive control for multi input multi output (MIMO) uncertain plants with unknown high frequency gain matrix (HFGM) is addressed in the paper. Two schemes of adaptive control algorithms using augmented error estimators and relaxed assumption concerning HFGM are proposed. It is assumed that the minimum of modulus of HFGM determinant is known. The first scheme involves the resettable approximation of the inversion of HFGM estimate in control law. The second scheme is based on modification of conventional augmented error estimator by introduction of a switched projection mechanism. The proposed theoretical ideas are wrapped up by simulation examples.

Presents an approach to the design and real-time implementation of an adaptive controller for a robotic manipulator based on digital signal processors. The Texas Instruments DSP (TMS320C31) chips are used in implementing real-time adaptive control algorithms to provide enhanced motion control performance for robotic manipulators. In the proposed scheme, adaptation laws are derived from the direct model reference adaptive control principle based on the improved Lyapunov second method. The proposed adaptive controller consists of an adaptive feedforward and feedback controller and PI-type time-varying auxiliary control elements. The proposed control scheme is simple in structure, fast in computation, and suitable for real-time control. Moreover, this scheme does not require any accurate dynamic modeling nor values of manipulator parameters and payload. Performance of the proposed adaptive controller is illustrated by simulation and experimental results for an industrial robot with four joints in the joint space and Cartesian space.

In an effort to increase the efficiency and power density of electric vehicles and aircraft, the automobile and aerospace industries are adopting variable-speed generators as part of the powertrain. Herein, the engine directly drives a permanent magnet synchronous machine, which generates a variable-frequency/amplitude output ac-voltage. More specifically, this unique ac-voltage profile serves as the input for an ac-dc power converter that energizes the main dc-bus of the powertrain. In this paper, a smart active rectifier for hybrid/all-electric powertrains is presented. A direct model reference adaptive control (MRAC) scheme is utilized to address the rapid changes in amplitude and frequency while regulating the dc-bus voltage at unity power factor. Moreover, the MRAC requires minimal tuning due to its capability of adjusting its gains adaptively with the input voltage variations. As a result, the MRAC exhibits seamless dynamics. The performance of the MRAC is verified experimentally under accelerating and decelerating variable-frequency/amplitude input voltage ramps using a laboratory-scale 2-kW 270-V SiC-MOSFET-based power converter supplied by a programmable grid emulator. Lastly, a classic proportional-integral (PI) control scheme is implemented experimentally with the same converter as a benchmark to highlight the merits of the proposed converter.

SUMMARY We study the convergence properties of a direct model reference adaptive control system by applying techniques from numerical analysis. In particular, a first-order discrete system coupled to a minimal control synthesis algorithm discretized by the one-step one-stage zero-order-hold sampling is studied. This results in a strongly non-linear dynamic system owing to the adaptive mechanism where stability at steady state, i.e. at the operating point, equates to successful control. This paper focuses on the convergence analysis of the overall dynamical system for understanding accuracy, stability and performance at steadystate. The local stability of the steady state solution is considered by linearizing the system in the neighbourhood of an operating point when the input is a step function. This analysis allows us to specify two gain space domains which define the region of local stability. Moreover, both the accuracy and the frequency-domain analyses give insight into the range of adaptive control weightings that results in optimal performance of the minimal control synthesis algorithm and also highlights a possible approach to a priori selection of the time step and adaptive weighting values. The effectiveness of the proposed analysis is further demonstrated by simulations and experiments on a first-order plant. Copyright # 2006 John Wiley & Sons, Ltd.