Multiple Fuzzy Reference Model Adaptive Controller Design for Pitch-Rate Tracking

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.

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

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 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.

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.

Partial-state feedback multivariable MRAC and reduced-order designs

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.

A parameterization for a linear system is presented to design a direct model reference adaptive pole placement controller. This parameterized model is one of the structured nonminimal models. The exponentially weighted least-squares algorithm is employed to estimate the control parameters. The direct adaptive controller has the exponential weighting properties by the proposed method of selecting the characteristic polynomials of the sensitivity function filters in connection with the reference model.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

The precise position control for electro-hydraulic servo system (EHSS) is a key technical problem for modern industry control. This paper presents a model reference adaptive control (MRAC) strategy combining fractional order calculus theory and neural network to solve the high nonlinearity and parameter uncertainties in the EHSS. The reference model in the MRAC strategy is a fractional second-order model and the controller with adaptive law is designed by a Wiener recurrent neural network (WRNN). Simulations indicate the validity and superiority of the proposed fractional MRAC strategy.

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.

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.

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.

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.

Robust Adaptive Control

Model reference adaptive controllers with improved performance for applications in LCL-filtered grid-connected converters