Direct Adaptive Control

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.

In this paper we focus on the adaptive control of structural acoustics using intelligent structures with embedded piezoelectric (PZT) patches and low cost digital signal processor systems. After a discussion on the adaptive feedforward control scheme, a hybrid adaptive control scheme is proposed, which takes advantage of both feedback control and adaptive feedforward control. The two schemes are realized on a low-cost, small volume, convenient and universal digital signal processing (DSP) board. A carbon fiber reinforced polymer plate with two embedded PZT patches is developed and used in two experiments. The first experiment is adaptive interior noise control using the intelligent plate, in which the adaptive feedforward control scheme is employed. Obvious noise reduction is obtained for constant frequency, swept frequency and varying amplitude harmonic disturbances. The second experiment is adaptive control of sound-induced vibration of the plate, where two embedded PZT patches are used as an actuator and a sensor, respectively, and the hybrid adaptive controller is applied. The full vibration reduction for various harmonic excitations is obtained, verifying the advantage of the hybrid adaptive control. It is demonstrated that active control of structural acoustics can be efficiently achieved by employing intelligent structures, advanced adaptive control schemes and the low-cost DSP board.

A direct adaptive neural control scheme with single and double integral-plus-state (IPS) actions is proposed. The control scheme contains two recurrent trainable neural network (RTNN) models, which are a plant parameter identifier and state estimator, an IPS feedback/feedforward controller, and one or two I-terms. The good performance of the adaptive IPS control scheme is confirmed by closed-loop systems analysis and by simulation results obtained with a MIMO plant, corrupted by noise. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 213–224, 2005.

An adaptive integral plus states neural control of aerobic continuous stirred tank reactor

This paper proposes a new scheme for direct neural adaptive control that works efficiently employing only one neural network, used for simultaneously identifying and controlling the plant. The idea behind this structure of adaptive control is to compensate the control input obtained by a conventional feedback controller. The neural network training process is carried out by using two different techniques: backpropagation and extended Kalman filter algorithm. Additionally, the convergence of the identification error is investigated by Lyapunov's second method. The performance of the proposed scheme is evaluated via simulations and a real time application.

The flying mechanism of birds and big insects, especially the rules of wings motion in flight, are investigated, and some details of mechanical frame are also considered. The entire dynamic model of flight attitude of flapping wing micro aerial vehicle (FWMAV) is developed. The design of attitude controller is challenging due to the complexity of the flight process, and the heavy difficulty includes the system uncertainty, nonlinearity, multi-variable coupled parameters, and all kinds of disturbances. To control the attitude movement effectively, an adaptive robust control scheme is proposed to decompose the system into nominal model, structured uncertainties, and unstructured uncertainty. For them, direct feedback controller, adaptive controller, and robust controller are constructed respectively. Simulation results are presented to verify the validity of the dynamic model and the control strategy.

A controller to rotate a rigid body between two successive orientations is designed. Particular features are the fact that it is based on the quaternion approach, known to provide singularity-free attitude description, and it is adaptive in the sense that it does not need specific knowledge of the inertia matrix. Global stability of the overall controller is proved analytically and tested in computer simulations. © 1994 American Institute of Aeronautics and Astronautics, Inc., All rights reserved.

This paper investigates the application of direct adaptive control to suppress the aeroelastic response of a flexible wing model for a range of flight conditions, including pre- and post-flutter airspeeds, while also subject to unmodeled nonlinearities such as nonlinear structural stiffness and actuator free-play. A three-dimensional aeroelastic wing is developed in which the structural dynamics are modeled using a three-dimensional extension of a typical aeroelastic section by mode shape weighting. The aerodynamic forcing is incorporated via an unsteady vortex lattice method. Nonlinear structural stiffness and actuator free-play models are developed and incorporated into the aeroelastic model as well as exogenous disturbances, such as continuous turbulence and discrete gust models. A direct adaptive control strategy is employed that takes the form of an adaptive regulator in which the feedback gain represents the adaptive augmentation of a baseline linear quadratic regulator (LQR) that is designed using the linear aeroelastic model at a pre-flutter flight condition. Simulation studies are presented to evaluate the performance of the adaptive controller in comparison to the baseline LQR controller for a range of flight conditions and with the inclusion of a torsional structural nonlinearity and actuator free-play. The results show that the adaptive controller provides improved attenuation of the linear aeroelastic model as the airspeed increases past the flutter point. In addition, for the case where torsional nonlinearity is included, the adaptive controller is able to stabilize the aeroelastic response at the flutter speed whereas the closed-loop response using the baseline LQR controller is unstable.

In direct adaptive inverse control (DAIC), parameters of the controller are estimated directly in the feed-forward loop. In this paper, we propose a closed loop direct adaptive inverse control (CDAIC) scheme which improves tracking, error convergence, and disturbance rejection properties of DAIC. CDAIC is applicable to stable or stabilized, minimum or nonminimum phase linear plants. CDAIC and DAIC are compared using computer simulations for disturbance free and disturbed discrete type nonminimum phase linear plants. CDAIC shows better results compared to DAIC in terms of mean square tracking error and disturbance rejection.

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 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".

Intelligent switching adaptive control for uncertain nonlinear dynamical systems

In this paper, we present a neural adaptive control scheme for active vibration suppression of a composite aircraft fin tip. The mathematical model of a composite aircraft fin tip is derived using the finite element approach. The finite element model is updated experimentally to reflect the natural frequencies and mode shapes very accurately. Piezo-electric actuators and sensors are placed at optimal locations such that the vibration suppression is a maximum. Model-reference direct adaptive neural network control scheme is proposed to force the vibration level within the minimum acceptable limit. In this scheme, Gaussian neural network with linear filters is used to approximate the inverse dynamics of the system and the parameters of the neural controller are estimated using Lyapunov based update law. In order to reduce the computational burden, which is critical for real-time applications, the number of hidden neurons is also estimated in the proposed scheme. The global asymptotic stability of the overall system is ensured using the principles of Lyapunov approach. Simulation studies are carried-out using sinusoidal force functions of varying frequency. Experimental results show that the proposed neural adaptive control scheme is capable of providing significant vibration suppression in the multiple bending modes of interest. The performance of the proposed scheme is better than the H(infinity) control scheme.

In this brief, a novel direct adaptive fuzzy tracking control (DAFTC) scheme for marine vehicles with fully unknown parametric dynamics and uncertainties is proposed. The significant contributions of the DAFTC approach are as follows. First, in the backstepping framework, fully unknown parametric dynamics and uncertainties are encapsulated into a lumped nonlinearity function encompassing system states and virtual control signals. Second, the integrated nonlinearity function is further identified online by an adaptive fuzzy approximator that synthesizes a model-free control scheme (termed DAFTC) without requiring any a priori knowledge of the model. Third, tracking errors are proven to be uniformly ultimately bounded (UUB) and can converge to an arbitrarily small neighborhood of zero in a finite time. Simulation studies and comprehensive comparisons demonstrate that the proposed DAFTC scheme has remarkable performance and is superior in both tracking accuracy and unknown parametric dynamics compensation.

A direct model-reference adaptive control scheme, which requires only output, rather than full-state, measurement is presented for a class of single-input, single-output (SISO) nonlinear systems with unknown constant parameters. The nonlinearities are not required to satisfy any growth conditions. The assumptions on the linear part of the nonlinear system are the same as in the standard adaptive control problem for linear systems, which appears as a special case of the nonlinear problem that is solved. >