Closed Loop Direct Adaptive Inverse Control for Linear Plants

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.

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.

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.

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.

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.

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

This paper aims to perform the performance analysis of the Modified Variable Step Size Fractional Least Mean Square (MVSS-FLMS) algorithm in the design of Direct Adaptive Inverse Control (DAIC) nonlinear based on Volterra model. The motivation for formulating the DAIC based on Volterra model, is so that the controller can track the inverse dynamics of plants with nonlinearity of polynomial type. Since the MVSS-FLMS algorithm proposes the use of variable step size, then its application in nonlinear DAIC design allows to obtain nonconservative results for a performance analysis, with respect to convergence speed and steady-state Mean Square Error (MSE). As an increment of the complexity scenario, the present work was evaluated in the presence of a sinusoidal disturbance signal added to the control signal.

This paper presents a direct robust adaptive tracking control strategy for the Iongitudinal motion of electric vehicles (EVs) subject to parametric uncertainties, nonlinear dynamics, and external disturbances. The vehicle longitudinal dynamics are formulated as a second-order nonlinear system with unknown nonlinearities. Unlike conventional indirect adaptive approaches that first identify unknown system dynamics, a radial basis function neural network (RBFNN) is employed to directly approximate the ideal feedback control law derived from sliding mode theory and Lyapunov synthesis. A robust adaptive law incorporating -modification is designed for online neural network weight update, enhancing robustness against approximation errors and bounded disturbances without requiring prior knowledge of their bounds. Lyapunov-based stability analysis rigorously demonstrates that all closed-loop signals are uniformly ultimately bounded (UUB), with tracking error converging to a tunable residual set around zero. The controller achieves model-independent operation, requiring no exact knowledge of vehicle nonlinear dynamics. Comprehensive simulations under step commands, and multi-frequency trajectories, together with parametric variations and road grade disturbances, validate the effectiveness of the proposed scheme in achieving accurate velocity tracking and superior robustness compared to conventional PID and sliding mode control. The main source code of this paper, including all simulation scripts and neural network modules, can support information found in S1 Pdf file.

In this paper, a neural network approach for the identification and control of a separately excited direct (DC) motor (SEDCM) driving a centrifugal pump load is applied. In this application, two radial basis function neural networks (RBFNN) are used: The first is a RBFNN identifier trained offline to emulate the dynamic performance of the DC motor-load system. The second is a RBFNN controller, which is trained to make the motor speed follow a selected reference signal. Two RBFNN control schemes are proposed using direct inverse and internal model control schemes. The performance of the RBFNN identifier and controller is investigated in terms of step response, sharp changes in speed trajectory, and sudden load change, as well as changes in motor parameters. The performance of RBFNN in system identification and control has been compared with the performance of the well-known back-propagation neural network (BPNN). The simulation results show that both of the BPNN and RBFNN controllers exhibit excellent dynamic response, adapt well to changes in speed trajectory and load connected to the motor, and adapt to the variations of motor parameters. Furthermore, the simulation results show that the step response of RBFNN internal model and direct inverse controllers are identical.

Adaptive fuzzy asymptotic tracking control of uncertain nonaffine nonlinear systems with non-symmetric dead-zone nonlinearities

Direct adaptive control covers schemes where the parameters of the controller are directly updated from a signal error (adaption error) reflecting the performance error. The chapter presents strategies for direct adaptive control in a deterministic and in a stochastic environment and the corresponding analysis. This includes adaptive tracking and regulation with independent objectives, adaptive minimum variance control and their extensions. Robustification of direct adaptive control schemes is also discussed.KeywordsController ParameterUnmodeled DynamicAdaptive Control SchemeModel Reference Adaptive ControlAdaptive TrackingThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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

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>

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. >

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.