Design Of Adaptive Control Law Research Articles

Erratum for “Modeling and Adaptive Control Law Design for a Bi-Tiltrotor Unmanned Aerial Vehicle” by Chao Ma, Donglei Sun, and Naira Hovakimyan

Erratum for “Modeling and Adaptive Control Law Design for a Bi-Tiltrotor Unmanned Aerial Vehicle” by Chao Ma, Donglei Sun, and Naira Hovakimyan

Modeling and Adaptive Control Law Design for a Bi-Tiltrotor Unmanned Aerial Vehicle

This paper presents modeling and control law design for a bi-tiltrotor unmanned aerial vehicle for autonomous payload delivery. An ultralight bi-tiltrotor aerial vehicle was developed for a payload delivery contest using only two motors attached to a carbon fiber tube with an additional cube basket to carry the payload. To achieve desirable three-dimensional (3D) trajectory tracking performance with unknown payload, an adaptive control system has been developed, which consists of two components: (1) a baseline control law, including a lateral speed controller, forward speed controller, yaw angle controller, and altitude controller; and (2) an adaptive augmentation to all these four channels to compensate for modeling uncertainties and disturbances caused by the payload, which has large influence on system dynamics due to the light weight of the vehicle. A mathematical model of the vehicle has been built using the Newton-Euler method and simplified based on reasonable assumptions to facilitate control design. The baseline control law employs conventional proportional-derivative control structure. Subsequently, L1 adaptive control theory is adopted in the adaptive augmentation design. Simulation examples verify the efficacy of the proposed adaptive control solution.

Adaptive Inventory Control Based on Fuzzy Neural Network under Uncertain Environment

In order to achieve the actual inventory effectively tracking the target inventory under uncertain environment, this paper investigates an adaptive inventory controller for the production-inventory system. First, an uncertain production-inventory model is constructed, and then, the uncertainty of the production-inventory model is approximated by a fuzzy neural network. Secondly, in terms of the design of adaptive control law, the adaptive inventory controller is developed. Under the adaptive inventory controller, the actual inventory can track the target inventory in real time and the production-inventory system can be robustly stable in uncertain environment. Finally, the results of three simulation experiments show that the proposed adaptive inventory controller can realize both the fast tracking speed and the high tracking accuracy.

Computing stability limits for adaptive control laws with high-order actuator dynamics

A challenge in the design of adaptive control laws for uncertain dynamical systems is to achieve system stability and a prescribed level of command following performance in the presence of actuator dynamics. It is well-known that if the actuator dynamics do not have sufficiently high bandwidth, their presence cannot be practically neglected in the design since they limit the achievable stability of adaptive control laws. In this paper, we consider the design of model reference adaptive control laws for uncertain dynamical systems in the presence of high-order actuator dynamics. Specifically, a linear matrix inequalities-based hedging approach is proposed, where this approach modifies the ideal reference model dynamics to allow for correct adaptation that is not affected by the presence of actuator dynamics. The stability of the modified reference model is then computed using linear matrix inequalities, which reveals the fundamental stability interplay between the parameters of the actuator dynamics and the allowable system uncertainties. In addition, we analyze the convergence properties of the modified reference model to the ideal reference model. The presented theoretical results are finally illustrated through a numerical example.

Adaptive Control of Novel Configuration with Moving Mass

Abstract A new adaptive control law design for moving mass control of a novel flight vehicle configuration is proposed. The coupling dynamic model between angle of attack and deflection angle is generated. An integrated control of attitude-servo system based on immersion and invariance (I&I) theory is presented to deal with the coupling caused by the additional inertia moment. To overcome the uncertainties in the model, the I&I theory is employed to design estimator to estimate the unknown aerodynamic parameters. The simulation results show that the proposed configuration is feasible and validate the quality of the adaptive controller.

Parameter‐dependent Riccati equation–based output feedback adaptive control

SummaryA parameter‐dependent Riccati equation approach is proposed to design and analyze the stability properties of an output feedback adaptive control law design. The adaptive controller is intended to augment an existing fixed‐gain observer‐based output feedback control law. Although the formulation is in the setting of model reference adaptive control, the realization of the adaptive controller does not require implementing the reference model. In this regard, the increased complexity of implementing the adaptive controller, above that of a fixed‐gain control law, is less than that of other methods. The error signals are shown to be uniformly ultimately bounded, and an estimate for the ultimate bound is provided. The issue of sensor noise is addressed by introducing an error filter. The control design process and the theoretical results are illustrated using a model for wing rock dynamics.

Adaptive control of a nonlinear aeroelastic system

Aeroelastic two-dimensional wing section with both trailing-edge (TE) and leading-edge (LE) was investigated in this paper through numerical simulation in time domain. Structural stiffness and damping in pitch degree of freedom were represented by nonlinear polynomials. Open-loop limit cycle oscillation (LCO) characters of two examples were studied, and flutter boundaries with initial conditions were obtained. Parametric uncertainties in both pitch stiffness and damping were considered in the design of adaptive control laws to depress LCOs. Firstly an adaptive controller based on partial feedback linearization was derived for the wing section with a single TE control surface. Secondly a structured model reference adaptive control law was designed for the aeroelastic system with both TE and LE control surfaces. The results show that the designed control laws are effective for flutter suppression, and that considering damping uncertainty has positive effect on flutter control. It may reduce convergent time or increase flutter speed.

A new adaptive backstepping method for nonlinear control of turbine main steam valve

A new approach for nonlinear adaptive control of turbine main steam valve is developed. In comparison with the existing controller based on “classical” adaptive backstepping, this method does not follow the classical certainty-equivalence principle in the design of adaptive control law. We introduce this approach, for the first time, to power systems and present a novel parameter estimator and dynamic feedback controller for a single machine infinite bus (SMIB) system with steam valve control. This system contains unknown parameters such as reactance of transmission lines. Besides preserving useful nonlinearities and the real-time estimation of uncertain parameters, the proposed approach possesses better performances with respect to the response of the system and the speed of adaptation. The simulation results demonstrate that the proposed approach is better than the design based on “classical” adaptive backstepping in terms of properties of stability and parameter estimation, and recovers the performance of the “full-information” controller. Hence, the proposed method provides an alternative for engineers in applications.

ADAPTIVE SLIDING MODE BACKSTEPPING CONTROL OF NONLINEAR SYSTEMS WITH UNMATCHED UNCERTAINTY

ABSTRACTThis paper considers an adaptive backstepping algorithm for designing the control for a class of nonlinear continuous uncertain processes with disturbances that can be converted to a parametric semi‐strict feedback form. Sliding mode control using a combined adaptive backstepping sliding mode control (SMC) algorithm, is also studied. The algorithm follows a systematic procedure for the design of adaptive control laws for the output tracking of nonlinear systems with matched and unmatched uncertainty.

Identification Based Adaptive Control of Chaotic Continuous-Time Systems

The present paper further developes ideas of controlling chaos by the speed gradient method. It is shown how to design parameter identifier for chaotic systems ensuring achievement of the objective of parameter identification. The data obtained by the identifier can be used for adaptive control law design. Two examples are considered: identification of parameters of periodically perturbed Duffing equation and stabilizing the inherent unstable equilibrium point of the thermal convection loop model when parameters of the model are unknown.

Computational aircraft dynamics and loads

An alternative approach that has the potential to advance classical methods of flight load prediction by combining computational fluid dynamics (CFD), structural flexibility and the interaction of flight control system (FCS) in a multidisciplinary analysis package is described. The method employs the concept of system identification to characterize aircraft dynamics in the state space coordinate system and includes an adaptive control law design methodology. An extended account of the theoretical basis for the new multidisciplinary flight manoeuvre analysis will be presented in one of a seven-volume series on computational mechanics by Argyris and his associates to be published shortly. However, as a precursor to the complete work, a brief account of the theoretical development leading to this loads prediction methodology is included in this paper.

Model‐based control of industrial manipulators: An experimental analysis

AbstractModel‐based control techniques for industrial robotic manipulators were experimentally analyzed. The case study for the analysis was the first three joints of a PUMA‐560. Development of an advanced control evaluation environment removed the computational restrictions of previous studies. The specific issues of: incomplete and/or asynchronous dynamic compensation, feedback paradigm selection, payload sensitivity, and drive systems effects were experimentally analyzed. Evaluation results clearly demonstrate that the performance improvement capability of model‐based control is not restricted to research robots. A model‐based controller with complete feedforward compensation and sliding mode feedback reduced the maximum tracking error by at least a factor of five when compared to feedback alone. End‐effector payload invariant tracking only requires mass information. Tracking performance is not dependent on dynamic compensation at the servo rate. Evaluation results provide insight for adaptive control law design and a baseline to compare their performance against.