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Relatively simple method of model-reference adaptive control (MRAC) developed from two prior classes of MRAC techniques: signal-synthesis method and parameter-adaption method. Incorporated into unified theory, which yields more general adaptation scheme.
This project presents the results of controlling two types of robots using new Command Generator Tracker (CGT) based Direct Model Reference Adaptive Control (MRAC) algorithms. Two mathematical models were used to represent a single-link, flexible joint arm and a Unimation PUMA 560 arm; and these were then controlled in simulation using different MRAC algorithms. Special attention was given to the performance of the algorithms in the presence of sudden changes in the robot load. Previously used CGT based MRAC algorithms had several problems. The original algorithm that was developed guaranteed asymptotic stability only for almost strictly positive real (ASPR) plants. This condition is very restrictive, since most systems do not satisfy this assumption. Further developments to the algorithm led to an expansion of the number of plants that could be controlled, however, a steady state error was introduced in the response. These problems led to the introduction of some modifications to the algorithms so that they would be able to control a wider class of plants and at the same time would asymptotically track the reference model. This project presents the development of two algorithms that achieve the desired results and simulates the control of the two robots mentioned before. The results of the simulations are satisfactory and show that the problems stated above have been corrected in the new algorithms. In addition, the responses obtained show that the adaptively controlled processes are resistant to sudden changes in the load.
This paper presents a modification of the conventional model reference adaptive control (MRAC) architecture in order to improve transient performance of the input and output signals of uncertain systems. A simple modification of the reference model is proposed by feeding back the tracking error signal. It is shown that the proposed approach guarantees tracking of the given reference command and the reference control signal (one that would be designed if the system were known) not only asymptotically but also in transient. Moreover, it prevents generation of high frequency oscillations, which are unavoidable in conventional MRAC systems for large adaptation rates. The provided design guideline makes it possible to track a reference commands of any magnitude from any initial position without re-tuning. The benefits of the method are demonstrated with a simulation example
Model reference adaptive control system redesign by Liapunov second method for linear systems
Paper discusses stability of digital model-reference adaptive control (MRAC) of robotic system or plant that operates at discrete time steps. Command-generator tracker (CGT) concept, originally proposed for continuous-time systems, is applied in discrete-time setting, enabling relaxation of some restrictive assumptions that guarantee stability of system controlled according to resulting algorithm. Likely applications include systems in which sensors and actuators not placed together.
This paper presents a Hamilton's principle for distributed control of infinite-dimensional systems modeled by a distributed form of the Euler-Lagrange method. The distributed systems are governed by a system of linear partial differential equations in space and time. A generalized potential energy expression is developed that can capture most physical systems including those systems that have no spatial distribution. The Hamilton's principle is applied to derive distributed feedback control methods without resorting to the standard weak-form discretization approach to convert an infinite-dimensional systems to a finite-dimensional systems. It can be shown by the principle of least action that the distributed control synthesized by the Hamilton's principle is a minimum-norm control. A model-reference adaptive control framework is developed for distributed Lagrangian systems in the presence of uncertainty. The theory is demonstrated by an application of adaptive flutter suppression control of a flexible aircraft wing.
Attention is given to model reference adaptive control procedures that do not require explicit parameter identification for large structural systems. Even though such applications have been shown to be feasible for multivariable systems, provided there exists a feedback gain matrix that makes the resulting input/output transfer function strictly positive real, it is shown here that this constraint is overly restrictive and that only positive realness is required. Subsequent consideration of a simply supported beam reveals that if actuators and sensors are collocated, then the positive realness constraint will be satisfied and the model reference adaptive control will then indeed be suitable for velocity following when only velocity sensors are available and for both position and velocity following when velocity plus scaled position outputs are measured. For both cases, all states are guaranteed to be stable, regardless of system dimension.
This paper is devoted to robust, Predictor-based Model Reference Adaptive Control (PMRAC) design. The proposed adaptive system is compared with the now-classical Model Reference Adaptive Control (MRAC) architecture. Simulation examples are presented. Numerical evidence indicates that the proposed PMRAC tracking architecture has better than MRAC transient characteristics. In this paper, we presented a state-predictor based direct adaptive tracking design methodology for multi-input dynamical systems, with partially known dynamics. Efficiency of the design was demonstrated using short period dynamics of an aircraft. Formal proof of the reported PMRAC benefits constitute future research and will be reported elsewhere.
Model reference adaptive control is applied to linear time varying systems and to nonlinear systems amenable to virtual linearization. Asymptotic stability is guaranteed even if the perfect model following conditions do not hold, provided that some sufficient conditions are satisfied. Simulations show the scheme to be capable of effectively controlling certain nonlinear systems.
A Model Reference Adaptive Controller (MRAC) is derived for a Shuttle payload called the Instrument Pointing System (IPS). The unique features of this MRAC design are that total state feedback is not required, that the internal structure of the model is independent of the internal structure of the IPS, and that the model input is of bounded variation and not required a priori. An application of Liapunov's stability theorems is used to synthesize a control signal which assures MRAC asymptotic stability. Exponential observers are used to obtain the necessary state information to implement the control synthesis. Results are presented which show how effectively the MRAC can maneuver the IPS.
Parameter adjustment model reference adaptive control of nuclear rocket engine, noting design parameters, system performance, propellant savings estimates and dynamic stability
Quadratic performance index for VTOL aircraft model reference attitude control system
Description of a simplified Model Reference Adaptive Control (MRAC) Experiment that will be flown in December of 2010 on the F-18 Full-scale Advanced Systems Testbed (FAST) vehicle.
Some problems associated with the design of model-reference adaptive control systems are considered and solutions to these problems are advanced. The stability of the adapted system is a primary consideration in the development of both the time-domain and the frequency-domain design techniques. Consequentially, the use of Liapunov's direct method forms an integral part of the derivation of the design procedures. The application of sensitivity coefficients to the design of model-reference adaptive control systems is considered. An application of the design techniques is also presented.
This paper considers the model-reference adaptive control problem which has received considerable attention in the literature in the last few years. An adaptive control scheme is proposed which has terms in the Liapunov function used in the design procedure which are not included in previously proposed schemes. The relationship of this new scheme to existing schemes is shown by considering the root-loci of the linearized error equations between plant and model. Finally, a second order example is given which illustrates the difference between the two previously proposed model-reference adaptive methods and the one proposed in this paper.
This project dealt with the application of a Direct Model Reference Adaptive Control algorithm to the control of a PUMA 560 Robotic Manipulator. This chapter will present some motivation for using Direct Model Reference Adaptive Control, followed by a brief historical review, the project goals, and a summary of the subsequent chapters.
State space structure of model reference adaptive control and parameter tracking systems subject to noise
This paper is devoted to the design and analysis of a predictor-based model reference adaptive control. Stable adaptive laws are derived using Lyapunov framework. The proposed architecture is compared with the now classical model reference adaptive control. A simulation example is presented in which numerical evidence indicates that the proposed controller yields improved transient characteristics.