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At least 19 records

Non-Causal Controller Approximation Methodology Augmented with Model Reference Control – Robust Design

This paper describes the development of a new control design methodology based on a non-causal controller structure and its approximation. The non-causal controller design is based on a loop shaping approach. Partial fraction decomposition is used, along with derivative approximations, to derive a proper transfer function design for a causal control structure. The premise for this non-causal control methodology is that the phase delay as a function of frequency ideally remains within approximately -90o, which tends to make the system robustly stable to uncertainties in the plant dynamics. To have a predictable control system response in lieu of the tolerance of this methodology to relatively large uncertainty in the plant dynamics, the approach is augmented with model reference control. The addition of model reference control can potentially further enhance the robustness of the design. A control system design example is presented in this paper, along with simulation results, to demonstrate the high degree of robustness of this non-causal methodology and its augmentation.

Control Systems

Model reference control of the NASA SCOLE problem

Model reference controllers have been developed and applied to the linearized Spacecraft Control Laboratory Experiment (SCOLE) roll beam mode equation. When a large- but finite-dimensional approximation to the partial differential equation is used, the resulting control will be finite-dimensional, but possibly not adequately representative for the actual distributed-parameter system. To date the theory for such model reference controllers has been developed; here it is applied to a 16th-order finite-dimensional representation of the flexible and rigid body modes of the SCOLE. Results of the analysis and some preliminary simulation studies are presented.

Kaufman, Howard

A standard satellite control reference model

This paper describes a Satellite Control Reference Model that provides the basis for an approach to identify where standards would be beneficial in supporting space operations functions. The background and context for the development of the model and the approach are described. A process for using this reference model to trace top level interoperability directives to specific sets of engineering interface standards that must be implemented to meet these directives is discussed. Issues in developing a 'universal' reference model are also identified.

Golden, Constance

Robust Control Methodology Via Loop Shaping, Root Locus, and Model Reference Control

The paper describes the development of a robust control methodology that can tolerate large uncertainties in plant dynamics in terms of proportional gain variations as well as frequencies and damping. For this to be accomplished, the methodology starts by designing a feedback controller based on a loop shaping technique. The design is then improved by employing a root locus control design approach. Finally, the response of the feedback system is further improved by augmenting the control design using model reference control. The design approach also takes into consideration the speed of the process actuator for a fast response and for high disturbance rejection. Results from two control design examples covered in the paper show that this control approach can tolerate plant proportional gain uncertainty of approximately ± two orders of magnitude with relatively large variations in plant frequencies and damping.

Control Systems

An implicit adaptation algorithm for a linear model reference control system

This paper presents a stable implicit adaptation algorithm for model reference control. The constraints for stability are found using Lyapunov's second method and do not depend on perfect model following between the system and the reference model. Methods are proposed for satisfying these constraints without estimating the parameters on which the constraints depend.

Mabius, L.

Analytical and experimental study of control effort associated with model reference adaptive control

Numerical simulation results presently obtained for the performance of model reference adaptive control (MRAC) are experimentally verified, with a view to accounting for differences between the plant and the reference model after the control function has been brought to bear. MRAC is both experimentally and analytically applied to a single-degree-of-freedom system, as well as analytically to a MIMO system having controlled differences between the reference model and the plant. The control effort is noted to be sensitive to differences between the plant and the reference model.

Messer, R. S.

Control effort associated with model reference adaptive control for vibration damping

The performance of Model Reference Adaptive Control (MRAC) is studied in numerical simulations with the objective of understanding the effects of differences between the plant and the reference model. MRAC is applied to two structural systems with adjustable error between the reference model and the actual plant. Performance indices relating to control effort and response characteristics are monitored in order to determine what effects small errors have on the control effort and performance of the two systems. It is shown that reasonable amounts of error in the reference model can cause dramatic increases in both the control effort and response magnitude (as measured by energy integrals) of the plant.

Messer, Richard Scott

Adaptive Performance Seeking Control Using Fuzzy Model Reference Learning Control and Positive Gradient Control

Performance Seeking Control attempts to find the operating condition that will generate optimal performance and control the plant at that operating condition. In this paper a nonlinear multivariable Adaptive Performance Seeking Control (APSC) methodology will be developed and it will be demonstrated on a nonlinear system. The APSC is comprised of the Positive Gradient Control (PGC) and the Fuzzy Model Reference Learning Control (FMRLC). The PGC computes the positive gradients of the desired performance function with respect to the control inputs in order to drive the plant set points to the operating point that will produce optimal performance. The PGC approach will be derived in this paper. The feedback control of the plant is performed by the FMRLC. For the FMRLC, the conventional fuzzy model reference learning control methodology is utilized, with guidelines generated here for the effective tuning of the FMRLC controller.

Kopasakis, George

Nonlinear Performance Seeking Control using Fuzzy Model Reference Learning Control and the Method of Steepest Descent

Performance Seeking Control (PSC) attempts to find and control the process at the operating condition that will generate maximum performance. In this paper a nonlinear multivariable PSC methodology will be developed, utilizing the Fuzzy Model Reference Learning Control (FMRLC) and the method of Steepest Descent or Gradient (SDG). This PSC control methodology employs the SDG method to find the operating condition that will generate maximum performance. This operating condition is in turn passed to the FMRLC controller as a set point for the control of the process. The conventional SDG algorithm is modified in this paper in order for convergence to occur monotonically. For the FMRLC control, the conventional fuzzy model reference learning control methodology is utilized, with guidelines generated here for effective tuning of the FMRLC controller.

Kopasakis, George

Input error versus output error model reference adaptive control

Algorithms for model reference adaptive control were developed in recent years, and their stability and convergence properties have been investigated. Typical algorithms in continuous time involve strictly positive real conditions on the reference model, while similar discrete time algorithms do not require such conditions. It is shown how algorithms differ by the use of an input error versus an output error, and present a continuous time input error adaptive control algorithm which does not involve SPR conditions. The connections with other schemes are discussed. The input error scheme has general stability and ocnvergence properties that are similar to the output error scheme. However, analysis using averaging methods reveals some preferable convergence properties of the input error scheme. Several other advantages are also discussed.

Bodson, Marc

An Optimal Control Modification to Model-Reference Adaptive Control for Fast Adaptation

This paper presents a method that can achieve fast adaptation for a class of model-reference adaptive control. It is well-known that standard model-reference adaptive control exhibits high-gain control behaviors when a large adaptive gain is used to achieve fast adaptation in order to reduce tracking error rapidly. High gain control creates high-frequency oscillations that can excite unmodeled dynamics and can lead to instability. The fast adaptation approach is based on the minimization of the squares of the tracking error, which is formulated as an optimal control problem. The necessary condition of optimality is used to derive an adaptive law using the gradient method. This adaptive law is shown to result in uniform boundedness of the tracking error by means of the Lyapunov s direct method. Furthermore, this adaptive law allows a large adaptive gain to be used without causing undesired high-gain control effects. The method is shown to be more robust than standard model-reference adaptive control. Simulations demonstrate the effectiveness of the proposed method.

Nguyen, Nhan T.

The cost of model reference adaptive control - Analysis, experiments, and optimization

In this paper the performance of Model Reference Adaptive Control (MRAC) is studied in numerical simulations and verified experimentally with the objective of understanding how differences between the plant and the reference model affect the control effort. MRAC is applied analytically and experimentally to a single degree of freedom system and analytically to a MIMO system with controlled differences between the model and the plant. It is shown that the control effort is sensitive to differences between the plant and the reference model. The effects of increased damping in the reference model are considered, and it is shown that requiring the controller to provide increased damping actually decreases the required control effort when differences between the plant and reference model exist. This result is useful because one of the first attempts to counteract the increased control effort due to differences between the plant and reference model might be to require less damping, however, this would actually increase the control effort. Optimization of weighting matrices is shown to help reduce the increase in required control effort. However, it was found that eventually the optimization resulted in a design that required an extremely high sampling rate for successful realization.

Messer, R. S.

Model reference adaptive control for systems with time varying model commands

Model reference adaptive control is applied to linear time invariant systems for the case of arbitrary time varying model commands. Asymptotic stability is guaranteed, provided that the output stabilized transfer matrix is strictly positive real. Only output measurements are needed. Neither perfect model following nor explicit parameter identification is required. Simulations show the scheme to be capable of guaranteeing stability when the model inputs are time varying.

Abida, L.

A Nonlinear Dynamic Inversion Predictor-Based Model Reference Adaptive Controller for a Generic Transport Model

Presented here is a Predictor-Based Model Reference Adaptive Control (PMRAC) architecture for a generic transport aircraft. At its core, this architecture features a three-axis, non-linear, dynamic-inversion controller. Command inputs for this baseline controller are provided by pilot roll-rate, pitch-rate, and sideslip commands. This paper will first thoroughly present the baseline controller followed by a description of the PMRAC adaptive augmentation to this control system. Results are presented via a full-scale, nonlinear simulation of NASA s Generic Transport Model (GTM).

Campbell, Stefan F.

Decentralized model reference adaptive control of large flexible structures

A decentralized model reference adaptive control (DMRAC) method is developed for large flexible structures (LFS). The development follows that of a centralized model reference adaptive control for LFS that have been shown to be feasible. The proposed method is illustrated using a simply supported beam with collocated actuators and sensors. Results show that the DMRAC can achieve either output regulation or output tracking with adequate convergence, provided the reference model inputs and their time derivatives are integrable, bounded, and approach zero as t approaches infinity.

Lee, Fu-Ming