Explicit Reference Model Research Articles

Signal Transmission by Sampled-time Adaptive Synchronization of Time-varying Chaotic Systems

In the paper, the adaptive synchronization problem is studied for a class of time-varying nonlinear systems. The only assumption imposed is the boundedness of the rate of change of the controlled system parameters, while the initial uncertainty can be quite large. The adaptation algorithms in the discrete-time form are derived based on adaptive control of nonlinear time-varying systems with an explicit reference model. As the result, the estimate of the limiting deviation of the solution of the closed-loop system from that of the reference model was obtained. It is shown that with sufficiently slow changes in the parameters and a small initial uncertainty, the limiting error in the system can be made arbitrarily small despite a possibly big range of system parameter values. The systems based on a direct approach are considered. The procedure for the synthesis of a sampled-time adaptive controller and analysis of the synthesized system is illustrated by an example of the signal transmission system. The results obtained allow one to build and analyze a wide class of adaptive systems with reference models under time-varying conditions.

Adaptive control of time-varying non-linear plants by speed-gradient algorithms

Introduction: The motion of many real world systems is described by essentially non-linear and non-stationary models. A number of approaches to the control of such plants are based on constructing an internal model of non-stationarity. However, the non-stationarity model parameters can vary widely, leading to more errors. It is only assumed in this paper that the change rate of the object parameters is limited, while the initial uncertainty can be quite large.Purpose: Analysis of adaptive control algorithms for non-linear and time-varying systems with an explicit reference model, synthesized by the speed gradient method.Results: An estimate was obtained for the maximum deviation of a closed-loop system solution from the reference model solution. It is shown that with sufficiently slow changes in the parameters and a small initial uncertainty, the limit error in the system can be made arbitrarily small. Systems designed by the direct approach and systems based on the identification approach are both considered. The procedures for the synthesis of an adaptive regulator and analysis of the synthesized system are illustrated by an example.Practical relevance: The obtained results allow us to build and analyze a broad class of adaptive systems with reference models under non-stationary conditions.

A saturation-balancing control method for enhancing dynamic vehicle stability

This paper proposes a vehicle stability control method that quantifies and uses the level of lateral force saturation on each axle of a vehicle. The magnitude of the saturation is determined from on-line estimated nonlinear lateral tire forces and their linear projections. Once known, saturation levels are employed in a saturation balancing control structure that biases the drive torque to either the front or rear axles/wheels. The control structure avoids the need for an explicit reference model to generate target responses. The benefits of the proposed approach are demonstrated considering a nominally unstable vehicle in an extreme obstacle avoidance manoeuvre.

Pixel-processing fundamentals

The key to developing line drawing algorithms is an explicit reference model for quantifying a good raster approximation. The article looks at old practices, then demonstrates the development of line and edge drawing algorithms. Computer driven raster displays introduce a higher performance, more demanding technology that benefits from systematic rules. To examine the development of a compact, integer arithmetic algorithm for line rastering, the article looks at some pixel space basics, then considers alternative measures by which closeness to a true line's locus could be evaluated. The key to effective use of all algorithms is a clear understanding of your objective function and the implications that can arise from incomplete or unintentionally fuzzy assumptions.

Strategies of Model Reference Adaptive Control with Active Learning Properties

The problems of synthesis and analysis of model reference adaptive control for systems with active learning are considered New active adaptive control algorithms for systems with implicit and explicit reference model are designed. The interconnections between direct and indirect active adaptive control systems are discussed and it is shown that the suggested method is uniformal for both types of systems. The convergence properties of the designed algorithms are analysed. Using the martingale convergence theory the solutions for global stability of the algorithms are presented. A simulated example is used to demonstrate the potential and superiority of the algorithms.

Redesign of explicit and implicit discrete time model reference adaptive control schemes

The design of MRAC for achieving independent tracking and regulation objectives with a unique equilibrium point in the controller parameter space is presented. The corresponding schemes can use either an explicit reference model or an implicit reference model. The design is done from a stability point of view by extending an appropriate control configuration used for the case of known plant parameters. The result of the design is a simple control scheme using a linear constant feedforward controller and a non-linear feedback controller. The stability of the system and boundness of various variables do not require any positivity condition. The performances of the control structure in tracking and regulation are evaluated by simulations.