Natural tracking with a prediction-error based parameter estimator

Current trends in control applications are oriented toward control algorithms which, to the least extent, depend on the system´s mathematical model and/or its parameters. The recently disclosed concept of high gain natural tracking is one such approach to control of nonlinear dynamic systems. This law is independent of the plant´s internal dynamics, but dependent on the system parameters. The work presented in this article demonstrates how the natural tracker can be applied to a nonlinear system, such as a robot, with unknown parameters. The parameters can be identified online using the prediction-error based estimator and the linearly parametrized mathematical model. The persistent excitation condition is satisfied with external high-frequency and low-amplitude dither signals which are superimposed on the control inputs. The identification is based on the knowledge of the system´s total energy and under the assumption that the acceleration measurements are not available. This methodology, however, requires the use of additional filtering of the plant´s input signals