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.