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It is widely recognized that the existing parameter estimators and adaptivecontrollers for robot manipulators are extremely complicated to be of practicaluse. This is mainly due to the fact that the existing parameterization includesthe complicated signal and parameter relations introduced by the Coriolis andcentrifugal forces matrix. In an insightful remark of their seminal paperSlotine and Li suggested to use the parameterization of the power balanceequation, which avoids these terms -- yielding significantly simpler designs.To the best of our knowledge, such an approach was never actually pursued inon-line implementations, because the excitation requirements for the consistentestimation of the parameters is ``very high". In this paper we use a recenttechnique of generation of ``exciting" regressors developed by the authors toovercome this fundamental problem. The result is applied to generalEuler-Lagrange systems and the fundamental advantages of the newparameterization are illustrated with comprehensive simulations of a 2degrees-of-freedom robot manipulator.

Parameter Estimation and Adaptive Control of Euler-Lagrange Systems Using the Power Balance Equation Parameterization