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Mohammadreza Homayounzadeh (PhD)


Grade: Ph.D

Year: 2010 - 2015

Thesis Title: Position and Force Control of Robotic Systems based on Joint Velocity, Parametric Uncertaities and Current of Motors Estimation

Thesis Abstract: This dissertation deals with the problem of position/force controller design for robotic systems based on estimation of joint velocities; motor currents; and parametric uncertainties. To eliminate the need for link velocity measurements two approaches are proposed: a) Filtering techniques; b) Observer techniques. Furthermore, a novel observer is designed to estimate the armature currents.

To deal with the parametric uncertainties, a novel adaptive strategy is proposed to estimate the robot uncertain parameters. Existing adaptive methods are generally based on the certainty equivalence (CE) principle. The procedure for designing the CE-based adaptive controller consists of two steps: First, designing the deterministic controller assuming there is no uncertainty in system parameters—it is called the certainty equivalence principle; second, substituting the actual parameters for their estimated values in the control law.

In fact, the fundamental concept of the CE-based adaptive control systems is to exactly cancel the uncertain parameters. However, the exact cancellation of uncertain parameters never occurs in real applications. Thus, the overall performance of the regulating/tracking control depends on the performance of the parameter estimator. The performance of the parameter estimator is poor, since the parameter estimate generally exhibit wide variations compared to their true values.

In this dissertation, we propose a novel output feedback adaptive method where the unknown parameters converge to the attractive manifold. This enables the controlled system to asymptotically recover the transient performance of deterministic control and the performance of the system in parameter estimation. The proposed adaptive method is not based on the certainty equivalent principle. In the proposed NCEA (non-certainty equivalent adaptive) method, the detrimental effect of the uncertain parameters is compensated pursuing a robustness perspective. The proposed control method requires the sole measurements of joint positions.

To deal with the constrained applications of robots, the proposed adaptive strategy is extended to position and force control of robotic systems. As well, the proposed adaptive strategy is extended to control the electrically driven robotic systems without joint velocity and armature current measurements.

The proposed control method guarantees semiglobal asymptotic motion tracking and velocity estimation, as well as   and   ounded parameter estimation error. Furthermore; it is shown that the system is finite gain   table exposed to external disturbance.

The effectiveness of the proposed controller is verified mathematically and numerically. 


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