Showing papers by "M Maarten Steinbuch published in 2001"

Modeling and identification of an RRR-robot

Abstract: A dynamic model of a robot with 3 rotational degrees of freedom is derived in closed form. A systematic procedure for estimation of model dynamic parameters is suggested. It consists of the following steps: (i) identification of friction model parameters for each joint; (ii) calculation of optimal exciting trajectories, required for estimation of the remaining dynamic model parameters; (iii) estimation of these parameters using a least-squares method. The estimated model satisfactory reconstructs experimental control signals, justifying its use in model-based nonlinear control.

High performance regulator control for mechanical systems subjected to friction

Abstract: Several control strategies are compared with respect to their performance for regulator tasks on mechanical systems that exhibit friction. For this purpose a classic PID-controller combined with mass and frictional feedforward is compared to (i) a PID-controller combined with a model-based friction compensation using the dynamic LuGre friction model and (ii) a gain-scheduled optimal PD-controller based on a polytopic linear model (PLM). The latter consists of a feedforward part and an optimal nonlinear feedback part. The controllers are compared to the classic PID-controller by means of experiments on a rotating arm subjected to friction. The performance for three third order point to point setpoints shows that the gain-scheduled optimal PD-controller outperforms the other controllers with respect to settling time and maximal error after setpoint. The tracking performance is comparable for the LuGre-based controller and the classic PID-controller where the tracking performance of the gain-scheduled PD-controller is limited.

Friction induced limit cycling : hunting

Abstract: In this paper friction induced limit cycles are predicted for a simple motion system of a single motor-driven inertia subjected to friction and a PID-controlled regulator task. The two friction models used, i.e., (i) the dynamic LuGre friction model and (ii) the static Switch friction model, are compared with respect to the so-called hunting phenomenon. Analysis tools originating from the field of nonlinear dynamics will be used to investigate the friction induced limit cycles. For a varying controller gain, stable and unstable periodic solutions are computed numerically which, together with the stability analysis of the closed-loop fixed points, result in a bifurcation diagram. For both friction models, the bifurcation analysis indicates the disappearance of the hunting behaviour for controller gains larger than the gain corresponding to the cyclic fold bifurcation point.

Dynamics and control of the zero inertia powertrain

Abstract: Due to rotating inertias within the engine and transmission, the response of a vehicle during large engine speed transients may appear reluctant or even counteracting. Reminiscent of comparable behaviour in aircraft jet-propulsion, this phenomenon is also referred to as jet-start. To overcome this behaviour, a CVT powertrain is augmented with a plenetary gear set and a compact steel flywheel. The new transmission seamlessly combines two contradictory features: the driveability in terms of the pedal-to-wheel response is greatly improved and a large leap towards optimal fuel economy can be made. This is achieved by cruising the vehicle at extremely low engine speeds, using the large ratiocoverage of the CVT. The flywheel acts as a "peak shaver" during speed shifts: it delivers power during engine acceleration and absorbs kinetic energy during engine decelerations. In this paper, system dynamics and control aspects of the new powertrain are discussed.

Evaluation of (unstable) non-causal systems applied to iterative learning control

Abstract: This paper presents a new approach towards the design of iterative learning control (Moore, 1993). In linear motion control systems the design is often complicated by the inverse plant sensitivity being non-causal and even unstable. To overcome these problems the inverse system is split up in a causal and a non-causal part. We apply high-performance differentiating filters together with a mixed boundary value ODE solver to compute the control signal. This allows for an accurate approximation of the theoretical solution and offers the advantage of control over the boundary conditions of the learnt signal. The advantages of this approach over the existing commonly-used ZPETC technique are demonstrated by two examples, respectively a non-minimum-phase system and an industrial H-drive.