, "Current sensor-based feed cutting force intelligent estimation and tool wear condition monitoring," IEEE Trans. Ind. Electron., vol. 47, pp. 697-702, July 2000. [9] X. Li, "On-line detection of the breakage of small diameter drills using current signature wavelet transform," Int. J. Mach. Tools, Manuf., vol. 39, no. 1, pp. 157-164, 1999 Abstract-In this paper, we consider the regulation control problem for an underactuated overhead crane system. Motivated by recent passivitybased controllers for underactuated systems, we design several controllers that asymptotically regulate the planar gantry position and the payload angle. Specifically, utilizing LaSalle's invariant set theorem, we first illustrate how a simple proportional-derivative (PD) controller can be utilized to asymptotically regulate the overhead crane system. Motivated by the desire to achieve improved transient performance, we then present two nonlinear controllers that increase the coupling between the planar gantry position and the payload angle. Experimental results are provided to illustrate the improved performance of the nonlinear controllers over the simple PD controller.

In this paper, a learning-based feedforward term is developed to solve a general control problem in the presence of unknown nonlinear dynamics with a known period. Since the learning-based feedforward term is generated from a straightforward Lyapunov-like stability analysis, the control designer can utilize other Lyapunov-based design techniques to develop hybrid control schemes that utilize learning-based feedforward terms to compensate for periodic dynamics and other Lyapunov-based approaches (e.g., adaptive-based feedforward terms) to compensate for nonperiodic dynamics. To illustrate this point, a hybrid adaptive/learning control scheme is utilized to achieve global asymptotic link position tracking for a robot manipulator.

hi this st,u.dy, we co,iisider the nonlinecir co,ntrol of kinernaticcilly red,u;ndunt robot manipulators. Specifically, we use U Lyup u n o~u technique to design U model bused nonlineur controller that i i c l i ~C I J C S czpoircrrt%ct,l l i d position ar1.11 s,ub-tusk trucking. We then illrrstr~ute h o w the iriodel bused controller CUIL be redesigned us an adnpti,iie firllstute feedback controller thut achie,ues asymptotic link pos,i.tror, cind sii,li-tusk tr.ucking despite pwrumetiic uncertainty ussociuted with the dynambc model. We ulso illustrate how the model based controller can be redesigned us an eruct model knowledge outp,ut feedback controller that achieves semi-global exponential h i k position and su.b-task tracking despite the luck of link velocity ,iirecis,irr'einents. We note that the co,ntrol strategy does not require the coriip,utatioii of iwerse kinemutics und does not place a n y restriction on the self-motion of the munipw.lutor; hence, the extra degrees of freedom are uvuiluble f o r subtusks (i.e., ,muantairzing ~it~,cirii~u.lab~il~ity, u~uoidaiice of joint limits and obstacle avoidance).Sirnwlation results ure included to illustrute the performunce of the control 1U. W.

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This paper addresses the link position setpoint control problem of n–link robotic manipulators with amplitude-limited control inputs. We design a global-asymptotic exact model knowledge controller and a semi-global asymptotic controller which adapts for parametric uncertainty. Explicit bounds for these controllers can be determined; hence, the required input torque can be calculated a priori so that actuator saturation can be avoided. We also illustrate how the proposed control algorithm in this paper can be slightly modified to produce a proportional-integral-derivative (PID) controller which contains a saturated integral term. Experimental results are provided to illustrate the improved performance of the proposed control strategy over a standard adaptive controller that has been artificially limited to account for torque saturation.