Experimental comparison of parameter estimation methods in adaptive robot control

In the literature on adaptive robot control a large variety of parameter estimation methods have been proposed, ranging from tracking-error-driven gradient methods to combined tracking- and prediction-error-driven least-squares type adaptation methods. This paper presents experimental data from a comparative study between these adaptation methods, performed on a two-degrees-of-freedom robot manipulator. Our results show that the prediction error concept is sensitive to unavoidable model uncertainties. We also demonstrate empirically the fast convergence properties of least-squares adaptation relative to gradient approaches. However, in view of the noise sensitivity of the least-squares method, the marginal performance benefits, and the computational burden, we (cautiously) conclude that the tracking-error driven gradient method is preferred for parameter adaptation in robotic applications

Robust vehicle suspension system by converting active and passive control of a vehicle to semi-active control ystem analytically

This research article deals with a simplified translational model of an automotive suspension system which is constructed by considering the translation motion of one wheel of a car. Passive Vehicle Suspension System is converted into Semi Active Vehicle System. Major advantage achieved by this system is that it adjusts the damping of the suspension system without the application of any actuator by using MATLAB® simulations. The semi-active control is found to control the vibration of suspension system very well

Identification of Photovoltaic Arrays' Maximum Power Extraction Point via Dynamic Regressor Extension and Mixing

This paper deals with the problem of identification of photovoltaic arrays' maximum power extraction point---information that is encrypted in the current-voltage characteristic equation. We propose a new parameterisation of the classical five parameter model of this function that, combined with the recently introduced identification technique of dynamic regressor extension and mixing, ensures a fast and accurate estimation of all unknown parameters. A concavity property of the current-voltage characteristic equation is then exploited to directly identify the desired voltage operating point. Realistic numerical examples via computer simulations are presented to assess the performance of the proposed approach.Comment: This paper is submitted to th International Journal of Adaptive Control and Signal Processin

Titan imagery with Keck adaptive optics during and after probe entry

We present adaptive optics data from the Keck telescope, taken while the Huygens probe descended through Titan's atmosphere and on the days following touchdown. No probe entry signal was detected. Our observations span a solar phase angle range from 0.05° up to 0.8°, with the Sun in the west. Contrary to expectations, the east side of Titan's stratosphere was usually brightest. Compiling images obtained with Keck and Gemini over the past few years reveals that the east-west asymmetry can be explained by a combination of the solar phase angle effect and an enhancement in the haze density on Titan's morning hemisphere. While stratospheric haze was prominent over the northern hemisphere, tropospheric haze dominated the south, from the south pole up to latitudes of ∼45°S. At 2.1 μm this haze forms a polar cap, while at 1.22 μm it appears in the form of a collar at 60°S. A few small clouds were usually present near the south pole, at altitudes of 30–40 km. Our narrowband J,H,K images of Titan's surface compare extremely well with that obtained by Cassini ISS, down to the small-scale features. The surface contrast between dark and bright areas may be larger at 2 μm than at 1.6 and 1.3 μm, which would imply that the dark areas may be covered by a coarser-grained frost than the bright regions and/or that there is additional 2 μm absorption there

Active fault tolerant control for nonlinear systems with simultaneous actuator and sensor faults

The goal of this paper is to describe a novel fault tolerant tracking control (FTTC) strategy based on robust fault estimation and compensation of simultaneous actuator and sensor faults. Within the framework of fault tolerant control (FTC) the challenge is to develop an FTTC design strategy for nonlinear systems to tolerate simultaneous actuator and sensor faults that have bounded first time derivatives. The main contribution of this paper is the proposal of a new architecture based on a combination of actuator and sensor Takagi-Sugeno (T-S) proportional state estimators augmented with proportional and integral feedback (PPI) fault estimators together with a T-S dynamic output feedback control (TSDOFC) capable of time-varying reference tracking. Within this architecture the design freedom for each of the T-S estimators and the control system are available separately with an important consequence on robust L₂ norm fault estimation and robust L₂ norm closed-loop tracking performance. The FTTC strategy is illustrated using a nonlinear inverted pendulum example with time-varying tracking of a moving linear position reference. Keyword

Angular velocity nonlinear observer from single vector measurements

The paper proposes a technique to estimate the angular velocity of a rigid body from single vector measurements. Compared to the approaches presented in the literature, it does not use attitude information nor rate gyros as inputs. Instead, vector measurements are directly filtered through a nonlinear observer estimating the angular velocity. Convergence is established using a detailed analysis of a linear-time varying dynamics appearing in the estimation error equation. This equation stems from the classic Euler equations and measurement equations. As is proven, the case of free-rotation allows one to relax the persistence of excitation assumption. Simulation results are provided to illustrate the method.Comment: 10 pages, 8 figures. arXiv admin note: substantial text overlap with arXiv:1503.0287