Adaptive SMC for trajectory tracking in freeform grinding
This work is a first step to the automation of freeform surface grinding. A control strategy for a robotic manipulator following a path which includes switching between different surfaces, constant depths of cut and system-inherent as well as environmental uncertainties is presented. The sliding mode control scheme with adaptive parameter update law is verified through simulation.
Adaptive trajectory tracking neural network control with robust compensator for robot manipulators
This paper presents an adaptive trajectory tracking neural network control using radial basis function (RBF) for an n-link robot manipulator with robust compensator to achieve the high-precision position tracking. One of the difficulties in designing a suitable control scheme which can achieve accurate trajectory tracking and good control performance is to guarantee the stability and robustness of control system, due to friction forces, external disturbances error, and parameter variations. To deal with this problem, the RBF network is investigated to the joint position control of an n-link robot manipulator. The RBF network is one approach which has shown a great promise in this sort of problems because of its fast learning algorithm and better approximation capabilities. The adaptive RBF network can effectively improve the control performance against large uncertainty of the system. The adaptive turning laws of network parameters are derived using the back-propagation algorithm and the Lyapunov stability theorem, so that the stability of the entire system and the convergence of the weight adaptation are guaranteed. In this control scheme, a robust compensator plays as an auxiliary controller to guarantee the stability and robustness under various environments such as the mass variation, the external disturbances, and modeling uncertainties. Finally, the simulation and experimental results in comparison with adaptive fuzzy and wavelet network control method are provided to verify the effectiveness of the proposed control methodology.
Influence of filler addition, storage medium and evaluation time on biaxial flexure strength and modulus of adhesive systems
Abstract Objective. This study evaluated the effects of filler addition, storage medium and time on biaxial flexural strength and flexural modulus of six adhesive systems. Materials and methods. The adhesives were either unfilled resins: Single Bond, Prime&Bond 2.1 and One-Step; or filled resins: Single Bond Plus, Prime&Bond NT and One-Step Plus. Resin discs of each product (0.5 mm thick × 6.0 mm diameter) were prepared using silicon molds (n = 10). The discs were stored dry, in water, mineral oil or ethanol for 1 week or 3 months before biaxial flexural testing. Data were statistically analyzed by 3-way ANOVA and Tukey's post-hoc test (α = 0.05). The specimens were tested in a universal testing machine at 1.27 mm/min until failure occurred. Results. In general, the storage in ethanol led to significantly lower flexural strength and flexural modulus values than the other storage conditions for all adhesives in both storage periods. Filler addition increased flexural strength and flexural modulus for Prime&Bond NT, when it was stored dry or in water for 1 week. For Single Bond Plus, the filler addition resulted in higher flexural modulus, when it was stored for 1 week in oil and in ethanol. No significant differences in flexural modulus were observed between One-Step and One-Step Plus in any storage medium. Conclusions. Results suggested that filler addition does not necessarily have to increase the flexural strength and flexural modulus. Ethanol and oil storages tended to yield opposite effects.