Adaptive mechanically controlled lubrication mechanism found in articular joints

Articular cartilage is a highly efficacious water-based tribological system that is optimized to provide low friction and wear protection at both low and high loads (pressures) and sliding velocities that must last over a lifetime. Although many different lubrication mechanisms have been proposed, it is becoming increasingly apparent that the tribological performance of cartilage cannot be attributed to a single mechanism acting alone but on the synergistic action of multiple "modes" of lubrication that are adapted to provide optimum lubrication as the normal loads, shear stresses, and rates change. Hyaluronic acid (HA) is abundant in cartilage and synovial fluid and widely thought to play a principal role in joint lubrication although this role remains unclear. HA is also known to complex readily with the glycoprotein lubricin (LUB) to form a cross-linked network that has also been shown to be critical to the wear prevention mechanism of joints. Friction experiments on porcine cartilage using the surface forces apparatus, and enzymatic digestion, reveal an "adaptive" role for an HA-LUB complex whereby, under compression, nominally free HA diffusing out of the cartilage becomes mechanically, i.e., physically, trapped at the interface by the increasingly constricted collagen pore network. The mechanically trapped HA-LUB complex now acts as an effective (chemically bound) "boundary lubricant"-reducing the friction force slightly but, more importantly, eliminating wear damage to the rubbing/shearing surfaces. This paper focuses on the contribution of HA in cartilage lubrication; however, the system as a whole requires both HA and LUB to function optimally under all conditions.

Adaptive fast finite-time multiple-surface sliding control for a class of uncertain non-linear systems

This paper concerns the adaptive fast finite-time multiple-surface sliding control (AFFTMSSC) problem for a class of high-order uncertain non-linear systems of which the upper bounds of the system uncertainties are unknown. By using the fast control Lyapunov function and the method of so-called adding a power integrator merging with adaptive technique, a recursive design procedure is provided, which guarantees the fast finite-time stability of the closed-loop system. Further, it is proved that the control input is bounded.

Sliding mode-like learning control for SISO complex systems with T-S fuzzy models

In this paper, a sliding mode-like learning control scheme is developed for a class of single input single output (SISO) complex systems. First, the Takagi-Sugeno (T-S) fuzzy modelling technique is employed to model the uncertain complex dynamical systems. Second, a sliding mode-like learning control is designed to drive the sliding variable to converge to the sliding surface, and the system states can then asymptotically converge to zero on the sliding surface. The advantages of this scheme are that: 1) the information about the uncertain system dynamics and the system model structure is not required for the design of the learning controller; 2) the closed-loop system behaves with a strong robustness with respect to uncertainties; 3) the control input is chattering-free. The sufficient conditions for the sliding mode-like learning control to stabilise the global fuzzy model are discussed in detail. A simulation example for the control of an inverted pendulum cart is presented to demonstrate the effectiveness of the proposed control scheme.