In this paper, the combination of neural network (NN), proportional derivative (PD), and robust controller are used for determining the maximum load-carrying capacity (MLCC) of articulated robots, subject to both actuator and end-effector deflection constraints. The proposed technique is then applied to articulated robots, and MLCC is obtained for a given trajectory. In the practical simulations, it's impossible to determine the parameters of robot model exactly, so the trajectory tracking performance of the proportional integral derivative (PID) and computed torque methods significantly decrease. The PD control of robot has major problem, it cannot guarantee zero steady state error. For this reason, the NN controller with PD and robust controller are used. The multilayer neural network is also used to compensate gravity and friction effects. By using Lyapunov Direct Method it is shown that the stability of closed loop system would be guaranteed, if the weights of multilayer had certain learning rules. Standard back propagation algorithm is used as a learning algorithm to update the connection weights of the NN controller. The simulation results of the proposed adaptive robust neural network (ARNN) controller are compred with sliding mode and feedback linearization methods for flexible joint robot, and compared with open loop controller for 3D industrial robot. The obtained results assured the robustness and improvement in MLCC in the presence of uncertainties in dynamic model of the robot arm and external disturbances. In fact, adaptive robust NN controller suppresses disturbances accurately and achieves very small errors between commanded and actual trajectories.

The quality of the attachment of meat to bone is often reported to be insufficient by more and more poultry’s consumers. This is particularly true for thigh meat in broilers. The aim of this study was to compare muscle to bone attachment (namely, tendons) from a biomechanical and a biochemical point of view in 50 standard (S) and 50 Label Rouge (LR) chickens. Carcasses weighted around 1.7 kg in the two groups. Two tendons were harvested and proceeded for passive stretch tests, prior to cooking or not, to determine main mechanical characteristics (maximum load, stiffness and longitudinal strain). Biochemical parameters such as dry matter percentage, total collagen content, collagen solubility and sulphated glycosaminoglycans (sGAGs) content were also determined. Results showed that biomechanical values differ largely between the two studied tendons. For a given tendon, the values were also different between the two groups of chickens mainly after cooking. The results clearly showed that, mainly after cooking, the mechanical resistance of tendon to stretch was better in LR than in S chickens. LR chickens were reported to have tendons with higher collagen and sGAGs contents associated with a lower collagen solubility. These differences may explain biomechanical differences observed for the two types of tendons and could be due to increased age and/or higher physical activity of LR chickens.