Hydraulic systems work under harsh conditions and fault diagnosis is of vital importance. The right set of sensors to collect data for the possible faults should be selected. In this paper, an electrohydraulic system model is established on Simscape on the Matlab platform, and typical hydraulic faults are simulated by using the software. Later, the data-driven approach was employed by using the Matlab Diagnostic Feature Designer Toolbox and Feature Classification Toolbox for best fit for fault identification.

A ball and beam mechanism is widely utilized in laboratory experiments to demonstrate the behavior of more complex systems. In this research, the phenomena such as nonlinear frictions, dead-zone and time-delay in the ball and beam mechanism's mathematical model is investigated. The following procedures are taken to construct a credible mathematical model of the system for this purpose. Firstly, the ball and beam mechanism's mathematical model, which includes different probable physically meaningful nonlinearities, is simulated using MATLAB\Simulink. Then, the Particle Swarm Optimization (PSO) algorithm is coded to determine the exact nonlinear model of a ball and beam system using the experimental data. Third, the accuracy of the results obtained from the PSO algorithm is tested using the hypothesis test and the confidence interval test. According to the statistical tests, the PSO algorithm is highly accurate in determining the parameters of the actual model of the system.

Industrial robots are traditionally programmed by hard-coding the desired motion into them. That approach, however, costs significant time and effort and shows little to no promise in transferring human skills to robots. Programming by demonstration (PbD) is an alternative approach that allows robots to learn tasks from demonstrations. Because of its several advantages over the traditional method, PbD is particularly suited for tasks encountered in assembly operations, the most typical of which is the peg-in-hole task. A successful PbD implementation for a peg-in-hole task requires that the peg should still be inserted into the hole even under situations that are not encountered during the demonstrations. Previous research in the field shows that the success rate of a peg-in-hole task under such cases varies greatly. In this study, we use a UR5 manipulator to experimentally investigate how the success rate of a peg-in-hole task changes with respect to the novelty of the task, quantified in terms of the distance of the hole to its original position. It is found that the success ratio decreases as the novelty of the task increases. To increase the performance, the use of strategies that alter the robot's motion dynamically in the run time is suggested for future work.

Selecting the most suitable motor sizes, gear boxes and structure under certain constraints or desired values such as payload, speed, deflections, total weight, etc. for a dynamic system is an exhaustive and time-consuming iterative process. To overcome this problem, a new-“evolving” conceptual design algorithm is developed. The suggested algorithm can be used for the conceptual design of any dynamic system including drive-train and structural optimization. To illustrate the suggested methodology, a robot manipulator, having 3 degrees of freedom, is selected as a case study. The objective function is minimizing the robot mass while satisfying the desired dynamic requirements and constraints of link deflections. A dynamic simulation environment for flexible body motion, containing 3 DOF robot manipulator drive-trains and flexible links, is developed in an evolving optimization loop. The lumped parameter estimation method is used to model the flexibility of uniform links in Simmechanics by allowing the estimation of deflections caused by the dynamic motion. Thus, both dynamic and structural simulations are made simultaneously in Simmechanics with no additional software. Hence, drive-trains and thickness of all links are simultaneously optimized by using the suggested evolving conceptual design algorithm.

Trajectory planning is one of the most studied topics in robotics. Among several methods, a sampling-based method, Rapidly-exploring Randomized Tree (RRT) algorithm, has become popular over the last two decades due to its computational efficiency. However, the RRT method does not suggest an exact way to obtain a smooth trajectory along the viapoints given by itself. In this paper, we present an approach using a timeoptimal trajectory planning algorithm, specifically for robotic manipulators without using inverse kinematics. After the trajectory smoothing with cubic splines in an environment with obstacles considering not only velocity and acceleration but also jerk constraints; the study is simulated on a six degrees of freedom humanoid robot arm model and always finds a solution successfully if there is a feasible one.