In control of cooperating manipulator systems, force distribution is a central problem. For rigid manipulators, solutions are based on various criteria such as minimum potential damage to the grasped object, minimum energy consumption and optimal load distribution in actuators. However, when considering manipulators with structural flexibility, minimizing the deflections of the mechanical components becomes a highly desirable objective. In this paper, we propose a force distribution scheme based on a local minimum of the strain energy stored in the elastic links of the cooperating manipulators. Numerical example are given to demonstrate the effectiveness of the scheme.

In this paper, we consider the object trajectory tracking control for flexible-link cooperating manipulators. In particular, we develop a stable inversion control law which is commonly known as the inverse dynamics control or the computed torque method for rigid manipulators. Difficulties in applying this method to the control of flexible link manipulators are due to the fact that the inverse dynamics system is generally unstable. As such, bounded actuator torques cannot be guaranteed. For multiple manipulators handling a common object, there are more actuators than the degrees of freedom of the system. Through decomposing the manipulator end-effector wrenches into resultant and internal force components, control laws are designed such that the internal forces are used to stabilize the system zero dynamics. Consequently, nonlinear inversion control can be applied for the object trajectory tracking control.

This paper describes the application of neural networks to gearbox fault diagnosis. In order to increase learning speed of BP network, a modified learning algorithm was presented. Considering of the difficulty of choosing neural networks’ architecture, genetic algorithm was employed. The discussion of the effect of hidden layer nodes shows that with the increase of the number of nodes, the learning speed increase also yet result in poor generalization ability. The test of fault tolerance ability tells that neural networks have ‘bench type’ tolerance ability. This ensures that when signals were contaminated by noise or feature extraction methods were not effective, the result can still be acceptable. To test the performance of the application of neural networks on gearbox fault diagnosis, experiments of single fault and multi-faults were both implemented and diagnosed by neural networks. The results were satisfied.

Optimizing the processing parameters of fused deposition modeling is a challenging task due to the significant number of factors that influence the quality of the fabricated parts. Therefore, optimizing the dimensional accuracy, surface roughness, or mechanical properties usually requires a considerable amount of test samples. In the presented work, the design of experiments approach was used to minimize the number of tests needed to optimize the factors. Most of the work available in literature did not investigate the interactions between the factors or their second-order effects on the strength and stiffness of fused deposition modeling parts. In this work, the effects of the layer thickness, extrusion temperature, and infill percentage are investigated and optimized to achieve the optimum strength and stiffness of the fabricated parts. The response surface method was used to investigate the interaction effects and second-order effects on the tensile strength and elastic modulus of the fabricated samples. It was found that some interaction and second-order effects exist and affect the strength and stiffness of FDM parts. Generally, increasing the extrusion temperature and infill percentage increase the tensile strength and the elastic modulus. In addition, the predicted data matches the experimental results well. However, multi-objective optimization is needed when choosing the layer thickness.

In this paper, an innovative piezoelectric energy harvesting unit (PEHU) for the piezoelectric energy harvesting system (PEHS) from highway traffic is introduced. The proposed PEHU contains a non-linear force amplification mechanism, which substantially increases the electricity output of the PEHU in contrast to the conventional designs with direct loading to the piezoelectric stacks. Quasi-static laboratory tests have been performed to validate the design and the mathematical model. In a quasi-static load cycle of 1333N, a preloaded PEHU prototype is able to generate a voltage of 128V and a potential electric energy of 120mJ with a displacement as small as 2.54mm. The energy density that the PEHS can potentially deliver is estimated to be 8.64J/(m.pass.lane) and is the highest reported in the literature. This level of power generation suggests that the PEHU has a great potential for roadway energy harvesting.

The multi-objective optimal control design usually generates hundreds or thousands of Pareto optimal solutions. How to assist an user to select an appropriate controller to implement is a post-processing issue. In this paper, we develop a method of cluster analysis of the Pareto optimal designs to discover the similarity of the optimal controllers. After we identify the clusters of optimal controllers, we then develop a switching strategy to select controls from different clusters to improve the performance. Numerical results show that the switching control algorithm is quite promising.

Feedback controls are important to the improvement of dynamic performance of high-speed trains. However, designing an active control for these vehicles is a very challenging task because the control system is usually under-actuated and has to meet multiple conflicting objectives. Examples of conflicting objectives include designing a highly relative stable system while minimizing the control efforts or maximizing the capability of the system to reject external disturbances. In addition, the mathematical models of these systems are not completely controllable and observable. This paper studies multi-objective optimal design of feedback controls for a sub-system of high-speed trains, i.e. the bogie system. The bogie system can be decomposed such that the observable and controllable components of the model are used to stabilize the internal states and therefore the overall system. A linear mathematical model of the system is used in the design. The controllable and the observable states of the model are separated to form a state-feedback control to drive the internal modes and the whole system to stability. A multi-objective genetic algorithm is used to search for the feedback control gains to optimize three objectives: the Frobenius norm of the control law, relative stability and the disturbance rejection. The solutions of the multi-objective optimization provide various trade-offs among the objectives. Numerical simulations show that the proposed control designs can stabilize the system even at a high critical speed of 500 km/h.

The airfoil/wing design is probably the most important part of an aircraft design. A practical aerodynamic design of airfoil requires optimal performance on a wide range of operating conditions. These requirements are often found to be conflicting and demand designer expertise for satisfactory results, not to mention the computational burden of the simulations. Although there exists many studies on direct and inverse design of airfoils, less attention has been paid to simultaneous consideration of multiple objectives. In this paper, a multi-objective optimal airfoil design procedure is presented. PARSEC parametrization method has been utilized to express the airfoil geometry in terms of twelve physical parameters. The aerodynamic performance is obtained by 2D panel method using XFOIL package. Multi-Objective Particle Swarm Optimization (MOPSO) algorithm has been applied for airfoil geometry design because it is efficient and keeps the diversity among the solution set. The objective functions and constraints are chosen to enhance the flight performance at takeoff, cruise, and landing conditions for a long range cargo aircraft. Objectives include maximization of lift to drag ratio ( C L /C D ), maximization of rate of change of lift to attack angle ( dC L /dα ) for having increased lift at takeoff/landing condition and minimization of pitching moment C M 2 . Two applied constraints are C L > C Lmin at operating condition and thickness ≤ %25. Each evaluation is consist of finding the optimal operating angle of attack and reporting the corresponding objective values. The quality of the solution at various generations has been studied to guarantee the convergence of the solution. Like any other multi-objective optimization problem (MOP), the solution would be a set of Pareto optimal configurations. Although having multiple solutions gives us a better understanding of the problem, only one configuration should be chosen by the designer. A post processing technique is also used to help the decision maker to choose the most appropriate compromise in the solution set. The method is found to be effective in finding efficient set of airfoils. The simulation is also found to be effective because it can be done on a regular personal computer. It should be noticed that the method can be easily applied to other airfoil design applications by simply modifying the objective functions and the constraints.

In this paper, a multi-objective optimization of a time-delayed fractional order damping system is presented. In previous studies, a single objective optimization of this system was based on minimizing the integral of the squared error J ISE , integral of the time-weighted squared error J ITSE , integral absolute error J IAE , or integral time-weighted absolute error J ITAE . However, from the practical point of view other design goals such as those related to system’s response and stability should be also included since an error-based optimized system can result in a system having the smallest error but at the expense of other crucial objectives. Moreover, the one-objective optimization returns only one design option hiding many important details about other possible design preferences. Here, the system under study is optimized by considering three objectives: peak time t p , the percentage overshoot M p (%), and J ISE . The results show that the multi-objective optimization technique provides more information about the system behavior and grants the decision maker different design options, called the Pareto set. In addition, this study investigates the meaning of the aforementioned integral errors within the multi-objective optimization scope.