Human resources allocation for aircraft maintenance with predefined sequence

There are always large-scale items in the maintenances schedule of aircraft system, many of which have been fixed to be done in predefined sequences, which leads the workflow to be sys-tematically complex and makes this kind of problem quite different from all sorts of existing job-selection modes. On the other hand, the human resources are always limited and men have different working capabilities on different items, which make the allocation operation of human resources be much roomy. However, the final total time span of maintenance is often required to be as short as possible in many practices, in order to suffer only the lowest cost of loss while the system is stopping. A new model for op-timizing the allocation if aircraft maintenance human resources with the constraint of predefined sequence is presented. The ge-netic algorithm is employed to find the optimal solution that holds the shortest total time span of maintenance. To generate the ul-timate maintenance work items and the human resource array, the sequences among all maintenance work items are considered firstly, the work item array is then generated through traversal with the constraint of maintenance sequence matrix, and the human resources are finally allocated according to the work item array with the constraint of the maintenance capability. An example is demonstrated to show that the model and algorithm behave a satisfying performance on finding the optimal solution as expected.

Predefined Exponential Basis Set for Half-Bounded Multi Domain Spectral Method

A non-orthogonal predefined exponential basis set is used to handle half-bounded domains in multi domain spectral method (MDSM). This approach works extremely well for real-valued semi-infinite differential problems. It spans simultaneously wide range of exponential decay rates with multi scaling and does not suffer from zero crossing. These two conditions are necessary for many physical problems. For comparison, the method is used to solve different problems and compared with analytical and published results. The comparison exhibits the strengths and accuracy of the presented basis set.

Exponential Continuous Non-Parametric Neural Identifier With Predefined Convergence Velocity

This paper addresses the design of an exponential function-based learning law for artificial neural networks(ANNs)with continuous dynamics.The ANN structure is used to obtain a non-parametric model of systems with uncertainties,which are described by a set of nonlinear ordinary differential equations.Two novel adaptive algorithms with predefined exponential convergence rate adjust the weights of the ANN.The first algorithm includes an adaptive gain depending on the identification error which accelerated the convergence of the weights and promotes a faster convergence between the states of the uncertain system and the trajectories of the neural identifier.The second approach uses a time-dependent sigmoidal gain that forces the convergence of the identification error to an invariant set characterized by an ellipsoid.The generalized volume of this ellipsoid depends on the upper bounds of uncertainties,perturbations and modeling errors.The application of the invariant ellipsoid method yields to obtain an algorithm to reduce the volume of the convergence region for the identification error.Both adaptive algorithms are derived from the application of a non-standard exponential dependent function and an associated controlled Lyapunov function.Numerical examples demonstrate the improvements enforced by the algorithms introduced in this study by comparing the convergence settings concerning classical schemes with non-exponential continuous learning methods.The proposed identifiers overcome the results of the classical identifier achieving a faster convergence to an invariant set of smaller dimensions.

Connectivity-preserving consensus of nonlinear switched multi-agent systems with predefined accuracy

This paper focuses on the connectivity-preserving consensus of nonlinear switched multi-agent systems with predefined accuracy.Accordingly,a unified error transformation is adopted to preserve the initial interaction pattern determined by agents’limited communication ranges and initial states.Meanwhile,the so-called congelation of variables method is used to handle the unknown aperiodically time-varying parameters,which are fast-varying in an unknown compact set with only their radii known a priori.In addition,a series of continuously differentiable functions are incorporated into the Lyapunov function to design the controller.Based on Lyapunov stability theory,the proposed control algorithm guarantees that the consensus errors converge with a predefined accuracy,whereas most existing connectivity-preserving results can only ensure uniform ultimate boundedness.Simultaneously,all closed-loop signals remain bounded.Finally,two simulations are provided to validate the effectiveness of the proposed control protocol.

New nonlinear stiffness actuator with predefined torque‒deflection profile

A nonlinear stiffness actuator(NSA)could achieve high torque/force resolution in low stiffness range and high bandwidth in high stiffness range,both of which are beneficial for physical interaction between a robot and the environment.Currently,most of NSAs are complex and hardly used for engineering.In this paper,oriented to engineering applications,a new simple NSA was proposed,mainly including leaf springs and especially designed cams,which could perform a predefined relationship between torque and deflection.The new NSA has a compact structure,and it is lightweight,both of which are also beneficial for its practical application.An analytical methodology that maps the predefined relationship between torque and deflection to the profile of the cam was developed.The optimal parameters of the structure were given by analyzing the weight of the NSA and the mechanic characteristic of the leaf spring.Though sliding friction force is inevitable because no rollers were used in the cam-based mechanism,the sliding displacement between the cam and the leaf spring is very small,and consumption of sliding friction force is very low.Simulations of different torque‒deflection profiles were carried out to verify the accuracy and applicability of performing predefined torque‒deflection profiles.Three kinds of prototype experiments,including verification experiment of the predefined torque‒deflection profile,torque tracking experiment,and position tracking experiment under different loads,were conducted.The results prove the accuracy of performing the predefined torque‒deflection profile,the tracking performance,and the interactive performance of the new NSA.

Prescribed Performance Control of One-DOF Link Manipulator With Uncertainties and Input Saturation Constraint

In this paper, we mainly address the position control problem for one-degree of freedom(DOF) link manipulator despite uncertainties and the input saturation via the backstepping technique, active disturbance rejection control(ADRC) as well as predefined tracking performance functions. The extended state observer(ESO) is employed to compensate uncertain dynamics and disturbances, and it does not rely on the accurate model of systems. The tracking differentiator(TD) is utilized to substitute the derivative of the virtual control signals, and the explosion of complexity caused by repeated differentiations of nonlinear functions is removed. The auxiliary system is used to deal with the control input limitation, and the tracking accuracy and speed are improved by predefined tracking performance functions.With the help of the input-to-state stability(ISS) and Lyapunov stability theories, it is proven that the tracking error can be gradually converged into arbitrarily small neighborhood of the origin, and the tracking error is adjusted by suitable choice of control parameters. The simulation results are presented for the verification of the theoretical claims.