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Abstract This paper presents a method for quantifying drivers’ driving skills using closed-loop dynamic simulations of articulated heavy vehicles (AHVs). Due to AHVs’ multi-unit configurations, large sizes, and high centers of gravity (CGs), these large vehicles exhibit poor directional performance. AHVs require wide roads and large radii of path curvature for evasive maneuvers; these large vehicles frequently display unstable motion modes, including trailer sway, jackknifing, and rollover. The directional performance of AHVs is frequently evaluated in terms of maneuverability and lateral stability. There exists a trade-off between the performance measures of maneuverability and lateral stability. An AHV, its driver and the road constitute a unique closed-loop dynamic system. The unique dynamic characteristics of AHVs impose significant challenges for safe operations of these vehicles. However, little attention has been paid to the interactions of driver-AHV. This paper tackles the problem of quantifying drivers’ driving skills considering the interactions of driver-AHV under simulated single lane-change (SLC) maneuvers. Based on two driver characteristic parameters, namely preview time (PT) and transport delay (TD), we propose two performance measures, i.e., path-following score (PFS) and combined stability score (CSS), as the indicators for assessing the driving skills of AHV drivers under the simulated maneuvers. The driver-AHV closed-loop dynamic simulation is implemented using the built-in driver model and virtual vehicles developed in TruckSim. The numerical simulation results for rearward amplification (RA) measures are validated using driver-in-the-loop (DIL) real-time simulation. The simulation identifies AHV drivers’ driving skills in terms of lateral stability, path-following, as well as balanced path-following and stability regions considering PT and TD properties.

A bivariate gamma-type density function involving a confluent hypergeometric function of two variables is being introduced. The inverse Mellin transform technique is employed in conjunction with the transformation of variable technique to obtain its moment generating function, which is expressed in terms of generalized hypergeometric functions. Its cumulative distribution function is given in closed form as well. Many distributions such as the bivariate Weibull, Rayleigh, half-normal and Maxwell distributions can be obtained as limiting cases of the proposed gamma-type density function. Computable representations of the moment generating functions of these distributions are also provided.

Abstract Based on the lifting technique, the lifted state space model and the lifted transfer function model can be derived to describe non-uniformly sampled-data (NUSD) systems. However, the lifted models are inconvenient for both identification and control purposes due to the causality constraint and the model complexity. To solve this problem, a novel model of NUSD systems is proposed by introducing a time-varying backward shift operator. The proposed model is more concise in structure and has fewer parameters than the lifted models, using which the traditional identification methods and control strategies of single-rate systems can be easily extended to NUSD systems. The advantages and effectiveness of the proposed model are well illustrated by a simulation example.

Support from the Chilean Fondecyt project 1040926 and the Millennium Science Institute ‘‘Complex Engineering Systems’’ (www.sistemasdeingenieria.cl) is greatly acknowledged.

This paper is concerned with control of processes with uncertain delays for disturbance rejection. The effect of the uncertain delays on the stability is studied. First, the method to compute the maximum uncertain delay that a given controller can tolerate is described. Second, in the case of PI/PID controller, all of the admissible controller parameters stabilizing a system with uncertain but bounded delays are determined. Meanwhile, we propose a simple method to construct the parameter space satisfying a given robustness index for the nominal model. In the admissible regions satisfying various objectives, the global optimum controller is achieved for disturbance rejection in the presence of uncertain delay. As a result, the MIGO ( ${{M}_{s}}$ -constrained Integral Gain Optimization) method is revisited in the case of uncertain delay, and the rule of selecting the value of maximum sensitivity function is proposed in terms of the bound on the uncertain delay. Two simulation examples and an experiment are given to demonstrate the effectiveness and advantage of the proposed method.

This paper considers the trajectory tracking problem of hybrid machines. A hybrid machine here refers to a machine that is driven by the constant velocity (CV) motors and servomotors in a proper configuration. The hybrid machine is a meaningful tradeoff between task flexibility and power capacity. However, this system has brought a new challenge to control due to the velocity fluctuation in the CV motor. The velocity fluctuation problem is caused mainly by the uncontrollable input current and the time-varying workload. In addition, the dynamic parameters are uncertain, which further increases the control difficulty. In this paper, we propose an adaptive control law for the trajectory tracking and demonstrate the effectiveness of this control law on a 2-DOF closed-chain five-bar hybrid mechanism driven by one servomotor and one CV motor. The principle of the proposed controller is to properly design the servomotor control input that can compensate not only the uncertainty in the servomotor but also the uncertainty in the CV motor. By the proposed adaptive control law, it can be theoretically proved that the position/velocity tracking errors of the joint associated with the servomotor and the velocity tracking error of the joint associated with the CV motor are convergent to zero as time goes to infinity. Finally, the simulation examples are given to illustrate the effectiveness of the proposed method.

This work considers the problem of determining the transition of ethanol-producing bio-reactors from batch to continuous operation and subsequent control subject to constraints and performance considerations. To this end, a Lyapunov-based non-linear model predictive controller is utilized that stabilizes the bio-reactor under continuous mode of operation. The key idea in the predictive controller is the formulation of appropriate stability constraints that allow an explicit characterization of the set of initial conditions from where feasibility of the optimization problem and hence closed-loop stability is guaranteed. Additional constraints are incorporated in the predictive control design to expand on the set of initial conditions that can be stabilized by control designs that only require the value of the Lyapunov function to decay. Then, the explicit characterization of the set of stabilizable initial conditions is used in determining the appropriate time for which the reactor must be run in batch mode. Specifically, the predictive control approach is utilized in determining the appropriate batch length that achieves stabilizable values of the state variables at the end of the batch. Application of the proposed method to the ethanol production process using Zymomonas mobilis as the ethanol producing micro-organism demonstrates the effectiveness of the proposed model predictive control strategy in stabilizing the bio-reactor.

We present Buzz, a novel programming language for heterogeneous robot swarms. Buzz advocates a compositional approach, offering primitives to define swarm behaviors both from the perspective of the single robot and of the overall swarm. Single-robot primitives include robot-specific instructions and manipulation of neighborhood data. Swarm-based primitives allow for the dynamic management of robot teams, and for sharing information globally across the swarm. Self-organization stems from the completely decentralized mechanisms upon which the Buzz run-time platform is based. The language can be extended to add new primitives (thus supporting heterogeneous robot swarms), and its run-time platform is designed to be laid on top of other frameworks, such as the Robot Operating System. We showcase the capabilities of Buzz by providing code examples, and analyze scalability and robustness of the run-time platform through realistic simulated experiments with representative swarm algorithms.

L605 (ASTM F90), a cobalt-chromium-tungsten alloy with excellent mechanical properties and high radiopacity, has been widely accepted as a suitable alloy for stent applications. The presence of carbides in this alloy, primary carbides and secondary carbides, leads to difficulties in controlling mechanical performances and therefore in optimizing stent size and performances. This work is thus to investigate the carbides and their role in advanced mechanical properties of L605 alloy for stent fabrication. Herein, the nature, nucleation, distribution and dissolution of the carbides were investigated in a series of recrystallized L605 tubes from hard-drawn (HD) state. The mechanical properties corresponding to each carbide state were examined by tensile tests and microhardness measurements. The results indicate important relationships among carbide precipitation, grain size and mechanical behaviors, as a function of annealing temperature and duration. The intergranular secondary carbides, induced at the onset of the recrystallization of L605 matrix, were preferentially precipitated at grain boundaries. The nucleation of such particulate phase leads to a pinning effect on grain coarsening, resulting in a strengthening effect of the material. However, the further growth of the secondary carbides brings about considerable reduction of ductility, which is inacceptable for stent application. Therefore, an optimization protocol on carbides controlling was developed to maintain the strengthening effect without losing ductility and small grain size.