Recently, new distributed architectures of manufacturing systems are proposed, aiming at realizing more flexible control structures of the manufacturing systems. Many researches have been carried out to deal with the distributed architectures for planning and control of the manufacturing systems. However, the human operators have not yet been discussed for the autonomous components of the distributed manufacturing systems. A real-time scheduling method is proposed, in this research, to select suitable combinations of the human operators, the resources and the jobs for the manufacturing processes. The proposed scheduling method consists of following three steps. In the first step, the human operators select their favorite manufacturing processes which they will carry out in the next time period, based on their preferences. In the second step, the machine tools and the jobs select suitable combinations for the next machining processes. In the third step, the automated guided vehicles and the jobs select suitable combinations for the next transportation processes. The second and third steps are carried out by using the utility value based method and the dispatching rule-based method proposed in the previous researches. Some case studies have been carried out to verify the effectiveness of the proposed method.
Ant Colony Optimization (ACO), which is a type of swarm intelligence inspired by ants' foraging behavior, has been studied extensively and its effectiveness has been shown by many researchers. The previous studies have reported that MAX–MIN Ant System (MMAS) is one of effective ACO algorithms. The MMAS maintains the balance of intensification and diversification concerning pheromone by limiting the quantity of pheromone to the range of minimum and maximum values. In this paper, we propose MAX–MIN Ant System with Random Selection (MMASRS) for improving the search performance even further. The MMASRS is a new ACO algorithm that is MMAS into which random selection was newly introduced. The random selection is one of the edgechoosing methods by agents (ants). In our experimental evaluation using ten quadratic assignment problems, we have proved that the proposed MMASRS with the random selection is superior to the conventional MMAS without the random selection in the viewpoint of the search performance.
This paper considers linear time-invariant continuous-time servosystems with control input saturation nonlinearities, and proposes a design method of output feedback controllers satisfying a regional integral quadratic performance for the systems based on the generalized sector approach. The method assumes the output of the nonlinearities to be available for the control, and then it is an integrated design of dynamic output feedback and anti-windup compensators with a given servocompensator based on an error system regarding a steady state as a new origin. In this case, this paper clarifies that the design problem using the method can be recast as a convex optimization problem based on linear matrix inequalities (LMIs), and a problem setting initial states of the compensators can be recast as a problem solving linear matrix equations (LMEs), respectively. Finally, this paper points out that the proposed design method is helpful through a numerical example designing the servosystem with anti-windup structure via the derived LMIs and LMEs.
This paper describes the dynamics and impact model of a wheel with various radius rim. The dynamics is expressed by a rst order linear ordinary dierential equation with respect to the absolute orientation of the wheel, and an analytic solution is derived. Poincaré map is also derived analytically. Stability and basin of attraction (BoA) of the Poincaré map are discussed. Finally, the analysis is validated through some numerical simulations. As a result, the rim radius aects the stability and broadens its BoA. The analysis helps understanding of not only a geometric tracking control but also many underactuated control methods for bipeds.