In recent years, the development of optimization methods in multi-agent systems has been remarkable. Most scheduling problems belong to NP-hard and it is not easy to solve them in large-scale systems. We have proposed applying the alternating direction method of multipliers for the consensus problem to the distributed scheduling problem and showed that the job shop scheduling problem is formulated by the consensus-based distributed scheduling problem. The distributed scheduling method is a method for finding a feasible solution by repeating solving subproblems and exchanging data. It is expected that it can be applied to large-scale problems. However, since subproblems also belong to NP-hard, there is a limit to the applicable scale. In this paper, we propose to use hybrid meta-heuristics to solve subproblems. For the scheduling problem of the rolling system, we constructed a hybrid meta-heuristic model using the local search method and mathematical programming method for the scheduling of the melt shop and evaluated the proposed method. The results of computer experiments show that meta-heuristics are effective for the consensus-based distributed scheduling method.
Event-triggered stabilization of a discrete-time system via a data-rate limited channel is studied. We concern the data rate required for stabilizability and provide a sufficient number of bits transmitted from the sensor to the controller at each event. When the controller receives a packet, it can extract information on the plant state from the reception timing and the event-triggering condition in addition to the bits carried by the packet. While the amount of the information is impaired by the communication delay and the discrete-time manner of the sampling, we show that for some systems the required data rate becomes lower than that obtained by the well-known data-rate theorem established for time-triggered systems.
This paper deals with repetitive learning control for functional electrical stimulation (FES) pedaling exploiting pedal force direction with antagonistic biarticular muscles . FES pedaling motion with muscle contraction is expressed as an Euler Lagrange system. Asymptotic tracking for the closed-loop system by the proposed controller is analyzed through Lyapunov-based methods. Experiments were conducted in three healthy individuals to show the validity of the proposed method from a practical point of view.