Takayuki WADA

A weighted averaging consensus algorithm over a noisy network is considered for a distributed power dispatch with load balancing. The availability of the communication among dispatching agents is represented as a network that is assumed to be a directed, balanced, and a weakly connected graph with no self loop. A simple representation of the consensus algorithm is given by using its graph Laplacian. The convergence analysis of the algorithm reveals an explicit relation between the number of iterations and the closeness of the agreement in terms of the characteristic values of the graph Laplacian, which gives a stopping rule for the consensus algorithms.

Stochastic consensus algorithms are considered for multi-agent systems over noisy unbalanced directed networks. The graph which represents a communication network of the system is assumed to contain a directed spanning tree, that is, a given digraph is weakly connected. Then two types of stochastic consensus are investigated, where one is for the agent states themselves and the other is for the time averages of the agent states. The convergence of the algorithms is investigated, which gives a stopping rule, i.e., an explicit relation between the number of iterations and the closeness of the agreement.

n the last 10 years, with the increase of renewable energies, great attention is devoted to operation and maintenance of wind turbines and wind farms, which are the fundamental objectives to be guaranteed by the design of control systems. This study proposes a trade-off approach between fatigue reduction and power extraction for wind farm scenarios. The focus of this approach is to reduce maintenance costs for fatigue loads. This issue is solved by an optimisation problem, in which the maximum fatigue load among the turbines in the wind farm is minimised, then pitch angle and tip-speed ratio are given as reference points for each turbine in the wind farm. Furthermore, the super-twisting sliding mode algorithm is used for the rotor speed control and it shows that the turbines work at the optimised operating points. Moreover, the sliding mode controller produces continuous torques and improves the wind turbine performance by enhancing energy capture and reducing dynamic loads. The effectiveness of the method is illustrated through simulations of two wind farm scenarios. The results obtained with the proposed approach are compared with the case when each turbine in the wind farm tracks the maximum power operating point.

This paper presents a bounded confidence gossip algorithm for describing the process of opinion formation over a communication network. Each agent in the network keeps a time-varying opinion vector (or state), which represents its opinion about a set of matters. A common confidence threshold is set for all of the agents. The states of agents in the network will be updated time by time according to an iterative procedure: At each time, 1) one agent is chosen randomly, then it chooses one of its neighbors on the communication graph to contact with; 2) they exchange their states; and 3) if they have different states and the distance between their states is strictly smaller than the confidence threshold, they update their states as the average of the two. This algorithm converges almost surely to some equilibrium point such that any two adjacent agents either have the same state or have distinct states whose distance is no less than the confidence threshold. This is called the constant confidence threshold algorithm. An increasing confidence threshold algorithm, which repeats the constant confidence threshold algorithm several times with increasing confidence threshold, is also proposed. The algorithm is also convergent almost surely to some equilibrium point. Applicability of the method to clustering problems is shown through numerical examples.