%---------------------------------------------------------------------------------------------------

% Copyright (c) Institute of Control Systems, Hamburg University of Technology. All rights reserved.

% Licensed under the GPLv3. See LICENSE in the project root for license information.

% Author(s): Christian Hespe

%---------------------------------------------------------------------------------------------------

classdef(Sealed) SimulationManager < handle

%SIMULATIONMANAGER Class to manage the simulation of multi-rate

%discrete-time multi-agent systems.

% This class makes it easier to simulate networks of agents with

% heterogeneous sample times among the agents and the network. The

% sample times do not need to be integer multiples.

properties(GetAccess = public, SetAccess = public)

lastLaplacian % Last value of the networks Laplacian matrix

end

properties(GetAccess = public, SetAccess = private)

nextAgentCall % Contains the time when each agent should be called next

nextNetworkCall % Contains the time when the network should be called next

needsUpdate % Flags for each agent, if it needs to be updated in the network

end

properties(GetAccess = public, SetAccess = immutable)

agents % Array of agents, that should be simulated

network % Network that the agents communicate over

end

methods

function obj = SimulationManager(network, agents)

%SIMULATIONMANAGER Construct an instance of this class

obj.agents = agents;

obj.network = network;

% Initialize times and flags to the beginning of the simulation

agentCount = length(agents);

obj.nextAgentCall = zeros(agentCount, 1);

obj.nextNetworkCall = 0;

obj.needsUpdate = ones(agentCount, 1, 'logical');

end

function k = estimateSteps(obj, T)

%ESTIMATESTEPS Estimates the number of calls to the step

%function that are required to reach the time T.

% The estimation of the number of calls to step() is required

% for preallocating the required buffer arrays. This

% function works by performing a dry run of the simulation,

% i. e. it stops the time but does not execute the agent

% functions.

% Initialize temporary counters to start of simulation

k = 0;

t = 0;

times = [obj.agents.dT, obj.network.cycleTime];

calls = zeros(size(times));

% Perform simulation steps until final time is reached

while t < T

k = k + 1;

% Calculate time of next simulation step

t = min(calls);

% Calculate next step for updated agents

mask = calls == t;

calls(mask) = calls(mask) + times(mask);

end

end

function t = step(obj)

%STEP This method steps the simulation forward in time

% The time step that is performed by this method does not

% need to be uniform. It steps to the next "event", i. e.

% update of an agent or update of the network. This enables

% multi-rate simulation with non-integer multiples of the

% fundamental frequencies.

% Calculate the time of the next action

nextCall = min(obj.nextAgentCall);

t = min(nextCall, obj.nextNetworkCall);

% Check if the agents are next to run

if nextCall <= obj.nextNetworkCall

for agent = obj.agents(obj.nextAgentCall == nextCall)

agent.step()

% Set update required

obj.needsUpdate(agent.id) = true;

% Set time for next call to this agent

obj.nextAgentCall(agent.id) = t + agent.dT;

end

end

% Check if the network is the next to run

if obj.nextNetworkCall <= nextCall

% If required, update the information that the network has

% about the agents

for agent = obj.agents(obj.needsUpdate)

obj.network.updateAgent(agent)

end

obj.needsUpdate(:) = false;

% Process sent messages in the network

recvMessages = obj.network.process();

obj.lastLaplacian = calculateLaplacian(recvMessages);

% Distribute transmitted messages among the agents

mask = ~cellfun(@isempty, recvMessages);

for agent = obj.agents(mask)

agent.receive(t, recvMessages{agent.id});

end

% Set time for next call

obj.nextNetworkCall = t + obj.network.cycleTime;

end

end

end

end

function L = calculateLaplacian(recvMessages)

�LCULATELAPLACIAN Function that calculates the Laplacian matrix based on

%the received messages and the sender information contained in the messages.

L = diag(cellfun(@length, recvMessages));

for i = 1:length(recvMessages)

L(i,[recvMessages{i}.sender]) = -1;

end

end