In course of a examination project for EECI course “Analysis and Design of Hybrid Control Systems”, this project has been conducted. The formation of platoons into a hybrid system is modelled as a hybrid system. You can find the full project report here:

Hybrid Model of Platoon Formation

Below, you can find the simulation code for the multi agent case.

function [value discrete] = C(x) %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Matlab M-file % % Description: Flow set % Return 0 if outside of C, and 1 if inside C %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% K = round(length(x)/3); d = x(1:K); u = x(K+1:2*K); p = x(2*K+1:3*K); value = 0; for i = 1:K-1 if d(i+1) = d(i) if p(i+1) ~= p(i) inside = 1; end end end end

function inside = D(x) %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Matlab M-file % % Description: Jump set % Return 0 if outside of D, and 1 if inside D %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% K = round(length(x)/3); d = x(1:K); u = x(K+1:2*K); p = x(2*K+1:3*K); inside = 0; for i = 1:K-1 if d(i+1) >= d(i) if p(i+1) ~= p(i) inside = 1; end end end end

function xdot = f(x) %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Matlab M-file % % Description: Flow map %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % state K = round(length(x)/3); d = x(1:K); u = x(K+1:2*K); p = x(2*K+1:3*K); % differential equations xdot = [u;zeros(2*K,1)]; end

function xplus = g(x) %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Matlab M-file % % Description: Jump map %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % state K = round(length(x)/3); d = x(1:K); u = x(K+1:2*K); p = x(2*K+1:3*K); u_min = 10; u_max = 20; % decide whether u_min or u_max will be used in the set valued case r = rand(1); if r > 0.5 bar_u = u_max; else bar_u = u_min; end % search for the platoons that caused the jump for i = 1:K-1 if d(i+1) >= d(i) if p(i+1) ~= p(i) break; end end end % find all agents in these platoons N = find(p == p(i+1) | p == p(i)); p(N) = p(i); first_agent = sum(N == 1); last_agent = sum(N == K); if first_agent && last_agent || ~first_agent && ~last_agent % we're in the middel or one platoon u(N) = bar_u; elseif first_agent && ~last_agent % that's the first platoon u(N) = u_min; elseif ~first_agent && last_agent % that's the las tplatoon u(N) = u_max; end % the adjustment in order to end up in the flow set after the jump d(N) = d(N(1)); xplus = [d;u;p]; end

clear all % function run u_min = 10; u_max = 20; K = 9; % initial conditions d = cumsum(rand(1,K))*5; d = d(K:-1:1); d = d.'; u(1) = u_min; des = rand(1,K-2); u(2:K-1) = u_min*(des > 0.5) + u_max*(des priority for jumps % rule = 2 -> priority for flows rule = 1; options = odeset('RelTol',1e-6,'MaxStep',.1); maxStepCoefficient = .1; % set the maximum step length. At each run of the % integrator the option 'MaxStep' is set to % (time length of last integration)*maxStepCoefficient. % Default value = 0.1 % simulate [t x j] = HyEQsolver( @f,@g,@C,@D,x0,TSPAN,JSPAN,rule,options,maxStepCoefficient); % plot solution figure; subplot(2,1,1) plot(t,x(:,1:K)); grid on; % legend('d'interpreter','tex'); ylabel('$d_i$','interpreter','latex'); xlabel('time'); subplot(2,1,2); plot(t,x(:,2*K+1:end)); xlabel('time'); ylabel('$p_i$','interpreter','latex'); ylim([0,K+1]);

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