# Example 6.3: Optimal input design

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    % Section 6.3.2, Figure 6.6
    % Boyd & Vandenberghe "Convex Optimization"
    % Original by Lieven Vandenberghe
    % Adapted for CVX by Joelle Skaf - 09/26/05
    %
    % Consider a dynamical system with scalar input sequence u(0),u(1),...,u(N)
    % and scalar output sequence y(0),y(1),...,y(N) related by the convolution
    % y = h*u where h = [h(0),h(1),...,h(N)] is the impulse response.
    % Our goal is to choose an input sequence to minimize the weighted sum:
    %           minimize J_track + delta*J_der + eta*J_mag
    % where J_track = 1/(N+1) sum_{t=0}^N (y(t) - y_des(t))^2
    %       J_mag   = 1/(N+1) sum_{t=0}^N u(t)^2
    %       J_der   = 1/N sum_{t=0}^{N-1} (u(t+1) - u(t))^2

    % Input data
    m = 201;  n = 201;  N=200;
    t = [0:m-1]';
    h = (1/9)*((.9).^t) .* (1 - 0.4*cos(2*t));   % sum(h) is approx. 1

    H = toeplitz(h', [h(1) zeros(1,n-1)]);
    % m1 = round(m/6); m2 = round(m/5); m3 = round(m/5);  m4 = m-m1-m2-m3;
    m1 = round(m/5); m2 = round(m/4); m3 = round(m/4);  m4 = m-m1-m2-m3;
    y_des = [zeros(m1,1); ones(m2,1); -ones(m3,1); zeros(m4,1)];

    D = [-eye(n-1) zeros(n-1,1)];
    D = D + [zeros(n-1,1) eye(n-1)];

    delta = [0 0 0.3];
    eta = [0.005 0.05 0.05];
    disp('Finding the optimal input for ');
    for i = 1:length(delta)
        disp(['* delta = ' num2str(delta(i)) ' and eta = ' num2str(eta(i))]);
        cvx_begin quiet
        variable u(N+1)
        minimize ( square_pos(norm(H*u - y_des))/(N+1) + ...
                   eta(i)*square_pos(norm(u))/(N+1) + ...
                   delta(i)*square_pos(norm(D*u))/N )
        cvx_end
        switch(i)
            case 1
                figure(1); plot(t,u); xlabel('t'); ylabel('u(t)');
                title(['Input u(t) for \delta = ' num2str(delta(i)) ' and  \eta = ' num2str(eta(i))]);
                %         print -deps smoothreg1u.eps
                figure(2); plot(t,H*u); xlabel('t'); ylabel('y(t)');
                title(['Output y(t) for \delta = ' num2str(delta(i)) ' and  \eta = ' num2str(eta(i))]);
                %         print -deps smoothreg1y.eps
            case 2
                figure(3); plot(t,u); xlabel('t'); ylabel('u(t)');
                title(['Input u(t) for \delta = ' num2str(delta(i)) ' and  \eta = ' num2str(eta(i))]);
                %         print -deps smoothreg2u.eps
                figure(4); plot(t,H*u); xlabel('t'); ylabel('y(t)');
                title(['Output y(t) for \delta = ' num2str(delta(i)) ' and  \eta = ' num2str(eta(i))]);
                %         print -deps smoothreg2y.eps
            case 3
                figure(5); plot(t,u); xlabel('t'); ylabel('u(t)');
                title(['Input u(t) for \delta = ' num2str(delta(i)) ' and  \eta = ' num2str(eta(i))]);
                %         print -deps smoothreg3u.eps
                figure(6); plot(t,H*u); xlabel('t'); ylabel('y(t)');
                title(['Output y(t) for \delta = ' num2str(delta(i)) ' and  \eta = ' num2str(eta(i))]);
                %         print -deps smoothreg3y.eps
        end
    end

    Finding the optimal input for
    * delta = 0 and eta = 0.005
    * delta = 0 and eta = 0.05
    * delta = 0.3 and eta = 0.05