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Leukemia-Treatment-Project" -"# Leukemia-Treatment-Project" +## References +### Data_Sheet_2_Model-Based Simulation of Maintenance Therapy of Childhood Acute Lymphoblastic Leukemia with deviations.csv +F. Jost, J. Zierk, T. T. Le, T. Raupach, M. Rauh, M. Suttorp, M. Stanulla, M. Metzler, and S. Sager, “Model-based simulation of maintenance therapy of Childhood Acute Lymphoblastic Leukemia,” Frontiers in Physiology, vol. 11, 2020. diff --git a/Steve_Jost_correct_theta5_ANC.png b/Steve_Jost_correct_theta5_ANC.png new file mode 100644 index 0000000..4f7bbf3 Binary files /dev/null and b/Steve_Jost_correct_theta5_ANC.png differ diff --git a/Steve_Jost_correct_theta5_PD.png b/Steve_Jost_correct_theta5_PD.png new file mode 100644 index 0000000..8f7e119 Binary files /dev/null and b/Steve_Jost_correct_theta5_PD.png differ diff --git a/Steve_Jost_correct_theta5_PK.png b/Steve_Jost_correct_theta5_PK.png new file mode 100644 index 0000000..d588a48 Binary files /dev/null and b/Steve_Jost_correct_theta5_PK.png differ diff --git a/Steve_Jost_expected_PK.png b/Steve_Jost_expected_PK.png new file mode 100644 index 0000000..fcbabce Binary files /dev/null and b/Steve_Jost_expected_PK.png differ diff --git a/Steve_Jost_linearized_trajectory.png b/Steve_Jost_linearized_trajectory.png new file mode 100644 index 0000000..41313a3 Binary files /dev/null and b/Steve_Jost_linearized_trajectory.png differ diff --git a/Steve_Jost_noisy_KF_control.png b/Steve_Jost_noisy_KF_control.png new file mode 100644 index 0000000..f8d1442 Binary files /dev/null and b/Steve_Jost_noisy_KF_control.png differ diff --git a/Steve_Jost_noisy_KF_reactive.png b/Steve_Jost_noisy_KF_reactive.png new file mode 100644 index 0000000..3715273 Binary files /dev/null and b/Steve_Jost_noisy_KF_reactive.png differ diff --git a/Steve_Jost_noisy_reactive.png b/Steve_Jost_noisy_reactive.png new file mode 100644 index 0000000..78e24a0 Binary files /dev/null and b/Steve_Jost_noisy_reactive.png differ diff --git a/Steve_Jost_wrong_theta5_PK.png b/Steve_Jost_wrong_theta5_PK.png new file mode 100644 index 0000000..6f18777 Binary files /dev/null and b/Steve_Jost_wrong_theta5_PK.png differ diff --git a/c2d_jost.m b/c2d_jost.m new file mode 100644 index 0000000..2f648c2 --- /dev/null +++ b/c2d_jost.m @@ -0,0 +1,9 @@ +function [A,b,C] = c2d_jost(x1_sym,x2_sym,x3_sym,x4_sym,x5_sym,x6_sym,x7_sym,x8_sym,u_sym,dfdx,dfdu,dgdx,x,u_double,t_s) + +% This function finds the state matrices in discrete time for the inverted +% pendulum problem only with n = 4 and m = 2 +A = double(subs(dfdx,{x1_sym,x2_sym,x3_sym,x4_sym,x5_sym,x6_sym,x7_sym,x8_sym,u_sym},{x(1),x(2),x(3),x(4),x(5),x(6),x(7),x(8),u_double})); +b = double(subs(dfdu,{x1_sym,x2_sym,x3_sym,x4_sym,x5_sym,x6_sym,x7_sym,x8_sym,u_sym},{x(1),x(2),x(3),x(4),x(5),x(6),x(7),x(8),u_double})); +C = double(dgdx); + +end \ No newline at end of file diff --git a/inv_pend_cart_sim.m b/inv_pend_cart_sim.m index 3fd4b9b..b3cfaa7 100644 --- a/inv_pend_cart_sim.m +++ b/inv_pend_cart_sim.m @@ -61,6 +61,7 @@ b_nominal = zeros([n N]); u_start = 0; u_end = 5; + u_nominal = zeros([1 N]); x_nominal = zeros([n 1 N]); for iter = 1:N diff --git a/jost.m b/jost.m new file mode 100644 index 0000000..c8c391a --- /dev/null +++ b/jost.m @@ -0,0 +1,48 @@ +function [f] = jost(t,x,d,theta) +%JOST state ODE for 6-MP and 6-TGN leukopoiesis +% 8 state model described in Jost et al. 2020 +% d = dose +% theta = vector of parameters + + +if t<14 + u = d; +else + u = 0; +end + + +% define matrices and nonlinear components +A = [-theta(1) 0 0 0 0 0 0 0; ... + theta(1) -theta(2) 0 0 0 0 0 0; ... + 0 theta(3)*theta(4) -theta(5) 0 0 0 0 0; ... + 0 0 0 -theta(7) 0 0 0 0; ... + 0 0 0 theta(7) -theta(7) 0 0 0; ... + 0 0 0 0 theta(7) -theta(7) 0 0; ... + 0 0 0 0 0 theta(7) -theta(7) 0; ... + 0 0 0 0 0 0 theta(7) -theta(10)]; + +B = [0.22;0;0;0;0;0;0;0]; + +f_hat = [0;... + 0;... + 0;... + theta(7)*((theta(6)/x(8))^theta(9))*x(4)-theta(7)*theta(8)*((theta(6)/x(8))^theta(9))*x(3)*x(4);... + 0;... + 0;... + 0;... + 0]; + +f = A*x + B*u + f_hat; + +% dfdx = A + ... +% [0 0 0 0 0 0 0 0;... +% 0 0 0 0 0 0 0 0;... +% 0 0 0 0 0 0 0 0;... +% 0 0 -theta(7)*theta(8)*((theta(6)/x(8))^theta(9))*x(4) theta(7)*((theta(6)/x(8))^theta(9))-theta(7)*theta(8)*((theta(6)/x(8))^theta(9))*x(3) 0 0 0 -theta(6)*theta(7)*((theta(6)/x(8))^(theta(9)-1))*x(4)/(x(8)^2)+theta(6)*theta(7)*theta(8)*((theta(6)/x(8))^(theta(9)-1))*x(3)*x(4)/(x(8)^2);... +% 0 0 0 0 0 0 0 0;... +% 0 0 0 0 0 0 0 0;... +% 0 0 0 0 0 0 0 0;... +% 0 0 0 0 0 0 0 0]; + +end \ No newline at end of file diff --git a/jost_fwd_euler.m b/jost_fwd_euler.m new file mode 100644 index 0000000..c0c115b --- /dev/null +++ b/jost_fwd_euler.m @@ -0,0 +1,53 @@ +function [x_tp1, dfdx, dfdu, dgdx] = jost_fwd_euler(t,x_t,d,theta,dt) +%JOST state ODE for 6-MP and 6-TGN leukopoiesis +% this one is for linearization purposes; contains extra return arg dfdu +% 8 state model described in Jost et al. 2020 +% d = dose +% theta = vector of parameters + + +if t<14 + u = d; +else + u = 0; +end + + +% define matrices and nonlinear components +A = [-theta(1) 0 0 0 0 0 0 0; ... + theta(1) -theta(2) 0 0 0 0 0 0; ... + 0 theta(3)*theta(4) -theta(5) 0 0 0 0 0; ... + 0 0 0 -theta(7) 0 0 0 0; ... + 0 0 0 theta(7) -theta(7) 0 0 0; ... + 0 0 0 0 theta(7) -theta(7) 0 0; ... + 0 0 0 0 0 theta(7) -theta(7) 0; ... + 0 0 0 0 0 0 theta(7) -theta(10)]; + +B = [0.22;0;0;0;0;0;0;0]; + +f_hat = [0;... + 0;... + 0;... + theta(7)*((theta(6)/x_t(8))^theta(9))*x_t(4)-theta(7)*theta(8)*((theta(6)/x_t(8))^theta(9))*x_t(3)*x_t(4);... + 0;... + 0;... + 0;... + 0]; + +x_tp1 = x_t+(A*x_t + B*u + f_hat)*dt; + +dfdx = eye(8)+(A + ... + [0 0 0 0 0 0 0 0;... + 0 0 0 0 0 0 0 0;... + 0 0 0 0 0 0 0 0;... + 0 0 -theta(7)*theta(8)*((theta(6)/x_t(8))^theta(9))*x_t(4) theta(7)*((theta(6)/x_t(8))^theta(9))-theta(7)*theta(8)*((theta(6)/x_t(8))^theta(9))*x_t(3) 0 0 0 -theta(6)*theta(7)*((theta(6)/x_t(8))^(theta(9)-1))*x_t(4)/(x_t(8)^2)+theta(6)*theta(7)*theta(8)*((theta(6)/x_t(8))^(theta(9)-1))*x_t(3)*x_t(4)/(x_t(8)^2);... + 0 0 0 0 0 0 0 0;... + 0 0 0 0 0 0 0 0;... + 0 0 0 0 0 0 0 0;... + 0 0 0 0 0 0 0 0])*dt; + +dfdu = B*dt; + +dgdx = [0 0 0 0 0 0 0 1]; + +end \ No newline at end of file diff --git a/jost_noisy.m b/jost_noisy.m new file mode 100644 index 0000000..5841f6d --- /dev/null +++ b/jost_noisy.m @@ -0,0 +1,48 @@ +function [f] = jost_noisy(t,x,d,theta,var_w,dt) +%JOST state ODE for 6-MP and 6-TGN leukopoiesis with system noise +% 8 state model described in Jost et al. 2020 +% d = dose +% theta = vector of parameters +% var_w = noise variance + +if t<14 + u = d; +else + u = 0; +end + + +% define matrices and nonlinear components +A = [-theta(1) 0 0 0 0 0 0 0; ... + theta(1) -theta(2) 0 0 0 0 0 0; ... + 0 theta(3)*theta(4) -theta(5) 0 0 0 0 0; ... + 0 0 0 -theta(7) 0 0 0 0; ... + 0 0 0 theta(7) -theta(7) 0 0 0; ... + 0 0 0 0 theta(7) -theta(7) 0 0; ... + 0 0 0 0 0 theta(7) -theta(7) 0; ... + 0 0 0 0 0 0 theta(7) -theta(10)]; + +B = [0.22;0;0;0;0;0;0;0]; + +f_hat = [0;... + 0;... + 0;... + theta(7)*((theta(6)/x(8))^theta(9))*x(4)-theta(7)*theta(8)*((theta(6)/x(8))^theta(9))*x(3)*x(4);... + 0;... + 0;... + 0;... + 0]; + +f = A*x + B*u + f_hat + normrnd(0,var_w,[8,1])/dt; + +% dfdx = A + ... +% [0 0 0 0 0 0 0 0;... +% 0 0 0 0 0 0 0 0;... +% 0 0 0 0 0 0 0 0;... +% 0 0 -theta(7)*theta(8)*((theta(6)/x(8))^theta(9))*x(4) theta(7)*((theta(6)/x(8))^theta(9))-theta(7)*theta(8)*((theta(6)/x(8))^theta(9))*x(3) 0 0 0 -theta(6)*theta(7)*((theta(6)/x(8))^(theta(9)-1))*x(4)/(x(8)^2)+theta(6)*theta(7)*theta(8)*((theta(6)/x(8))^(theta(9)-1))*x(3)*x(4)/(x(8)^2);... +% 0 0 0 0 0 0 0 0;... +% 0 0 0 0 0 0 0 0;... +% 0 0 0 0 0 0 0 0;... +% 0 0 0 0 0 0 0 0]; + +end \ No newline at end of file diff --git a/jost_sim.m b/jost_sim.m new file mode 100644 index 0000000..2358424 --- /dev/null +++ b/jost_sim.m @@ -0,0 +1,142 @@ +% Simu_symlation Length +t_s = step_size_noisy; +t_end = 21; +N = num_t_noisy; +time = [1:t_s:t_end]; + +% Dimension +% n: dimension of state vector x +n = 8; +% m: dimension of observation vector y +m = 1; + +% Initialize motion function +syms x1_sym x2_sym x3_sym x4_sym x5_sym x6_sym x7_sym x8_sym u_sym + +f = [-theta(1) 0 0 0 0 0 0 0; ... + theta(1) -theta(2) 0 0 0 0 0 0; ... + 0 theta(3)*theta(4) -theta(5) 0 0 0 0 0; ... + 0 0 0 -theta(7) 0 0 0 0; ... + 0 0 0 theta(7) -theta(7) 0 0 0; ... + 0 0 0 0 theta(7) -theta(7) 0 0; ... + 0 0 0 0 0 theta(7) -theta(7) 0; ... + 0 0 0 0 0 0 theta(7) -theta(10)]*[x1_sym;x2_sym;x3_sym;x4_sym;x5_sym;x6_sym;x7_sym;x8_sym] + ... + [0.22;0;0;0;0;0;0;0]*u_sym + ... + [0;... + 0;... + 0;... + theta(7)*((theta(6)/x8_sym)^theta(9))*x4_sym-theta(7)*theta(8)*((theta(6)/x8_sym)^theta(9))*x3_sym*x4_sym;... + 0;... + 0;... + 0;... + 0]; + +f = [x1_sym;x2_sym;x3_sym;x4_sym;x5_sym;x6_sym;x7_sym;x8_sym] + t_s * f; + +dfdx = jacobian(f,[x1_sym;x2_sym;x3_sym;x4_sym;x5_sym;x6_sym;x7_sym;x8_sym]); +dfdu = jacobian(f,u_sym); + +% Initialize observation function +g = [x8_sym]; +dgdx = jacobian(g,[x1_sym;x2_sym;x3_sym;x4_sym;x5_sym;x6_sym;x7_sym;x8_sym]); + +% Initialize disturbance xi and covariance Q +xi = zeros([n 1 N]); +Q = zeros([n n N]); +sig = 0.01; +Q_const = sig * eye(n); +for iter = 1:N + Q(:,:,iter) = Q_const; + xi(:,:,iter) = Q(:,:,iter) * randn([n,1]); +end +clear sig Q_const + +% Initialize measurement noise n and covariance R +nn = zeros([m 1 N]); +R = zeros([m m N]); +sig = 0.1; +R_const = sig * eye(m); +for iter = 1:N + R(:,:,iter) = R_const; + nn(:,:,iter) = R(:,:,iter) * randn([m,1]); +end +clear sig R_const + +% Initialize nominal trajectory and control to track +% nominal control is set as linear for test purposes +A_nominal = zeros([n n N]); +b_nominal = zeros([n N]); + +% convert and reshape for Sam's code +u_nominal = transpose(u_ref_flattened(1:N,1)); + +x_nominal = zeros([n 1 N]); +% Simulate System for the nominals +x_o = x0; +for k = 1:N + % Compute linearized c2d-ed state matrices, we use this to simulate the + % system because we do not know the discrete time nonlinear equation + % for the inverse pendulum problem + if k == 1 + x_tmp = x_o; + else + x_tmp = x_nominal(:,:,k-1); + end + [A_actual,b_actual,~] = c2d_jost(x1_sym,x2_sym,x3_sym,x4_sym,x5_sym,x6_sym,x7_sym,x8_sym,u_sym,dfdx,dfdu,dgdx,x_tmp,u_nominal(:,k),t_s); + A_nominal(:,:,k) = A_actual; + b_nominal(:,k) = b_actual; + % Time step + x_nominal(:,:,k) = double(subs(f,{x1_sym,x2_sym,x3_sym,x4_sym,x5_sym,x6_sym,x7_sym,x8_sym,u_sym},{x_tmp(1),x_tmp(2),x_tmp(3),x_tmp(4),x_tmp(5),x_tmp(6),x_tmp(7),x_tmp(8),u_nominal(:,k)})); +end +clear k x_tmp A_actual b_actual + + +% Initialize Cost Parameters +W = zeros([n n N+1]); +rho = zeros([n 1 N+1]); +lambda = zeros([1 N]); +W_const = 100 * eye(n); +lambda_const = 0.1; +for iter = 1:N+1 + W(:,:,iter) = W_const; + if iter == 1 + rho(:,:,iter) = x_o; + else + rho(:,:,iter) = x_nominal(:,:,iter-1); + end +end +for iter = 1:N + lambda(:,iter) = lambda_const; +end +clear W_const lambda_const + +% Note: Iteration variables does not correspond to the notes due to matlab +% indexing start with 1 instead of 0 + +x_o = x0; +x_hat_o = x0+[-0.3;0.2;0.05;0.01;-0.5;0.01;0;-0.32]; % CHANGE this? +sigma_o = 0.2 * eye(n); % CHANGE THIS? + +% Simulate System with control +is_controlled = 1; +[x_c,y_c,x_hat_p_c,u_c] = simulate_jost(N,n,m,t_s,x1_sym,x2_sym,x3_sym,x4_sym,x5_sym,x6_sym,x7_sym,x8_sym,u_sym,f,dfdx,dfdu,dgdx,xi,Q,nn,R,W,rho,lambda,x_o,x_hat_o,sigma_o,A_nominal,b_nominal,x_nominal,u_nominal,is_controlled); + + +% Process Result +x1_c_plot = [x_o(1,1);reshape(x_c(1,1,:),[N 1])]; +x2_c_plot = [x_o(2,1);reshape(x_c(2,1,:),[N 1])]; +x3_c_plot = [x_o(3,1);reshape(x_c(3,1,:),[N 1])]; +x4_c_plot = [x_o(4,1);reshape(x_c(4,1,:),[N 1])]; +x5_c_plot = [x_o(5,1);reshape(x_c(5,1,:),[N 1])]; +x6_c_plot = [x_o(6,1);reshape(x_c(6,1,:),[N 1])]; +x7_c_plot = [x_o(7,1);reshape(x_c(7,1,:),[N 1])]; +x8_c_plot = [x_o(8,1);reshape(x_c(8,1,:),[N 1])]; +y1_c_plot = [x_o(1,1);reshape(y_c(1,1,:),[N 1])]; +x1_hat_c_plot = [x_hat_o(1,1);reshape(x_hat_p_c(1,1,:),[N 1])]; +x2_hat_c_plot = [x_hat_o(2,1);reshape(x_hat_p_c(2,1,:),[N 1])]; +x3_hat_c_plot = [x_hat_o(3,1);reshape(x_hat_p_c(3,1,:),[N 1])]; +x4_hat_c_plot = [x_hat_o(4,1);reshape(x_hat_p_c(4,1,:),[N 1])]; +x5_hat_c_plot = [x_hat_o(5,1);reshape(x_hat_p_c(5,1,:),[N 1])]; +x6_hat_c_plot = [x_hat_o(6,1);reshape(x_hat_p_c(6,1,:),[N 1])]; +x7_hat_c_plot = [x_hat_o(7,1);reshape(x_hat_p_c(7,1,:),[N 1])]; +x8_hat_c_plot = [x_hat_o(8,1);reshape(x_hat_p_c(8,1,:),[N 1])]; diff --git a/nominal_trajectory.m b/nominal_trajectory.m new file mode 100644 index 0000000..8074e01 --- /dev/null +++ b/nominal_trajectory.m @@ -0,0 +1,699 @@ +%% 1.1 Trajectory with Jayachandran et al. model - theta_p estimate +% %% 1.1.1 Intialization +% clear +% bsa = 1.71; +% theta = [4.2;3.8;39.4;15.11;1;0.08;0.3287;8.2e9;0.4386;0.1207;0.5346;0.0782;84]; +% % initial values (from Chapman et al.) +% % x0 = [0;0;0;1.2e11;1.2e11;1.2e11;1.2e11;theta(8)*(theta(7)/theta(10)-1)^(1/theta(9))]; +% x0 = [0;0;0;1.2625e11;1.2625e11;1.2625e11;1.2625e11;0.2851e11]; +% % x0 = [0;0;0;0;0;0;0;0]; +% d = 50*bsa; % 6-MP dosage (mg 6-MP/m^2 body surface area) +% +% %% 1.1.2 Calculation of trajectory for 5 cycles +% state = []; +% t = []; +% x0_i = x0; +% num_cycles = 34; +% num_t = 21; % number of timepoints to evaluate ODE45 +% tspan = linspace(1,21,num_t); +% for i=1:num_cycles +% [t_i,state_i] = ode45(@(t,x)jayachandran(t,x,d,theta),tspan,x0_i); +% t_i = t_i + 20*(i-1); % shift the time index to the current cycle +% % check lower bound +% if state_i(end,8) < 1e9 +% d = 0.8*d; +% elseif state_i(end,8)*0.6 > 2e9 % check upper bound +% d = 1.2*d; +% end +% state = [state state_i]; +% t = [t t_i]; +% x0_i = state_i(end,:); +% end +% +% clear i; +% +% %% 1.1.3 Plot of trajectory for 5 cycles +% figure() +% hold on +% xlabel('Days Elapsed') +% ylabel('WBC Count') +% title("WBC Count vs Days Elapsed") +% for i=1:num_cycles +% plot(t(:,i),state(:,8*i)) +% end +% hold off +% +% clear i; +% +% figure() +% hold on +% xlabel('Days Elapsed') +% ylabel('Neutrophil Count') +% title("Neutrophil Lower Bound vs Days Elapsed") +% for i=1:num_cycles +% plot(t(:,i),0.4*state(:,8*i)) +% end +% hold off +% +% clear i; + +% %% Trajectory with theta_p estimate +% +% % WBC trajectory over 5 cycles; lower bound is +% [time1,state1] = ode45(@(t,x)jayachandran(t,x,d),[0 20],x0); +% +% % check lower bound +% if state1(end,8) < 1e9 +% d = 0.8*d; +% sprintf("less") +% elseif state1(end,8)*0.6 > 2e9 % check upper bound +% d = 1.2*d; +% end +% [time2,state2] = ode45(@(t,x)jayachandran(t,x,d),[0 20],state1(end,:)); +% time2 = time2+20; +% +% % check lower bound +% if state2(end,8)*0.4 < 1e9 +% d = 0.8*d; +% sprintf("less") +% elseif state2(end,8)*0.6 > 2e9 % check upper bound +% d = 1.2*d; +% end +% [time3,state3] = ode45(@(t,x)jayachandran(t,x,d),[0 20],state2(end,:)); +% time3 = time3+20*2; +% +% % check lower bound +% if state3(end,8)*0.4 < 1e9 +% d = 0.8*d; +% sprintf("less") +% elseif state3(end,8)*0.6 > 2e9 % check upper bound +% d = 1.2*d; +% end +% [time4,state4] = ode45(@(t,x)jayachandran(t,x,d),[0 20],state3(end,:)); +% time4 = time4+20*3; +% +% % check lower bound +% if state4(end,8)*0.4 < 1e9 +% d = 0.8*d; +% fprintf("less") +% elseif state4(end,8)*0.6 > 2e9 % check upper bound +% d = 1.2*d; +% end +% [time5,state5] = ode45(@(t,x)jayachandran(t,x,d),[0 20],state4(end,:)); +% time5 = time5+20*4; +% +% % [time1_ext,state1_ext] = ode45(@(t,x)jayachandran(t,x),[0 200],x0); +% +% figure() +% hold on +% plot(time1,state1(:,8)) +% [state1_max,state1_max_idx] = max(state1); +% state1_max +% plot(time2,state2(:,8)) +% plot(time3,state3(:,8)) +% plot(time4,state4(:,8)) +% plot(time5,state5(:,8)) +% % plot(time1_ext,state1_ext(:,8)) +% xlabel('Days Elapsed') +% ylabel('WBC Count') +% legend("cycle 1","cycle 2","cycle 3","cycle 4","cycle 5") +% title("WBC Count vs Days Elapsed") +% hold off +% +% figure() +% hold on +% plot(time1,0.4*state1(:,8)) +% plot(time2,0.4*state2(:,8)) +% plot(time3,0.4*state3(:,8)) +% plot(time4,0.4*state4(:,8)) +% plot(time5,0.4*state5(:,8)) +% xlabel('Days Elapsed') +% ylabel('ANC') +% legend("cycle 1","cycle 2","cycle 3","cycle 4","cycle 5") +% title("Neutrophil Lower Bound vs Days Elapsed") +% hold off +% +% %% Moar Plots +% [time1,state1] = ode45(@(t,x)jayachandran(t,x,d),[0 200],x0); +% figure() +% hold on +% plot(time1,state1(:,1)) +% plot(time1,state1(:,2)) +% hold off +% +% figure() +% hold on +% plot(time1,state1(:,3)) +% hold off +% +% figure() +% hold on +% plot(time1,state1(:,4)) +% plot(time1,state1(:,5)) +% plot(time1,state1(:,6)) +% plot(time1,state1(:,7)) +% hold off +% +% figure() +% hold on +% plot(time1,state1(:,8)) +% hold off +%% 1.2 Trajectory with Jayachandran et al. model - modified treatment protocol +%% 1.2.1 Intialization +clear +clc +bsa = 1.71; +theta = [4.2;3.8;39.4;15.11;1;0.08;0.3287;8.2e9;0.4386;0.1207;0.5346;0.0782;84]; +% initial values (from Chapman et al.) +% x0 = [0;0;0;1.2e11;1.2e11;1.2e11;1.2e11;theta(8)*(theta(7)/theta(10)-1)^(1/theta(9))]; +x0 = [0;0;0;1.2625e11;1.2625e11;1.2625e11;1.2625e11;0.2851e11]; +% x0 = [0;0;0;0;0;0;0;0]; +dose = 500000*bsa; % 6-MP dosage (mg 6-MP/m^2 body surface area) +%% +%u = @(t,d) d*(t<=14) + 0*(t>14); +H = @(x) (x >= 0); +u = @(t,d) H(t)-H(t-14); +% define matrices and nonlinear components +A = [-theta(1) 0 0 0 0 0 0 0; ... + theta(1) -theta(2) 0 0 0 0 0 0; ... + 0 0 -theta(6) 0 0 0 0 0; ... + 0 0 0 -theta(10) 0 0 0 0; ... + 0 0 0 theta(10) -theta(10) 0 0 0; ... + 0 0 0 0 theta(10) -theta(10) 0 0; ... + 0 0 0 0 0 theta(10) -theta(10) 0; ... + 0 0 0 0 0 0 theta(10) -theta(11)]; + +B = [5875.4;0;0;0;0;0;0;0]; + +f_hat = @(t,x) [0;... + -theta(3)*x(2)/(theta(4)+x(2));... + theta(5)*theta(3)*x(2)/(theta(4)+x(2));... + (theta(7)*(theta(8)^theta(9))/(theta(8)^theta(9)+x(8)^theta(9))-theta(12)*x(3)/(theta(13)+x(3)))*x(4);... + 0;... + 0;... + 0;... + 0]; + +f = @(t,x,d) A*x + B*0.2*bsa*u(t) + f_hat(t,x); +%% 1.2.2 Calculation of trajectory for 5 cycles +state = []; +t = []; +x0_i = x0; +num_cycles = 7; +num_t = 21; % number of timepoints to evaluate ODE45 +tspan = linspace(1,21,num_t); +for i=1:num_cycles + [t_i,state_i] = ode45(@(t,x)f(t,x,dose),tspan,x0_i); + t_i = t_i + 20*(i-1); % shift the time index to the current cycle + % check lower bound + if state_i(end,8) < 1.5e9 + dose = 0.8*dose; + elseif state_i(end,8) > 3.0e9 % check upper bound + dose = 1.2*dose; + end + x_dot = @(t,x)f(t,x,dose); + state = [state state_i]; + t = [t t_i]; + x0_i = state_i(end,:); +end % sanity check code + +clear i; + +%% 1.2.3 Plot of trajectory for 5 cycles +figure() +hold on +xlabel('Days Elapsed') +ylabel('WBC Count') +title("WBC Count vs Days Elapsed") +for i=1:num_cycles + plot(t(:,i),state(:,8*i)) +end +hold off + +clear i; + +% figure() +% hold on +% xlabel('Days Elapsed') +% ylabel('Neutrophil Count') +% title("Neutrophil Lower Bound vs Days Elapsed") +% for i=1:num_cycles +% plot(t(:,i),0.4*state(:,8*i)) +% end +% hold off + +clear i; + +%% 2. Trajectory with Jost et al. model +%% 2.1 Intialization of constants +clear +bsa = 1.71; +theta = [31.2;12.72;0.019;9.9216;0.219*(bsa^1.16);2.06;0.146;0.103;0.866;2.3765]; +% initial values (from Jost et al.) +x0 = [0;... + 0;... + 0;... + theta(6)*theta(10)/theta(7);... + theta(6)*theta(10)/theta(7);... + theta(6)*theta(10)/theta(7);... + theta(6)*theta(10)/theta(7);... + theta(6)]; + +num_cycles = 34; + +%% 2.2 Calculation of reference trajectory for 5 cycles + +x_ref = []; +t_ref = []; +u_ref = []; +step_size_ref = 0.01; % very fine step size +num_t_ref = (1/step_size_ref)*21+1; % number of timepoints to evaluate ODE45 +tspan = linspace(0,21,num_t_ref); + +u_i = 50*bsa; % 6-MP dosage (mg 6-MP/m^2 body surface area) +x0_i = x0; + +for i=1:num_cycles + u_ref = [u_ref [u_i*(ones((1/step_size_ref)*14,1));zeros((1/step_size_ref)*(20-14)+1,1)]]; + [t_i,x_i] = ode45(@(t,x)jost(t,x,u_i,theta),tspan,x0_i); + t_i = t_i + 21*(i-1); % shift the time index to the current cycle + % check lower bound + if x_i(end,8) < 1 + u_i = 0.8*u_i; + elseif x_i(end,8) > 2 % check upper bound + u_i = 1.2*u_i; + end + x_ref = [x_ref x_i]; + t_ref = [t_ref t_i]; + x0_i = x_i(end,:); +end + +clear i t_i x_i x0_i u_i tspan; + +% flatten to single nx8 matrix +x_ref_flattened = []; +t_ref_flattened = []; +u_ref_flattened = []; +for i=1:num_cycles + x_ref_flattened = [x_ref_flattened; x_ref(:,8*(i-1)+1:8*i)]; + t_ref_flattened = [t_ref_flattened; t_ref(:,i)]; + u_ref_flattened = [u_ref_flattened; u_ref(:,i)]; +end +clear i; + +%% 2.3 Plot of 6-MP trajectory + +figure() +hold on +xlabel('Days Elapsed') +ylabel('Amount (mg) or Concentration (mg/L blood)') +title("6-MP and 6-TGN Amount vs Days Elapsed") +plot(t_ref_flattened,x_ref_flattened(:,1)) +plot(t_ref_flattened,x_ref_flattened(:,2)) +plot(t_ref_flattened,x_ref_flattened(:,3)) +legend("x_1 6-MP in the gut (mg)","x_2 6-MP in the bloodstream (mg)","x_3 6-TGN in the bloodstream (mg/L blood)") +hold off + +%% 2.4 Plot of number of proliferating cells, number of cells in each compartment, and number of mature neutrophils + +figure() +hold on +xlabel('Days Elapsed') +ylabel('Number of cells / L blood') +title("Number of Cells per L Blood vs Days Elapsed") +plot(t_ref_flattened,x_ref_flattened(:,4)*1e9) +plot(t_ref_flattened,x_ref_flattened(:,5)*1e9) +plot(t_ref_flattened,x_ref_flattened(:,6)*1e9) +plot(t_ref_flattened,x_ref_flattened(:,7)*1e9) +plot(t_ref_flattened,x_ref_flattened(:,8)*1e9) +legend("x_4 Proliferating cells","x_5 Compartment 1","x_6 Compartment 2","x_7 Compartment 3","x_8 Mature neutrophils") +hold off + + +%% 2.5 Plot of neutrophil trajectory (color-coded by cycle) + +figure() +hold on +xlabel('Days Elapsed') +ylabel('ANC (Number of cells / L blood)') +title("All Neutrophil Count (ANC) x_8 vs Days Elapsed") +for i=1:num_cycles + plot(t_ref(:,i),x_ref(:,8*i)*10^9) +end +yline(1e9,'-b','Desired lower bound') +yline(2e9,'-r','Desired upper bound') +hold off + +clear i; + +%% 2.6 Linearization + +bsa = 1.71; % trajectory near original bsa +theta = [31.2;12.72;0.019;9.9216;0.219*(bsa^1.16);2.06;0.146;0.103;0.866;2.3765]; +x_lin = []; +t_lin = []; +u_lin = []; +step_size_lin = 0.01; % coarse step size; NOTE: because of forward euler's small stability region, the largest I could get this is 0.05 with a different bsa +num_t_lin = (1/step_size_lin)*21+1; % number of timepoints to evaluate ODE45 +tspan = linspace(0,21,num_t_lin); + + +t_lin_j_start = 0; % current time at end of last cycle +x_t = x0; % initial "guess" +u_i = 50*bsa; + + +dx_t = zeros(8,1); % keep track of delta x +%du_t = 0; % keep track of delta u + +dx_t_arr = []; + +for i=1:num_cycles + u_lin = [u_lin u_i]; + u_star = u_ref(end,i); + du_t = u_i - u_star; + + x_ref_i = x_ref(:,8*(i-1)+1:8*i); + x_lin_j = []; + t_lin_j = []; + + for j=1:length(tspan) + x_star = transpose(x_ref_i(round((j-1)*step_size_lin/step_size_ref)+1,:)); + %round((j-1)*step_size_lin/step_size_ref)+1 + x_t = x_star+dx_t; + x_lin_j = [x_lin_j; transpose(x_t)]; + + [f, dfdx, dfdu, dgdx] = jost_fwd_euler(tspan(j),x_star,u_star,theta,step_size_lin); + dx_t = dfdx*dx_t+dfdu*du_t; + if tspan(j) >= 14 + % u_i and u_ref(i) are both zero after 14 days + du_t = 0; + end + t_lin_j = [t_lin_j; t_lin_j_start+tspan(j)]; + end + t_lin = [t_lin t_lin_j]; + x_lin = [x_lin x_lin_j]; + + % check lower bound + if x_t(8) < 1 + u_i = 0.8*u_i; + elseif x_t(8) > 2 % check upper bound + u_i = 1.2*u_i; + end + t_lin_j_start = t_lin_j_start + 21; % shift the time index to the current cycle +end + +t_lin_flattened = []; +x_lin_flattened = []; +for i=1:num_cycles + x_lin_flattened = [x_lin_flattened; x_lin(:,8*(i-1)+1:8*i)]; + t_lin_flattened = [t_lin_flattened; t_lin(:,i)]; +end + +figure() +hold on +xlabel('Days Elapsed') +ylabel('Number of cells / L blood') +title("Number of Cells per L Blood vs Days Elapsed") +plot(t_lin_flattened,x_lin_flattened(:,4)*1e9) +plot(t_lin_flattened,x_lin_flattened(:,5)*1e9) +plot(t_lin_flattened,x_lin_flattened(:,6)*1e9) +plot(t_lin_flattened,x_lin_flattened(:,7)*1e9) +plot(t_lin_flattened,x_lin_flattened(:,8)*1e9) +legend("x_4 Proliferating cells","x_5 Compartment 1","x_6 Compartment 2","x_7 Compartment 3","x_8 Mature neutrophils") +hold off + + +figure() +hold on +xlabel('Days Elapsed') +ylabel('ANC (Number of cells / L blood)') +title("All Neutrophil Count (ANC) x_8 vs Days Elapsed") +plot(t_lin_flattened,x_lin_flattened(:,8)*1e9) +plot(t_ref_flattened,x_ref_flattened(:,8)*1e9) + +yline(1e9,'-b') +yline(2e9,'-r') + +legend("Linearized","Original","Desired lower bound","Desired upper bound","Location","southwest") +hold off + +clear i j x_lin_j x0_i u_i tspan; + +%% 2.7.1 Trajectory with Noise; Setup + +num_cycles = 7; %34 + +step_size_noisy = step_size_ref; +num_t_noisy = (1/step_size_noisy)*21+1; % number of timepoints to evaluate ODE45 +tspan = linspace(0,21,num_t_noisy); + +var_w = 0.01; % TODO: change this +var_v = 0.774341747; % CHECK if documented + +%% 2.7.2 Trajectory with Noise; Reactive Controller +tic + +u_i = 50*bsa; +x0_i = x0; + +y = []; + +x_noisy_r = []; +t_noisy_r = []; +u_noisy_r = []; + + +for i=1:num_cycles + u_i_all = [transpose(repelem(u_i,(1/step_size_noisy)*14));transpose(repelem(0,(1/step_size_noisy)*(21-14)+1))]; + u_noisy_r = [u_noisy_r u_i_all]; + [t_i,x_i] = ode45(@(t,x)jost_noisy(t,x,u_i,theta,step_size_noisy,var_w),tspan,x0_i); + t_i = t_i + 20*(i-1); % shift the time index to the current cycle + + % make a "reading" + % ground truth (at the end of the cycle) + y_true = x_ref(end,8*i); + % sample a reading + y_i = normrnd(y_true,var_v); + y = [y y_i]; + + % check lower bound + if y_i < 1 + u_i = 0.8*u_i; + elseif y_i > 2 % check upper bound + u_i = 1.2*u_i; + end + x_noisy_r = [x_noisy_r x_i]; + t_noisy_r = [t_noisy_r t_i]; + + x0_i = x_i(end,:); +end + +clear i t_i x_i x0_i u_i tspan; + +% flatten to single nx8 matrix +x_noisy_r_flattened = []; +t_noisy_r_flattened = []; +u_noisy_r_flattened = []; +for i=1:num_cycles + x_noisy_r_flattened = [x_noisy_r_flattened; x_noisy_r(:,8*(i-1)+1:8*i)]; + t_noisy_r_flattened = [t_noisy_r_flattened; t_noisy_r(:,i)]; + u_noisy_r_flattened = [u_noisy_r_flattened; u_noisy_r(:,i)]; +end +clear i; + +figure() +hold on +xlabel('Days Elapsed') +ylabel('ANC (Number of cells / L blood)') +title("All Neutrophil Count (ANC) x_8 vs Days Elapsed") +plot(t_noisy_r_flattened,x_noisy_r_flattened(:,8)*1e9) +plot(t_ref_flattened,x_ref_flattened(:,8)*1e9) +yline(1e9,'-b') +yline(2e9,'-r') +legend("Noisy","Original","Desired lower bound","Desired upper bound","Location","southwest") +hold off + +figure() +hold on +title("Noisy model reactive controller control sequence") +plot(t_noisy_r_flattened,u_noisy_r_flattened) +xlabel("Time elapsed (days)") +ylabel("Input dosage (mg)") +hold off + +toc + +%% 2.7.3 Trajectory with Noise; KF Controller +tic + +% Simu_symlation Length +t_s = step_size_noisy; +t_end = 21*num_cycles; +N = t_end/t_s; +time = [0:t_s:t_end]; + +% Dimension +% n: dimension of state vector x +n = 8; +% m: dimension of observation vector y +m = 1; + +% Initialize motion function +syms x1_sym x2_sym x3_sym x4_sym x5_sym x6_sym x7_sym x8_sym u_sym + +f = [-theta(1) 0 0 0 0 0 0 0; ... + theta(1) -theta(2) 0 0 0 0 0 0; ... + 0 theta(3)*theta(4) -theta(5) 0 0 0 0 0; ... + 0 0 0 -theta(7) 0 0 0 0; ... + 0 0 0 theta(7) -theta(7) 0 0 0; ... + 0 0 0 0 theta(7) -theta(7) 0 0; ... + 0 0 0 0 0 theta(7) -theta(7) 0; ... + 0 0 0 0 0 0 theta(7) -theta(10)]*[x1_sym;x2_sym;x3_sym;x4_sym;x5_sym;x6_sym;x7_sym;x8_sym] + ... + [0.22;0;0;0;0;0;0;0]*u_sym + ... + [0;... + 0;... + 0;... + theta(7)*((theta(6)/x8_sym)^theta(9))*x4_sym-theta(7)*theta(8)*((theta(6)/x8_sym)^theta(9))*x3_sym*x4_sym;... + 0;... + 0;... + 0;... + 0]; + +f = [x1_sym;x2_sym;x3_sym;x4_sym;x5_sym;x6_sym;x7_sym;x8_sym] + t_s * f; + +dfdx = jacobian(f,[x1_sym;x2_sym;x3_sym;x4_sym;x5_sym;x6_sym;x7_sym;x8_sym]); +dfdu = jacobian(f,u_sym); + +% Initialize observation function +g = [x8_sym]; +dgdx = jacobian(g,[x1_sym;x2_sym;x3_sym;x4_sym;x5_sym;x6_sym;x7_sym;x8_sym]); + +% Initialize disturbance xi and covariance Q +xi = zeros([n 1 N]); +Q = zeros([n n N]); +Q_const = var_w * eye(n); +for iter = 1:N + Q(:,:,iter) = Q_const; + xi(:,:,iter) = Q(:,:,iter) * randn([n,1]); +end +clear Q_const + +% Initialize measurement noise n and covariance R +nn = zeros([m 1 N]); +R = zeros([m m N]); +R_const = var_v * eye(m); +for iter = 1:N + R(:,:,iter) = R_const; + nn(:,:,iter) = R(:,:,iter) * randn([m,1]); +end +clear R_const + +% Initialize nominal trajectory and control to track +% nominal control is set as linear for test purposes +A_nominal = zeros([n n N]); +b_nominal = zeros([n N]); + +% convert and reshape for Sam's code +u_nominal = transpose(u_ref_flattened(1:N,1)); + +x_nominal = zeros([n 1 N]); +% Simulate System for the nominals +x_o = x0; +for k = 1:N + % Compute linearized c2d-ed state matrices, we use this to simulate the + % system because we do not know the discrete time nonlinear equation + % for the inverse pendulum problem + if k == 1 + x_tmp = x_o; + else + x_tmp = x_nominal(:,:,k-1); + end + [A_actual,b_actual,~] = c2d_jost(x1_sym,x2_sym,x3_sym,x4_sym,x5_sym,x6_sym,x7_sym,x8_sym,u_sym,dfdx,dfdu,dgdx,x_tmp,u_nominal(:,k),t_s); + A_nominal(:,:,k) = A_actual; + b_nominal(:,k) = b_actual; + % Time step + x_nominal(:,:,k) = double(subs(f,{x1_sym,x2_sym,x3_sym,x4_sym,x5_sym,x6_sym,x7_sym,x8_sym,u_sym},{x_tmp(1),x_tmp(2),x_tmp(3),x_tmp(4),x_tmp(5),x_tmp(6),x_tmp(7),x_tmp(8),u_nominal(:,k)})); +end +clear k x_tmp A_actual b_actual + + +% Initialize Cost Parameters +W = zeros([n n N+1]); +rho = zeros([n 1 N+1]); +lambda = zeros([1 N]); +W_const = 100 * eye(n); +lambda_const = 0.1; +for iter = 1:N+1 + W(:,:,iter) = W_const; + if iter == 1 + rho(:,:,iter) = x_o; + else + rho(:,:,iter) = x_nominal(:,:,iter-1); + end +end +for iter = 1:N + lambda(:,iter) = lambda_const; +end +clear W_const lambda_const + +% Note: Iteration variables does not correspond to the notes due to matlab +% indexing start with 1 instead of 0 + +x_o = x0; +x_hat_o = x0; +sigma_o = eye(n); + +% Simulate System with control +is_controlled = 1; +[x_c,y_c,x_hat_p_c,u_c] = simulate_jost(N,n,m,t_s,x1_sym,x2_sym,x3_sym,x4_sym,x5_sym,x6_sym,x7_sym,x8_sym,u_sym,f,dfdx,dfdu,dgdx,xi,Q,nn,R,W,rho,lambda,x_o,x_hat_o,sigma_o,A_nominal,b_nominal,x_nominal,u_nominal,is_controlled); + + +% Process Result +x1_c_plot = [x_o(1,1);reshape(x_c(1,1,:),[N 1])]; +x2_c_plot = [x_o(2,1);reshape(x_c(2,1,:),[N 1])]; +x3_c_plot = [x_o(3,1);reshape(x_c(3,1,:),[N 1])]; +x4_c_plot = [x_o(4,1);reshape(x_c(4,1,:),[N 1])]; +x5_c_plot = [x_o(5,1);reshape(x_c(5,1,:),[N 1])]; +x6_c_plot = [x_o(6,1);reshape(x_c(6,1,:),[N 1])]; +x7_c_plot = [x_o(7,1);reshape(x_c(7,1,:),[N 1])]; +x8_c_plot = [x_o(8,1);reshape(x_c(8,1,:),[N 1])]; +y1_c_plot = [x_o(1,1);reshape(y_c(1,1,:),[N 1])]; +x1_hat_c_plot = [x_hat_o(1,1);reshape(x_hat_p_c(1,1,:),[N 1])]; +x2_hat_c_plot = [x_hat_o(2,1);reshape(x_hat_p_c(2,1,:),[N 1])]; +x3_hat_c_plot = [x_hat_o(3,1);reshape(x_hat_p_c(3,1,:),[N 1])]; +x4_hat_c_plot = [x_hat_o(4,1);reshape(x_hat_p_c(4,1,:),[N 1])]; +x5_hat_c_plot = [x_hat_o(5,1);reshape(x_hat_p_c(5,1,:),[N 1])]; +x6_hat_c_plot = [x_hat_o(6,1);reshape(x_hat_p_c(6,1,:),[N 1])]; +x7_hat_c_plot = [x_hat_o(7,1);reshape(x_hat_p_c(7,1,:),[N 1])]; +x8_hat_c_plot = [x_hat_o(8,1);reshape(x_hat_p_c(8,1,:),[N 1])]; + +figure() +plot(t_noisy_r_flattened(1:N),x_noisy_r_flattened(1:N,8)); +hold on +rho8_plot = reshape(rho(8,1,:),[N+1 1]); +plot(time,x8_c_plot) +hold on +plot(time,rho8_plot) +legend('reactive','KF','nominal') + + +figure() +hold on +title("Noisy model KF controller control sequence") +plot(time(1:N),u_c) +xlabel("Time elapsed (days)") +ylabel("Input dosage (mg)") +hold off + +figure() +hold on +title("Noisy model control sequence comparison") +plot(t_noisy_r_flattened,u_noisy_r_flattened*1e9) +plot(time(1:N),u_c) +legend('reactive control sequence','KF control sequence') +xlabel("Time elapsed (days)") +ylabel("Input dosage (mg)") +hold off + +toc diff --git a/simulate_invpend.m b/simulate_invpend.m index 99d0b24..44d5472 100644 --- a/simulate_invpend.m +++ b/simulate_invpend.m @@ -49,20 +49,7 @@ else x_hat_km1 = reshape(x_hat_p(:,:,k-1),[n 1]); end -% for j = k:N -% if j == k -% x_tmp = x_hat_km1; -% else -% x_tmp = x_nominal(:,:,j-1); -% end -% [A_new,b_new,~] = c2d_invpend(x1_sym,x2_sym,x3_sym,x4_sym,u_sym,dfdx,dfdu,dgdx,x_tmp,u_nominal(:,j),t_s); -% A_nominal_new(:,:,j) = A_new; -% b_nominal_new(:,j) = b_new; -% x_nominal(:,:,j) = A_new * x_tmp + b_new * u_nominal(:,j); -% end -% [K_tilda,p_tilda,mu] = solve_riccati(N,n,W,rho,lambda,A_nominal,b_nominal); - -% 2 + delta_x_hat_km1 = x_hat_km1 - rho(:,:,k); delta_u = calc_control(A_nominal,b_nominal,K_tilda,p_tilda,mu,delta_x_hat_km1,k); u(:,k) = u_nominal(:,k) + delta_u; @@ -71,12 +58,6 @@ % Compute linearized c2d-ed state matrices, we use this to simulate the % system because we do not know the discrete time nonlinear equation % for the inverse pendulum problem -% if k == 1 -% x_tmp = x_o; -% else -% x_tmp = x(:,:,k-1); -% end -% [A_actual,b_actual,C_actual] = c2d_invpend(x1_sym,x2_sym,x3_sym,x4_sym,u_sym,dfdx,dfdu,dgdx,x_tmp,u(:,k),t_s); % Kalman Filter Prediction if k == 1 diff --git a/simulate_jost.m b/simulate_jost.m new file mode 100644 index 0000000..825dc37 --- /dev/null +++ b/simulate_jost.m @@ -0,0 +1,95 @@ +function [x,y,x_hat_p,u] = simulate_jost(N,n,m,t_s,x1_sym,x2_sym,x3_sym,x4_sym,x5_sym,x6_sym,x7_sym,x8_sym,u_sym,f,dfdx,dfdu,dgdx,xi,Q,nn,R,W,rho,lambda,x_o,x_hat_o,sigma_o,A_nominal,b_nominal,x_nominal,u_nominal,is_controlled) + +% Initialize state vector x +x = zeros([n 1 N]); + +% Initialize control u +% Note: in our ase control is scalar +u = zeros([1 N]); + +% Initialize observation y +y = zeros([m 1 N]); + +% Initialize predicted/updated state x_hat +x_hat_a = zeros([n 1 N]); +x_hat_p = zeros([n 1 N]); + +% Initialize predicted/updated covariance sigma +sigma_a = zeros([n n N]); +sigma_p = zeros([n n N]); + +% Initilaize Kalman Gain kg +kg = zeros([n m N]); + +% Compute the solution to Riccati Equation +% Solve for linearized state matrices for the target trajectory +% A = zeros([n n N]); +% b = zeros([n 1 N]); +% [A_const,b_const] = c2d_invpend(x1_sym,x2_sym,x3_sym,x4_sym,u_sym,dfdx,dfdu,dgdx,x_hat_o,0,t_s); +% for j = 1:N +% A(:,:,j) = A_const; +% b(:,:,j) = b_const; +% end +% [K_tilda,p_tilda,mu] = solve_riccati(N,n,W,rho,lambda,A,b); + +[K_tilda,p_tilda,mu] = solve_riccati(N,n,W,rho,lambda,A_nominal,b_nominal); +p_tilda = zeros([n 1 N+1]); + + +% Simulate System +% x_nominal = zeros([n 1 N]); +for k = 1:N + % Calculate for control + if is_controlled == 0 + u(:,k) = 0; + else + % Update the nominal trajectory based on current estiamte + if k == 1 + x_hat_km1 = x_hat_o; % Choose the predicted state to feed into algorithm + else + x_hat_km1 = reshape(x_hat_p(:,:,k-1),[n 1]); + end + + delta_x_hat_km1 = x_hat_km1 - rho(:,:,k); + delta_u = calc_control(A_nominal,b_nominal,K_tilda,p_tilda,mu,delta_x_hat_km1,k); + u(:,k) = u_nominal(:,k) + delta_u; + end + + % Compute linearized c2d-ed state matrices, we use this to simulate the + % system because we do not know the discrete time nonlinear equation + % for the inverse pendulum problem + + % Kalman Filter Prediction + if k == 1 + x_tmp = x_hat_o; + sigma_tmp = sigma_o; + else + x_tmp = x_hat_p(:,:,k-1); + sigma_tmp = sigma_p(:,:,k-1); + end + [A_ekf,b_ekf,C_ekf] = c2d_jost(x1_sym,x2_sym,x3_sym,x4_sym,x5_sym,x6_sym,x7_sym,x8_sym,u_sym,dfdx,dfdu,dgdx,x_tmp,u(:,k),t_s); + % x_hat_a(:,:,k) = A_actual * x_tmp + b_actual * u(:,k); + x_hat_a(:,:,k) = double(subs(f,{x1_sym,x2_sym,x3_sym,x4_sym,x5_sym,x6_sym,x7_sym,x8_sym,u_sym},{x_tmp(1),x_tmp(2),x_tmp(3),x_tmp(4),x_tmp(5),x_tmp(6),x_tmp(7),x_tmp(8),u(:,k)})); + sigma_a(:,:,k) = A_ekf * sigma_tmp * A_ekf' + Q(:,:,k); + kg(:,:,k) = (sigma_a(:,:,k)*C_ekf') * inv(C_ekf*sigma_a(:,:,k)*C_ekf'+R(:,:,k)); + % Time step + if k == 1 + x_tmp = x_o; + % x_nom_tmp = x_o; + else + x_tmp = x(:,:,k-1); + % x_nom_tmp = x_nominal(:,:,k-1); + end + % x(:,:,k) = A_actual * x_tmp + b_actual * u(:,k) + xi(:,:,k); + x(:,:,k) = double(subs(f,{x1_sym,x2_sym,x3_sym,x4_sym,x5_sym,x6_sym,x7_sym,x8_sym,u_sym},{x_tmp(1),x_tmp(2),x_tmp(3),x_tmp(4),x_tmp(5),x_tmp(6),x_tmp(7),x_tmp(8),u(:,k)})) + xi(:,:,k); + % Observer + % y(:,:,k) = C_actual * x(:,:,k) + nn(:,:,k); + y(:,:,k) = double(dgdx) * x(:,:,k) + nn(:,:,k); + % Kalman Filter Update + x_hat_p(:,:,k) = x_hat_a(:,:,k) + kg(:,:,k) * (y(:,:,k) - double(dgdx) * x_hat_a(:,:,k)); + sigma_p(:,:,k) = (eye(n) - kg(:,:,k) * C_ekf) * sigma_a(:,:,k); +end +clear x_tmp x_nom_tmp sigma_tmp x_hat_k_km1 sigma_kp1_k + + +end \ No newline at end of file diff --git a/simulate_system.m b/simulate_system.m index 70c193a..4311881 100644 --- a/simulate_system.m +++ b/simulate_system.m @@ -1,5 +1,24 @@ function [x,y,x_hat_p,u] = simulate_system(N,n,m,A,b,C,xi,Q,nn,R,W,rho,lambda,x_o,is_controlled) +% +% N = number of steps to simulate +% n = measurement noise +% A = state matrix (ok) +% b = control matrix (ok) +% C = observation matrix (ok) +% xi = system noise/disturbance ( x(k+1) = ... + xi ) +% Q = state noise covariance matrix (requires variance of noise) +% nn = measurement noise +% R = state covariance matrix +% W = (matrix related to cost function) +% rho = (cost function) +% lambda = (cost function) +% x_o = state at time zero +% is_controlled = is the system controlled? 1 + + + + % Initialize state vector x x = zeros([n 1 N]); %x_o = [100;0;-10];