Too Many Input Arguments

Question

Ivan Dwi Putra on 3 Nov 2020

0
Link

Direct link to this question

https://ch.mathworks.com/matlabcentral/answers/635304-too-many-input-arguments

Commented: Ivan Dwi Putra on 4 Nov 2020

I implement vehicle platooning to train model

This is my model stored in function

function [PositionN, VelocityN, TorqueN] = traindiscretemodel(u,Tim_step,Position,Velocity,Torque,Mass,Ca_0,Ca_1,Ca_2,Tao)
% Train Model
   PositionN = Position + Velocity*Tim_step;
   VelocityN = (u - 1/Mass*(Ca_0 + Ca_1*Velocity + Ca_2*Velocity^2));
   TorqueN = Torque - 1/Tao*Torque*Tim_step + 1/Tao*u*Tim_step;
    
end

and this is parameter value i use saved in .MAT

% Parameter Massa
Mass = [8095;8500;8457;8650;8700;8500;8300;];
% Parameter Gaya
u = [205*10^3;302*10^3;205*10^3;302*10^3;205*10^3;302*10^3;302*10^3;];
% Parameter Resistansi
Ca_0 = [0.01176;0.01176;0.01176;0.01176;0.01176;0.01176;0.01176;];
Ca_1 = [0.00077616;0.00077616;0.00077616;0.00077616;0.00077616;0.00077616;0.00077616;];
Ca_2 = [4.48;4.48;4.48;4.48;4.48;4.48;4.48;];
Ca = [0.987142335714838;1.14982586117376;1.16679864955151;1.11574703097155;1.13154802611567;1.12862649362498;1.05844540390683;];
    
% Desired Speed
v_0 = 300;
% Desired Train Following Headway Time
hstar = 120;
Num_step = 100;
Num_veh = 7;
AccMax = 6;
AccMin = -6;
Eta = 0.9600;
f = 0.0100;
g = 9.800;
i = 7;
Tim_step = 0.100;
Time_sim = 10;
R = [0.303571167857419;0.384912930586878;0.393399324775755;0.367873515485777;0.375774013057833;0.374313246812492;0.339222701953417;];
Tao = [0.510713503572257;0.754738791760633;0.780197974327265;0.703620546457332;0.727322039173500;0.722939740437475;0.617668105860250;];
Torquebound = [-1965.07627392709,1965.07627392709;-4448.46112597519,4448.46112597519;-4755.19773617931,4755.19773617931;-3859.76376866931,3859.76376866931;-4128.20664237638,4128.20664237638;-4077.98483605068,4077.98483605068;-2951.71882061833,2951.71882061833;];
cd('G:\Ivan\Semester 10\Tugas Akhir II\matlab\DMPC Train Model');
save valueparameters.mat

My Parameter_Initial

clc;clear;close all;
%% Parameter Initialation
Num_veh  = 7;                            % The number of vehicles in a platoon; 
Tim_step = 0.1;                          % Time step;
Time_sim = 10;                           % Time length for simulation
Num_step = Time_sim/Tim_step;            % Simulation setps
Mass =  zeros(Num_veh,1) + 1000*rand(Num_veh,1);  % Vehilce mass
Tao  = 0.5 +(Mass - 1000)/1000 * 0.3;   % Time lag
f    = 0.01;                            % rolling friction
Eta  = 0.96;                             % Efficency
g    = 9.8;                             % gravitity 
Ca   = 0.98 + (Mass - 1000)/1000 * 0.2;                  % ·ç×è 
Ca_0 = 0.98 + (Mass - 1000)/1000 * 0.2 /83.5 ;
Ca_1 = 0.98 + (Mass - 1000)/1000 / 1271856556;
Ca_2 = 0.98 + (Mass - 1000)/1000 * 0.2 * 4;
% Ca   = 1/2*0.4*2*1.23;                  % ·ç×è 
R    = 0.3 +(Mass - 2000)/2000 * 0.1 ;   % °ë¾¶
%% Acceleration bounds -6,6
AccMax = 6; AccMin = -6;
Torquebound = zeros(6,2);               %   [low up]
for i = 1:Num_veh
    Torquebound(i,1) = Mass(i)*AccMin*R(i)/Eta;
    Torquebound(i,2) = Mass(i)*AccMax*R(i)/Eta;
end

My Cost Function

function Cost = Fungsicos(Np, Tim_step, X0, u, Vehicle_Type, Q, Xdes, R, F, Xa, G, Xnba)
% Cost function
    Pp = zeros(Np,1);     % Predictive Position
    Vp = zeros(Np,1);     % Predictive Velocity
    Tp = zeros(Np,1);     % Predictive Torque
    
    Mass = Vehicle_Type(1);Radius = Vehicle_Type(2); g = Vehicle_Type(3);f = Vehicle_Type(4);
    Eta = Vehicle_Type(5);Ca = Vehicle_Type(6);Tao = Vehicle_Type(7);Ca_0 = Vehicle_Type(8);
    Ca_1 = Vehicle_Type(9);Ca_2 = Vehicle_Type(10);
    
    [Pp(1),Vp(1),Tp(1)] = traindiscretemodel(u(1),Tim_step,X0(1),X0(2),X0(3),Mass,Ca_0,Ca_1,Ca_2,Tao,Radius,g,f,Eta,Ca);
    for i = 1:Np-1
        [Pp(i+1),Vp(i+1),Tp(i+1)] = traindiscretemodel(u(i+1),Tim_step,Pp(i),Vp(i),Tp(i),Mass,Ca_0,Ca_1,Ca_2,Tao,Radius,g,f,Eta,Ca);
    end
    
    Xp = [Pp,Vp];      % Predictive State
    
    Udes = Radius/Eta*(Ca*Vp.^2 + Mass*g*f);
    U0 = Radius/Eta*(Ca*X0(2).^2 + Mass*g*f);
    
    Cost = (X0(1:2)-Xdes(1,:))*Q*(X0(1:2)-Xdes(1,:))' + ...
                (u(1)-U0)*R*(u(1)-U0) + (X0(1:2)-Xa(1,:))*F*(X0(1:2)-Xa(1,:))'+ ...
                (X0(1:2)-Xnba(1,:))*G*(X0(1:2)-Xnba(1,:))';                               % 第一步的优化值
    for i = 1:Np-1        %% 注意范数的定义问题， X'Q'QX
        Cost = Cost + (Xp(i,:)-Xdes(i+1,:))*Q*(Xp(i,:)-Xdes(i+1,:))' + ...
                (u(i+1)-Udes(i))*R*(u(i+1)-Udes(i)) + (Xp(i,:)-Xa(i+1,:))*F*(Xp(i,:)-Xa(i+1,:))'+ ...
                (Xp(i,:)-Xnba(i+1,:))*G*(Xp(i,:)-Xnba(i+1,:))';               
    end
   
end

My non linear constrain

function [C, Ceq] = kendalanonlinear(Np, Tim_step, X0, u, Vehicle_Type,x0,v0,T0)
%UNTITLED5 Summary of this function goes here
%   Detailed explanation goes here
    Pp = zeros(Np,1);     % Predictive Position
    Vp = zeros(Np,1);     % Predictive Velocity
    Tp = zeros(Np,1);     % Predictive Torque
    
    Mass = Vehicle_Type(1);Radius = Vehicle_Type(2); g = Vehicle_Type(3);f = Vehicle_Type(4);
    Eta = Vehicle_Type(5);Ca = Vehicle_Type(6);Tao = Vehicle_Type(7);Ca_0 = Vehicle_Type(8);
    Ca_1 = Vehicle_Type(9);Ca_2 = Vehicle_Type(10);
    
    [Pp(1),Vp(1),Tp(1)] = traindiscretemodel(u(1),Tim_step,X0(1),X0(2),X0(3),Mass,Radius,g,f,Eta,Ca,Tao,Ca_0,Ca_1,Ca_2);
    for i = 1:Np-1
        [Pp(i+1),Vp(i+1),Tp(i+1)] = traindiscretemodel(u(i+1),Tim_step,Pp(i),Vp(i),Tp(i),Mass,Radius,g,f,Eta,Ca,Tao,Ca_0,Ca_1,Ca_2);
    end
    
    Xp = [Pp,Vp,Tp];      % Predictive State
    
    C = [];
    Ceq = [Pp(Np) - x0;Vp(Np)-v0; Tp(Np) - T0];%Radius*(Ca*v0^2 + Mass*g*f)/Eta];   
end

This is the figure plot function

%% »æÍ¼
close all
figure;
T = 6
t = (1:Time_sim/Tim_step)*Tim_step;
 plot(t,v0,'m--','linewidth',2);
hold on; plot(t, Velocity(:,1),'r','linewidth',2);
plot(t, Velocity(:,2),'b','linewidth',2);
plot(t, Velocity(:,3),'k','linewidth',2);
plot(t, Velocity(:,4),'g','linewidth',2);
plot(t, Velocity(:,5),'m','linewidth',2);
plot(t, Velocity(:,6),'r--','linewidth',2);
plot(t, Velocity(:,7),'b--','linewidth',2);
h = legend('0','1','2','3','4','5','6','7','Location','SouthEast');
set(h,'box','off'); box off;
xlabel('Time (s)');ylabel('Speed (m/s)');
axis([0 T 19 floor(max(max(Velocity))+1)])
set(gcf,'Position',[250 150 300 350]);
figure;
plot(t, Torque(:,1),'r','linewidth',2);hold on;
plot(t, Torque(:,2),'b','linewidth',2);hold on;
plot(t, Torque(:,3),'k','linewidth',2);hold on;
plot(t, Torque(:,4),'g','linewidth',2);hold on;
plot(t, Torque(:,5),'m','linewidth',2);hold on;
plot(t, Torque(:,6),'r--','linewidth',2);hold on;
plot(t, Torque(:,7),'b--','linewidth',2);hold on;
h = legend('1','2','3','4','5','6','7');
set(h,'box','off'); box off;
xlabel('Time (s)');ylabel('Torque (N)');
xlim([0 T])
set(gcf,'Position',[250 150 300 350]);
figure;
plot(t, (Eta*Torque(:,1)/R(1) - Ca(1)*Velocity(:,1).^2 - Mass(1)*g*f)/Mass(1),'r','linewidth',2);hold on;
plot(t, (Eta*Torque(:,2)/R(2) - Ca(2)*Velocity(:,2).^2 - Mass(2)*g*f)/Mass(2),'b','linewidth',2);hold on;
plot(t, (Eta*Torque(:,3)/R(3) - Ca(3)*Velocity(:,3).^2 - Mass(3)*g*f)/Mass(3),'k','linewidth',2);hold on;
plot(t, (Eta*Torque(:,4)/R(4) - Ca(4)*Velocity(:,4).^2 - Mass(4)*g*f)/Mass(4),'g','linewidth',2);hold on;
plot(t, (Eta*Torque(:,5)/R(5) - Ca(5)*Velocity(:,5).^2 - Mass(5)*g*f)/Mass(5),'m','linewidth',2);hold on;
plot(t, (Eta*Torque(:,6)/R(6) - Ca(6)*Velocity(:,6).^2 - Mass(6)*g*f)/Mass(6),'r--','linewidth',2);hold on;
plot(t, (Eta*Torque(:,7)/R(7) - Ca(7)*Velocity(:,7).^2 - Mass(7)*g*f)/Mass(7),'b--','linewidth',2);hold on;
h = legend('1','2','3','4','5','6','7','Location','NorthEast');
set(h,'box','off'); box off;
xlabel('Time (s)');ylabel('Acceleration (m/s^2)');
xlim([0 T])
set(gcf,'Position',[250 150 300 350]);
figure;
plot(t, x0 - Postion(:,1) - d,'r','linewidth',2);hold on;
plot(t, Postion(:,1) - d - Postion(:,2),'b','linewidth',2);hold on;
plot(t, Postion(:,2) - d - Postion(:,3),'k','linewidth',2);hold on;
plot(t, Postion(:,3) - d - Postion(:,4),'g','linewidth',2);hold on;
plot(t, Postion(:,4) - d - Postion(:,5),'m','linewidth',2);hold on;
plot(t, Postion(:,5) - d - Postion(:,6),'r--','linewidth',2);hold on;
plot(t, Postion(:,6) - d - Postion(:,7),'b--','linewidth',2);hold on;
plot(t, Postion(:,7) - d - Postion(:,8),'k--','linewidth',2);hold on;
h = legend('1','2','3','4','5','6','7');
set(h,'box','off'); box off;
xlabel('Time (s)');ylabel('Spacing error (m)');
xlim([0 T])
set(gcf,'Position',[250 150 300 350]);
figure;
plot(t, Postion(:,1)- (x0- d),'r','linewidth',2);hold on;
plot(t, Postion(:,2)-(x0- 2*d) ,'b','linewidth',2);hold on;
plot(t, Postion(:,3)-(x0- 3*d),'k','linewidth',2);hold on;
plot(t, Postion(:,4)-(x0- 4*d),'g','linewidth',2);hold on;
plot(t, Postion(:,5)-(x0- 5*d),'m','linewidth',2);hold on;
plot(t, Postion(:,6)-(x0- 6*d),'r--','linewidth',2);hold on;
plot(t, Postion(:,7)-(x0- 7*d),'b--','linewidth',2);hold on;
h = legend('1','2','3','4','5','6','7');
set(h,'box','off'); box off;
xlabel('Time (s)');ylabel('Spacing error (m)');
xlim([0 T])
set(gcf,'Position',[250 150 300 350]);

Every Function used in main program

This is the main program

%% DMPC for platoons with PF topology
clc;clear;close all;
load valueparameters.mat
%% Initial Virables 
Postion  = zeros(Num_step,Num_veh);     % postion of each vehicle;
Velocity = zeros(Num_step,Num_veh);     % velocity of each vehicle;
Torque   = zeros(Num_step,Num_veh);     % Braking or Tracking Torque of each vehicle;
U        = zeros(Num_step,Num_veh);     % Desired Braking or Tracking Torque of each vehicle;
Cost    = zeros(Num_step,Num_veh);      % Cost function
Exitflg = zeros(Num_step,Num_veh);      % Stop flag - solvers
% Leading vehicle
d  = 20;                                % Desired spacing
a0 = zeros(Num_step,1); 
v0 = zeros(Num_step,1); 
x0 = zeros(Num_step,1);
% Transient process of leader, which is given in advance
v0(1) = 20; a0(1/Tim_step+1:2/Tim_step) = 2; 
for i = 2:Num_step
    v0(i) = v0(i-1)+a0(i)*Tim_step; 
    x0(i) = x0(i-1)+v0(i)*Tim_step;    
end
 
% Zero initial error for the followers
for i = 1:Num_veh
    Postion(1,i)  = x0(1)-i*d;
    Velocity(1,i) = 20;             
    Torque(1,i)   = (Mass(i)*g*f + Ca(i)*Velocity(1,i)^2)*R(i)/Eta;
end
%% MPC weighted matrix in the cost function
% PF topology --> Fi > Gi+1
% Q1 : leader weighted matrix for state; 
% R1 --> leader weighted matrix for control input
% Fi --> penalty for deviation of its own assumed trajectory 
% Gi --> penalty for deviation of its neighbors' assumed trajectory
 F1 = 10*eye(2); G1 = 0;       Q1 = 10*eye(2);R1 = 1;       
 F2 = 10*eye(2); G2 = 5*eye(2);Q2 = 0*eye(2); R2 = 1;       
 F3 = 10*eye(2); G3 = 5*eye(2);Q3 = 0*eye(2); R3 = 1;      
 F4 = 10*eye(2); G4 = 5*eye(2);Q4 = 0*eye(2); R4 = 1;       
 F5 = 10*eye(2); G5 = 5*eye(2);Q5 = 0*eye(2); R5 = 1;       
 F6 = 10*eye(2); G6 = 5*eye(2);Q6 = 0*eye(2); R6 = 1;      
 F7 = 10*eye(2); G7 = 5*eye(2);Q7 = 0*eye(2); R7 = 1;  
 
 
% Distributed MPC assumed state
Np = 20;                      % Predictive horizon
Pa = zeros(Np,Num_veh);       % Assumed postion of each vehicle;
Va = zeros(Np,Num_veh);       % Assumed velocity of each vehicle;
ua = zeros(Np,Num_veh);       % Assumed Braking or Tracking Torque input of each vehicle;
Pa_next = zeros(Np+1,Num_veh);  % 1(0): Assumed postion of each vehicle at the newt time step;
Va_next = zeros(Np+1,Num_veh);  % Assumed velocity of each vehicle at the newt time step;
ua_next = zeros(Np+1,Num_veh);  % Assumed Braking or Tracking Torque of each vehicle at the newt time step;
% Initialzie the assumed state for the first computation: constant speed
for i = 1:Num_veh
    ua(:,i) = Torque(1,i);
    Pa(1,i) = Postion(1,i);                % The first point should be interpreted as k = 0 (current state)
    Va(1,i) = Velocity(1,i);
    Ta(1,i) = Torque(1,i);
    for j = 1:Np
        [Pa(j+1,i),Va(j+1,i),Ta(j+1,i)] = traindiscretemodel(ua(j,i),Tim_step,Pa(j,i),Va(j,i),Ta(j,i),Mass(i),Ca_0(i),Ca_1(i),Ca_2(i),Tao(i));
    end    
end
tol_opt = 1e-5;
options = optimset('Display','off','TolFun', tol_opt, 'MaxIter', 2000,...
                'LargeScale', 'off', 'RelLineSrchBnd', [], 'RelLineSrchBndDuration', 1);
 
 %%  For debugging
 % Terminal state
 Xend = zeros(Num_step,Num_veh); Vend = zeros(Num_step,Num_veh);
 
 tol_opt = 1e-5;
options = optimset('Display','off','TolFun', tol_opt, 'MaxIter', 2000,...
                'LargeScale', 'off', 'RelLineSrchBnd', [], 'RelLineSrchBndDuration', 1);
 
 %%  For debugging
 % Terminal state
 Xend = zeros(Num_step,Num_veh); Vend = zeros(Num_step,Num_veh);
 
%% Iterative Simulation  
for i = 2:Num_step - Np
    
    fprintf('\n Steps i= %d\n',i)
    
    % Solve optimization problem
    tic
    %% Vehicle one
    Vehicle_Type = [Mass(1),Ca_0(1),Ca_1(1),Ca_2(1),Tao(1),R(1),g,f,Eta,Ca(1)];             % the vehicle parameters ： Mass,R,g,f,Eta,Ca(i),Tao, 
    X0 = [Postion(i-1,1),Velocity(i-1,1),Torque(i-1,1)];            % the vehicle variable in the last time step
    Pd = x0(i-1:i+Np-1) - d;  Vd = v0(i-1:i+Np-1);                  % Np+1 points in total: i-1 last state， i to be optimized
    Xdes = [Pd,Vd];                                                 % Desired state of the first vehicle
    Xa = [Pa(:,1),Va(:,1)];                                         % Assumed state, which is passed to the next vehicle
    Xnba = zeros(Np+1,2);                                           % 1：last state
   
    u0 = ua(:,1);                                                            % initial searching point    
    A = [];b = []; Aeq = []; beq = [];                                       % no linear constraints
    lb = Torquebound(1,1)*ones(Np,1); ub = Torquebound(1,2)*ones(Np,1);      % upper and lower bound for input              
    Pnp = Pd(end,1); Vnp = Vd(end,1);                                        % Terminal constraints
    Xend(i,1) = Pnp; Vend(i,1) = Vnp; Tnp = (Ca(1)*Vnp.^2 + Mass(1)*g*f)/Eta*R(1);
    % MPC - subproblem in vehicle 1
    [u, Cost(i,1), Exitflg(i,1), output] = fmincon(@(u) Fungsicos( Np, Tim_step, X0 ,u, Vehicle_Type,Q1,Xdes,R1,F1,Xa,G1,Xnba), ...
        u0, A, b, Aeq, beq, lb, ub, @(u) kendalanonlinear(Np, Tim_step, X0, u, Vehicle_Type,Pnp,Vnp,Tnp),options); 
    
    % state involves one step
    U(i,1) = u(1);
    [Postion(i,1),Velocity(i,1),Torque(i,1)] = traindiscretemodel(U(i,1),Tim_step,Postion(i-1,1),Velocity(i-1,1),Torque(i-1,1),Mass(1),R(1),g,f,Eta,Ca(1),Tao(1),Ca_0(1),Ca_1(1),Ca_2(1));
    
    % Update assumed state
    Temp = zeros(Np+1,3);
    Temp(1,:) = [Postion(i,1),Velocity(i,1),Torque(i,1)];   
    ua(1:Np-1,1) = u(2:Np);
    for j = 1:Np-1
        [Temp(j+1,1),Temp(j+1,2),Temp(j+1,3)] = traindiscretemodel(ua(j,1),Tim_step,Temp(j,1),Temp(j,2),Temp(j,3),Mass(1),R(1),g,f,Eta,Ca(1),Tao(1),Ca_0(1),Ca_1(1),Ca_2(1));
    end    
    ua(Np,1) = (Ca(1)*Temp(Np,2).^2 + Mass(1)*g*f)/Eta*R(1);
    [Temp(Np+1,1),Temp(Np+1,2),Temp(Np+1,3)] = traindiscretemodel(ua(Np,1),Tim_step,Temp(Np,1),Temp(Np,2),Temp(Np,3),Mass(1),R(1),g,f,Eta,Ca(1),Tao(1),Ca_0(1),Ca_1(1),Ca_2(1));
    Pa_next(:,1) = Temp(:,1);
    Va_next(:,1) = Temp(:,2);
    toc
    
  %% Vehicle two
    tic
    Vehicle_Type = [Mass(2),Ca_0(2),Ca_1(2),Ca_2(2),Tao(2),R(2),g,f,Eta,Ca(2)];                 % the vehicle parameters ： Mass,R,g,f,Eta,Ca(i),Tao, 
    X0 = [Postion(i-1,2),Velocity(i-1,2),Torque(i-1,2)];                % the vehicle variable in the last time
    Pd = zeros(Np+1,1);  Vd = zeros(Np+1,1);                      
    Xdes = [Pd,Vd];  
    Xa = [Pa(:,2),Va(:,2)];                                         
    Xnfa = [Pa(:,1) - d, Va(:,1)];                                         
   
    u0 = ua(:,2);   
    A = [];b = []; Aeq = []; beq = [];                                   
    lb = Torquebound(2,1)*ones(Np,1); ub = Torquebound(2,2)*ones(Np,1);         
    Pnp = Xnfa(end,1); Vnp = Xnfa(end,2);  
    Xend(i,2) = Pnp; Vend(i,2) = Vnp; Tnp = (Ca(2)*Vnp.^2 + Mass(2)*g*f)/Eta*R(2);
    % MPC - subporblem for vehicle 2
    [u, Cost(i,2), Exitflg(i,2), output] = fmincon(@(u) Fungsicos( Np, Tim_step, X0 ,u, Vehicle_Type,Q2,Xdes,R2,F2,Xa,G2,Xnfa), ...
        u0, A, b, Aeq, beq, lb, ub, @(u) kendalanonlinear(Np, Tim_step, X0, u, Vehicle_Type,Pnp,Vnp,Tnp),options); 
    
    % state involves one step
    U(i,2) = u(1);
    [Postion(i,2),Velocity(i,2),Torque(i,2)] = traindiscretemodel(U(i,2),Tim_step,Postion(i-1,2),Velocity(i-1,2),Torque(i-1,2),Mass(2),R(2),g,f,Eta,Ca(2),Tao(2),Ca_0(2),Ca_1(2),Ca_2(2));
    
    % Update assumed state
    Temp = zeros(Np+1,3);
    Temp(1,:) = [Postion(i,2),Velocity(i,2),Torque(i,2)]; 
    ua(1:Np-1,2) = u(2:Np);
    for j = 1:Np-1
        [Temp(j+1,1),Temp(j+1,2),Temp(j+1,3)] = traindiscretemodel(ua(j,2),Tim_step,Temp(j,1),Temp(j,2),Temp(j,3),Mass(2),R(2),g,f,Eta,Ca(2),Tao(2));
    end    
    
    ua(Np,2) = (Ca(2)*Temp(Np,2).^2 + Mass(2)*g*f)/Eta*R(2);
    [Temp(Np+1,1),Temp(Np+1,2),Temp(Np+1,3)] = traindiscretemodel(ua(Np,2),Tim_step,Temp(Np,1),Temp(Np,2),Temp(Np,3),Mass(2),R(2),g,f,Eta,Ca(2),Tao(2));
    Pa_next(:,2) = Temp(:,1);
    Va_next(:,2) = Temp(:,2);
    toc
    
    
    
    %% vehicle three
    tic
    Vehicle_Type = [Mass(3),Ca_0(3),Ca_1(3),Ca_2(3),Tao(3),R(3),g,f,Eta,Ca(3)];                 % the vehicle parameters ： Mass,R,g,f,Eta,Ca(i),Tao, 
    X0 = [Postion(i-1,3),Velocity(i-1,3),Torque(i-1,3)];                % the vehicle variable in the last time
    Pd = zeros(Np+1,1);  Vd = zeros(Np+1,1);                     
    Xdes = [Pd,Vd]; 
    Xa = [Pa(:,3),Va(:,3)];                                           
    Xnfa = [Pa(:,2) - d, Va(:,2)];                                            
   
    u0 = ua(:,3);  
    A = [];b = []; Aeq = []; beq = [];                                    
    lb = Torquebound(3,1)*ones(Np,1); ub = Torquebound(3,2)*ones(Np,1);             
    Pnp = Xnfa(end,1); Vnp = Xnfa(end,2);  
    Xend(i,3) = Pnp; Vend(i,3) = Vnp; Tnp = (Ca(3)*Vnp.^2 + Mass(3)*g*f)/Eta*R(3);
    % MPC-subproblem
    [u, Cost(i,3), Exitflg(i,3), output] = fmincon(@(u) Fungsicos( Np, Tim_step, X0 ,u, Vehicle_Type,Q3,Xdes,R3,F3,Xa,G3,Xnfa), ...
        u0, A, b, Aeq, beq, lb, ub, @(u) kendalanonlinear(Np, Tim_step, X0, u, Vehicle_Type,Pnp,Vnp,Tnp),options); 
    
    % state involves one step
    U(i,3) = u(1);
    [Postion(i,3),Velocity(i,3),Torque(i,3)] = traindiscretemodel(U(i,3),Tim_step,Postion(i-1,3),Velocity(i-1,3),Torque(i-1,3),Mass(3),R(3),g,f,Eta,Ca(3),Tao(3),Ca_0(3),Ca_1(3),Ca_2(3));
    
    % update assumed state
    Temp = zeros(Np+1,3);
    Temp(1,:) = [Postion(i,3),Velocity(i,3),Torque(i,3)];    
    ua(1:Np-1,3) = u(2:Np);
    for j = 1:Np-1
        [Temp(j+1,1),Temp(j+1,2),Temp(j+1,3)] = traindiscretemodel(ua(j,3),Tim_step,Temp(j,1),Temp(j,2),Temp(j,3),Mass(3),R(3),g,f,Eta,Ca(3),Tao(3),Ca_0(3),Ca_1(3),Ca_2(3));
    end   
    
    ua(Np,3) = (Ca(3)*Temp(Np,2).^2 + Mass(3)*g*f)/Eta*R(3);
    [Temp(Np+1,1),Temp(Np+1,2),Temp(Np+1,3)] = traindiscretemodel(ua(Np,3),Tim_step,Temp(Np,1),Temp(Np,2),Temp(Np,3),Mass(3),R(3),g,f,Eta,Ca(3),Tao(3),Ca_0(3),Ca_1(3),Ca_2(3));
    Pa_next(:,3) = Temp(:,1);
    Va_next(:,3) = Temp(:,2);
    toc
    
    
    
    %% vehicle four
    tic
    Vehicle_Type = [Mass(4),Ca_0(4),Ca_1(4),Ca_2(4),Tao(4),R(4),g,f,Eta,Ca(4)];                 % the vehicle parameters ： Mass,R,g,f,Eta,Ca(i),Tao, 
    X0 = [Postion(i-1,4),Velocity(i-1,4),Torque(i-1,4)];                
    Pd = zeros(Np+1,1);  Vd = zeros(Np+1,1);                   
    Xdes = [Pd,Vd];                                
    Xa = [Pa(:,4),Va(:,4)];                                            
    Xnfa = [Pa(:,3) - d, Va(:,3)];                                        
    
    u0 = ua(:,4); 
    A = [];b = []; Aeq = []; beq = [];                                     
    lb = Torquebound(4,1)*ones(Np,1); ub = Torquebound(4,2)*ones(Np,1);              
    Pnp = Xnfa(end,1); Vnp = Xnfa(end,2);   
    Xend(i,4) = Pnp; Vend(i,4) = Vnp; Tnp = (Ca(4)*Vnp.^2 + Mass(4)*g*f)/Eta*R(4);
    % MPC-subproblem
    [u, Cost(i,4), Exitflg(i,4), output] = fmincon(@(u) Fungsicos( Np, Tim_step, X0 ,u, Vehicle_Type,Q3,Xdes,R3,F3,Xa,G3,Xnfa), ...
        u0, A, b, Aeq, beq, lb, ub, @(u) kendalanonlinear(Np, Tim_step, X0, u, Vehicle_Type,Pnp,Vnp,Tnp),options); 
    
    % state involves one step
    U(i,4) = u(1);
    [Postion(i,4),Velocity(i,4),Torque(i,4)] = traindiscretemodel(U(i,4),Tim_step,Postion(i-1,4),Velocity(i-1,4),Torque(i-1,4),Mass(4),R(4),g,f,Eta,Ca(4),Tao(4),Ca_0(4),Ca_1(4),Ca_2(4));
    
    % Update assumed state
    Temp = zeros(Np+1,3);
    Temp(1,:) = [Postion(i,4),Velocity(i,4),Torque(i,4)];  
    ua(1:Np-1,4) = u(2:Np);
    for j = 1:Np-1
        [Temp(j+1,1),Temp(j+1,2),Temp(j+1,3)] = traindiscretemodel(ua(j,4),Tim_step,Temp(j,1),Temp(j,2),Temp(j,3),Mass(4),R(4),g,f,Eta,Ca(4),Tao(4),Ca_0(4),Ca_1(4),Ca_2(4));
    end
    
    ua(Np,4) = (Ca(4)*Temp(Np,2).^2 + Mass(4)*g*f)/Eta*R(4);
    [Temp(Np+1,1),Temp(Np+1,2),Temp(Np+1,3)] = traindiscretemodel(ua(Np,4),Tim_step,Temp(Np,1),Temp(Np,2),Temp(Np,3),Mass(4),R(4),g,f,Eta,Ca(4),Tao(4),Ca_0(4),Ca_1(4),Ca_2(4));
    Pa_next(:,4) = Temp(:,1);
    Va_next(:,4) = Temp(:,2);
    toc
    
    
    
    %% vehicle five
    tic
    Vehicle_Type = [Mass(5),Ca_0(5),Ca_1(5),Ca_2(5),Tao(5),R(5),g,f,Eta,Ca(5)];                
    X0 = [Postion(i-1,5),Velocity(i-1,5),Torque(i-1,5)];             
    Pd = zeros(Np+1,1);  Vd = zeros(Np+1,1);                    
    Xdes = [Pd,Vd];  % Udes = Td;                                   
    Xa = [Pa(:,5),Va(:,5)];                                      
    Xnfa = [Pa(:,4) - d, Va(:,4)];                                        
   
    u0 = ua(:,5);  
    A = [];b = []; Aeq = []; beq = [];                                     
    lb = Torquebound(5,1)*ones(Np,1); ub = Torquebound(5,2)*ones(Np,1);                 
    Pnp = Xnfa(end,1); Vnp = Xnfa(end,2);
    Xend(i,5) = Pnp; Vend(i,5) = Vnp; Tnp = (Ca(5)*Vnp.^2 + Mass(5)*g*f)/Eta*R(5);
    % MPC -subproblem
    [u, Cost(i,5), Exitflg(i,5), output] = fmincon(@(u) Fungsicos( Np, Tim_step, X0 ,u, Vehicle_Type,Q3,Xdes,R3,F3,Xa,G3,Xnfa), ...
        u0, A, b, Aeq, beq, lb, ub, @(u) kendalanonlinear(Np, Tim_step, X0, u, Vehicle_Type,Pnp,Vnp,Tnp),options); 
    
    % state involves one step
    U(i,5) = u(1);
    [Postion(i,5),Velocity(i,5),Torque(i,5)] = traindiscretemodel(U(i,5),Tim_step,Postion(i-1,5),Velocity(i-1,5),Torque(i-1,5),Mass(5),R(5),g,f,Eta,Ca(5),Tao(5),Ca_0(5),Ca_1(5),Ca_2(5));
    
    % update assumed state
    Temp = zeros(Np+1,3);
    Temp(1,:) = [Postion(i,5),Velocity(i,5),Torque(i,5)];   
    ua(1:Np-1,5) = u(2:Np);
    for j = 1:Np-1
        [Temp(j+1,1),Temp(j+1,2),Temp(j+1,3)] = traindiscretemodel(ua(j,5),Tim_step,Temp(j,1),Temp(j,2),Temp(j,3),Mass(5),R(5),g,f,Eta,Ca(5),Tao(5),Ca_0(5),Ca_1(5),Ca_2(5));
    end
    
    ua(Np,5) = (Ca(5)*Temp(Np,2).^2 + Mass(5)*g*f)/Eta*R(5);
    [Temp(Np+1,1),Temp(Np+1,2),Temp(Np+1,3)] = traindiscretemodel(ua(Np,5),Tim_step,Temp(Np,1),Temp(Np,2),Temp(Np,3),Mass(5),R(5),g,f,Eta,Ca(5),Tao(5),Ca_0(5),Ca_1(5),Ca_2(5));
    Pa_next(:,5) = Temp(:,1);
    Va_next(:,5) = Temp(:,2);
    toc
    
    %% vehicle six
    tic
    Vehicle_Type = [Mass(6),Ca_0(6),Ca_1(6),Ca_2(6),Tao(6),R(6),g,f,Eta,Ca(6)];                 % the vehicle parameters ： Mass,R,g,f,Eta,Ca(i),Tao, 
    X0 = [Postion(i-1,6),Velocity(i-1,6),Torque(i-1,6)];                % the vehicle variable in the last time
    Pd = zeros(Np+1,1);  Vd = zeros(Np+1,1);                      
    Xdes = [Pd,Vd];                            
    Xa = [Pa(:,6),Va(:,6)];                                           
    Xnfa = [Pa(:,5) - d, Va(:,5)];                                        
   
    u0 = ua(:,6);  
    A = [];b = []; Aeq = []; beq = [];                                      
    lb = Torquebound(6,1)*ones(Np,1); ub = Torquebound(6,2)*ones(Np,1);           
    Pnp = Xnfa(end,1); Vnp = Xnfa(end,2); 
    Xend(i,6) = Pnp; Vend(i,6) = Vnp; Tnp = (Ca(6)*Vnp.^2 + Mass(6)*g*f)/Eta*R(6);
    % MPC 优化求解
    [u, Cost(i,6), Exitflg(i,6), output] = fmincon(@(u) Fungsicos( Np, Tim_step, X0 ,u, Vehicle_Type,Q3,Xdes,R3,F3,Xa,G3,Xnfa), ...
        u0, A, b, Aeq, beq, lb, ub, @(u) kendalanonlinear(Np, Tim_step, X0, u, Vehicle_Type,Pnp,Vnp,Tnp),options); 
    
    % state involves one step
    U(i,6) = u(1);
    [Postion(i,6),Velocity(i,6),Torque(i,6)] = traindiscretemodel(U(i,6),Tim_step,Postion(i-1,6),Velocity(i-1,6),Torque(i-1,6),Mass(6),R(6),g,f,Eta,Ca(6),Tao(6),Ca_0(6),Ca_1(6),Ca_2(6));
    
    % update assumed state
    Temp = zeros(Np+1,3);
    Temp(1,:) = [Postion(i,6),Velocity(i,6),Torque(i,6)]; 
    ua(1:Np-1,6) = u(2:Np);
    for j = 1:Np-1
        [Temp(j+1,1),Temp(j+1,2),Temp(j+1,3)] = traindiscretemodel(ua(j,6),Tim_step,Temp(j,1),Temp(j,2),Temp(j,3),Mass(6),R(6),g,f,Eta,Ca(6),Tao(6),Ca_0(6),Ca_1(6),Ca_2(6));
    end
     
    ua(Np,6) = (Ca(6)*Temp(Np,2).^2 + Mass(6)*g*f)/Eta*R(6);
    [Temp(Np+1,1),Temp(Np+1,2),Temp(Np+1,3)] = traindiscretemodel(ua(Np,6),Tim_step,Temp(Np,1),Temp(Np,2),Temp(Np,3),Mass(6),R(6),g,f,Eta,Ca(6),Tao(6),Ca_0(6),Ca_1(6),Ca_2(6));
    Pa_next(:,6) = Temp(:,1);
    Va_next(:,6) = Temp(:,2);
    toc
    
    %% vehicle seven
    tic
    Vehicle_Type = [Mass(7),Ca_0(7),Ca_1(7),Ca_2(7),Tao(7),R(7),g,f,Eta,Ca(7)];                 % the vehicle parameters ： Mass,R,g,f,Eta,Ca(i),Tao, 
    X0 = [Postion(i-1,7),Velocity(i-1,7),Torque(i-1,7)];                % the vehicle variable in the last time
    Pd = zeros(Np+1,1);  Vd = zeros(Np+1,1);                    
    Xdes = [Pd,Vd];  % Udes = Td;                                     
    Xa = [Pa(:,7),Va(:,7)];                                            
    Xnfa = [Pa(:,6) - d, Va(:,6)];                                       
   
    u0 = ua(:,7);   
    A = [];b = []; Aeq = []; beq = [];                                       
    lb = Torquebound(7,1)*ones(Np,1); ub = Torquebound(7,2)*ones(Np,1);               
    Pnp = Xnfa(end,1); Vnp = Xnfa(end,2);   
    Xend(i,7) = Pnp; Vend(i,7) = Vnp; Tnp = (Ca(7)*Vnp.^2 + Mass(7)*g*f)/Eta*R(7);
    % MPC-subproblem
    [u, Cost(i,7), Exitflg(i,7), output] = fmincon(@(u) Fungsicos( Np, Tim_step, X0 ,u, Vehicle_Type,Q3,Xdes,R3,F3,Xa,G3,Xnfa), ...
        u0, A, b, Aeq, beq, lb, ub, @(u) kendalanonlinear(Np, Tim_step, X0, u, Vehicle_Type,Pnp,Vnp,Tnp),options); 
    
    % state involves one step
    U(i,7) = u(1);
    [Postion(i,7),Velocity(i,7),Torque(i,7)] = traindiscretemodel(U(i,7),Tim_step,Postion(i-1,7),Velocity(i-1,7),Torque(i-1,7),Mass(7),R(7),g,f,Eta,Ca(7),Tao(7),Ca_0(7),Ca_1(7),Ca_2(7));
    
    % update assumed state
    Temp = zeros(Np+1,3);
    Temp(1,:) = [Postion(i,7),Velocity(i,7),Torque(i,7)];
    ua(1:Np-1,7) = u(2:Np);
    for j = 1:Np-1
        [Temp(j+1,1),Temp(j+1,2),Temp(j+1,3)] = traindiscretemodel(ua(j,7),Tim_step,Temp(j,1),Temp(j,2),Temp(j,3),Mass(7),R(7),g,f,Eta,Ca(7),Tao(7),Ca_0(7),Ca_1(7),Ca_2(7));
    end
    ua(Np,7) = (Ca(7)*Temp(Np,2).^2 + Mass(7)*g*f)/Eta*R(7);
    [Temp(Np+1,1),Temp(Np+1,2),Temp(Np+1,3)] = traindiscretemodel(ua(Np,7),Tim_step,Temp(Np,1),Temp(Np,2),Temp(Np,3),Mass(7),R(7),g,f,Eta,Ca(7),Tao(7),Ca_0(7),Ca_1(7),Ca_2(7));
    Pa_next(:,7) = Temp(:,1);
    Va_next(:,7) = Temp(:,2);
    toc
    
    %% Update assumed data
    Pa = Pa_next;
    Va = Va_next;
       
end
FigurePlot

When I run the main program, there is error like this

Steps i= 2
Error using traindiscretemodel
Too many input arguments.
Error in Fungsicos (line 12)
    [Pp(1),Vp(1),Tp(1)] = traindiscretemodel(u(1),Tim_step,X0(1),X0(2),X0(3),Mass,Ca_0,Ca_1,Ca_2,Tao,Radius,g,f,Eta,Ca);
Error in Train_PF>@(u)Fungsicos(Np,Tim_step,X0,u,Vehicle_Type,Q1,Xdes,R1,F1,Xa,G1,Xnba) (line 115)
    [u, Cost(i,1), Exitflg(i,1), output] = fmincon(@(u) Fungsicos( Np, Tim_step, X0 ,u,
    Vehicle_Type,Q1,Xdes,R1,F1,Xa,G1,Xnba), ...
Error in fmincon (line 566)
      initVals.f = feval(funfcn{3},X,varargin{:});
Error in Train_PF (line 115)
    [u, Cost(i,1), Exitflg(i,1), output] = fmincon(@(u) Fungsicos( Np, Tim_step, X0 ,u,
    Vehicle_Type,Q1,Xdes,R1,F1,Xa,G1,Xnba), ...
Caused by:
    Failure in initial objective function evaluation. FMINCON cannot continue.
 

I don't know why in my traindiscretemodel function said Too Many Input Arguments and the other error. Need Help

1 Comment
Show -1 older commentsHide -1 older comments

Voss on 3 Nov 2020

Looks like traindiscretemodel is defined to have 10 input arguments, but you are sometimes calling it with 15.

Sign in to comment.

Sign in to answer this question.

Answer 1

Walter Roberson on 3 Nov 2020

0
Link

Direct link to this answer

https://ch.mathworks.com/matlabcentral/answers/635304-too-many-input-arguments#answer_533044

Open in MATLAB Online

function [PositionN, VelocityN, TorqueN] = traindiscretemodel(u,Tim_step,Position,Velocity,Torque,Mass,Ca_0,Ca_1,Ca_2,Tao)
% Train Model

10 inputs permitted

    [Pp(1),Vp(1),Tp(1)] = traindiscretemodel(u(1),Tim_step,X0(1),X0(2),X0(3),Mass,Ca_0,Ca_1,Ca_2,Tao,Radius,g,f,Eta,Ca);

15 inputs provided.

In particular Radius, g, f, Eta, Ca are not inputs defined by the function declaration

2 Comments
Show NoneHide None

Ivan Dwi Putra on 4 Nov 2020

ok thank you. I will see my code

Ivan Dwi Putra on 4 Nov 2020

Open in MATLAB Online

I just change the code but now error like this

 Steps i= 4
Elapsed time is 1.072240 seconds.
Error using barrier
Nonlinear constraint function is undefined at initial point. Fmincon cannot continue.
Error in fmincon (line 848)
    [X,FVAL,EXITFLAG,OUTPUT,LAMBDA,GRAD,HESSIAN] = barrier(funfcn,X,A,B,Aeq,Beq,l,u,confcn,options.HessFcn, ...
Error in Train_PF (line 150)
    [u, Cost(i,2), Exitflg(i,2), output] = fmincon(@(u) Fungsicos( Np, Tim_step, X0 ,u,
    Vehicle_Type,Q2,Xdes,R2,F2,Xa,G2,Xnfa), ...
 

Sign in to comment.

Too Many Input Arguments

1 Comment
Show -1 older commentsHide -1 older comments

Answers (1)

2 Comments
Show NoneHide None

See Also

Categories

Tags

Products

Release

Community Treasure Hunt

Too Many Input Arguments

1 Comment Show -1 older commentsHide -1 older comments

Answers (1)

2 Comments Show NoneHide None

See Also

Categories

Tags

Products

Release

Community Treasure Hunt

1 Comment
Show -1 older commentsHide -1 older comments

2 Comments
Show NoneHide None