Solving error in particle filter matlab

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Hi, I try to run simple particle filter but error keep appearing saying

Unable to perform assignment because the size of the left side is 10-by-1 and the size of the right side is 10-by-10.

Error in (line 43) x(:,k) = sys(k, x(:,k-1), u(:,k)); % simulate state

is there any recommendation I could make to solve this error? I really appreciate your help.

%% clear memory, screen, and close all figures
clear, clc, close all;
%% Process equation x[k] = sys(k, x[k-1], u[k]);% untuk state vector
nx = 10;  % number of states
sys=@(k, xkm1, uk) xkm1./2 + 25.*xkm1/(1+xkm1.^2) + 8*cos(1.2*k) + uk; % (returns column vector)
%% Observation equation y[k] = obs(k, x[k], v[k]);
ny = 1;                                           % number of observations
obs = @(k, xk,vk) 100 + vk;                     % (returns column vector)
%% PDF of process noise and noise generator function
nu = 10;                                           % size of the vector of process noise
sigma_u = sqrt(0.0000001);
p_sys_noise   = @(u) normpdf(u, 0, sigma_u);
gen_sys_noise = @(u) normrnd(0, sigma_u);         % sample from p_sys_noise (returns column vector)
%% PDF of observation noise and noise generator function
nv = 1;                                           % size of the vector of observation noise
sigma_v = sqrt(0.0000001);
p_obs_noise   = @(v) normpdf(v, 0, sigma_v);
gen_obs_noise = @(v) normrnd(0, sigma_v);         % sample from p_obs_noise (returns column vector)
%% Initial PDF
% p_x0 = @(x) normpdf(x, 0,sqrt(10));             % initial pdf
gen_x0 = @(x) [95;96;97;98;94;93;97;99;98;100]+ones(10,1)*normrnd(0, sqrt(0.0000001));     % sample from p_x0 (returns column vector)
%% Transition prior PDF p(x[k] | x[k-1])
% (under the suposition of additive process noise)
% p_xk_given_xkm1 = @(k, xk, xkm1) p_sys_noise(xk - sys(k, xkm1, 0));
%% Observation likelihood PDF p(y[k] | x[k])
% (under the suposition of additive process noise)
p_yk_given_xk = @(k, yk, xk) p_obs_noise(yk - obs(k, xk, 0));
%% %% Number of time steps
T = 100;
%% Separate memory space
x = zeros(nx,T);  y = zeros(ny,T);
u = zeros(nu,T);  v = zeros(nv,T);
%% Simulate system
xh0 = 0;                                  % initial state
u(:,1) = 0;                               % initial process noise
v(:,1) = gen_obs_noise(sigma_v);          % initial observation noise
x(:,1) = xh0;
y(:,1) = obs(1, xh0, v(:,1));
for k = 2:T
    % here we are basically sampling from p_xk_given_xkm1 and from p_yk_given_xk
    u(:,k) = gen_sys_noise();              % simulate process noise
    v(:,k) = gen_obs_noise();              % simulate observation noise
    x(:,k) = sys(k, x(:,k-1), u(:,k));     % simulate state
    y(:,k) = obs(k, x(:,k),   v(:,k));     % simulate observation
end
fprintf('Finish simulate system \n')
%% Separate memory
xh = zeros(nx, T); xh(:,1) = xh0;
yh = zeros(ny, T); yh(:,1) = obs(1, xh0, 0);
pf.k               = 1;                   % initial iteration number
pf.Ns              = 100;                 % number of particles
pf.w               = zeros(pf.Ns, T);     % weights
pf.particles       = zeros(nx, pf.Ns, T); % particles
pf.gen_x0          = gen_x0;              % function for sampling from initial pdf p_x0
pf.p_yk_given_xk   = p_yk_given_xk;       % function of the observation likelihood PDF p(y[k] | x[k])
pf.gen_sys_noise   = gen_sys_noise;       % function for generating system noise
%pf.p_x0 = p_x0;                          % initial prior PDF p(x[0])
%pf.p_xk_given_ xkm1 = p_xk_given_xkm1;   % transition prior PDF p(x[k] | x[k-1])
%% Estimate state
for k = 2:T
    fprintf('Iteration = %d/%d\n',k,T);
    % state estimation
    pf.k = k;
    %[xh(:,k), pf] = particle_filter(sys, y(:,k), pf, 'multinomial_resampling');
    [xh(:,k), pf] = particle_filter(sys, y(:,k), pf, 'systematic_resampling');   
    
    % filtered observation
    yh(:,k) = obs(k, xh(:,k), 0);
end
%% plot of the state vs estimated state by the particle filter vs particle paths
figure
hold on;
h2 = plot(1:T,x,'b','LineWidth',4);
h3 = plot(1:T,xh,'r','LineWidth',4);
%h4 = plot(1:T,xhmode,'g','LineWidth',4);
legend([h2(1) h3(1)  ],'state','mean of estimated state','particle paths');
title('State vs estimated state by the particle filter vs particle paths','FontSize',14);
%%  plot of the observation vs filtered observation by the particle filter
figure
plot(1:T,y,'b', 1:T,yh,'r');
legend('observation','filtered observation');
title('Observation vs filtered observation by the particle filter','FontSize',14);
return;