# Linearize nonlinear state space equation in MATLAB at steady state setpoint

This post shows one way to linearize a nonlinear state equation at a steady state setpoint in MATLAB. It is assumed that a function ode.m exists in which the state equation is implemented:

function dx = ode(t, x, u) % example ODE dx1 = tan(x(4)) * x(2) + 2*u(2) - 1; dx2 = x(1) - sin(x(2)); dx3 = 13 * x(4) + u(1) + 1; dx4 = 2*x(1); dx = [dx1; dx2; dx3; dx4]; end

If the desired setpoint is not known, a steady state setpoint could be calculated with a desired objective function J in CasADi like this:

close all clear variables clc import casadi.*; ocp = casadi.Opti(); nx = 4; nu = 2; X = ocp.variable(nx); U = ocp.variable(nu); % dynamics ocp.subject_to([0;0;0;0] == ode(0, X, U)); % add cost function to minimize input J = U(1,1)^2 + U(2,1)^2; ocp.minimize( J ); % specify solver ocp.solver( 'ipopt' ); % solve OCP sol = ocp.solve(); x_ss = sol.value(X); u_ss = sol.value(U);

The example yields the following setpoint: .

Now we can linearize the model at the setpoint using the MATLAB Symbolic Math Toolbox:

% setpoint t = 0; x=[0; 0; 0; -0.0769]; % state u=[0;0.5]; % control input % for eval later x1 = x(1); x2 = x(2); x3 = x(3); x4 = x(4); u1 = u(1); u2 = u(2); % symbolic vars x_sym = sym('x', [4 1]); u_sym = sym('u', [2 1]); ode_sym = ode(t,x_sym,u_sym); % calculate system and input matrices Jac_x = jacobian(ode_sym, x_sym); A = eval(subs(Jac_x)); Jac_u = jacobian(ode_sym, u_sym); B = eval(subs(Jac_u));

This linearization yields the system matrix

and the input matrix which could be used in a linear model approximation.

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