set_objective¶
Class method.
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do_mpc.controller.MPC.set_objective(self, mterm=None, lterm=None)¶ Sets the objective of the optimal control problem (OCP). We introduce the following cost function:
\[J(x,u,z) = \sum_{k=0}^{N}\left(\underbrace{l(x_k,z_k,u_k,p_k,p_{\text{tv},k})}_{\text{lagrange term}} + \underbrace{\Delta u_k^T R \Delta u_k}_{\text{r-term}}\right) + \underbrace{m(x_{N+1})}_{\text{meyer term}}\]which is applied to the discrete-time model AND the discretized continuous-time model. For discretization we use orthogonal collocation on finite elements . The cost function is evaluated only on the first collocation point of each interval.
set_objective()is used to set the \(l(x_k,z_k,u_k,p_k,p_{\text{tv},k})\) (lterm) and \(m(x_{N+1})\) (mterm), whereNis the prediction horizon. Please seeset_rterm()for the penalization of the control inputs.Parameters: - lterm (CasADi SX or MX) – Stage cost - scalar symbolic expression with respect to
_x,_u,_z,_tvp,_p - mterm (CasADi SX or MX) – Terminal cost - scalar symbolic expression with respect to
_xand_p
Raises: - assertion – mterm must have
shape=(1,1)(scalar expression) - assertion – lterm must have
shape=(1,1)(scalar expression)
Returns: None
Return type: None
- lterm (CasADi SX or MX) – Stage cost - scalar symbolic expression with respect to
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