![]() StabilizationParameter - Stabilization parameter. Nonnegative integer representing the maximum number of outer-loop iterations for the Restart - Number of outer-loop iterations. Positive integer representing the maximum number of inner-loop iterations allowed for MaxIterations - Maximum number of iterations allowed. Larger step size can result in faster convergence but may also lead to numerical This is a positive scalar representing the step size that the forwardįinite difference scheme uses to approximate the Jacobian in the optimization problem. The default value isįiniteDifferenceStepSize - Step size used for finiteĭifferences. "iter" to display solver information at every iteration, and use Vector specified as either "iter" or "none". The default value isĭisplay - Level of display. To penalize lower and upped bounds constraint violations. Scalar representing the strength of the penalty term added to the constraints in theīarrier function method for nonlinear model predictive control. The C/GMRES options object has the following properties, which you can modify using dotīarrierParameter - Parameter barrier value. Options object in the Optimization.SolverOptions property of your When you do so, the software stores a default C/GMRES ![]() Optimization.Solver property of your multistage nonlinear MPC objectĪs "cgmres". To use the C/GMRES method to solve multistage nonlinear MPC problems, specify the The computationalĮfficiency and convergence properties of the C/GMRES method are particularly useful whenĪpplied to large scale systems and systems that exhibit nonlinear and non-convex The C/GMRES method is another built-in option for optimization. The solver to calculate gradients that are known to be zero. ![]() For example, the constraint gradient matricesĪre sparse, and setting Specif圜onstraintGradient to false would cause Significantly affect controller performance. Specif圜onstraintGradient solver options, since doing so can In general, you should not modify the SpecifyObjectiveGradient and Options, see fmincon (Optimization Toolbox). For more information on the available solver Maximum number of solver iterations for your application, set You can modify the solver options for your application. These nondefault options typically improve the performance of the nonlinear MPC Use constraint gradients ( SolverOptions.Specif圜onstraintGradient =ĭo not display optimization messages to the command window Use objective function gradients ( SolverOptions.SpecifyObjectiveGradient = Use the SQP algorithm ( SolverOptions.Algorithm = To use these trajectories as initial guesses at Trajectories as initial guesses by default. In Simulink ®, the Nonlinear MPC Controller block is configured to use these Trajectories from the previous control interval as the initial guesses for the currentĬontrol interval. To do so, use the predicted state and manipulated variable Provide a good starting point near the global optimum.ĭuring closed-loop simulations, it is best practice to warm start However, nonlinear MPC optimization problems often allow multiple solutions (local minima),Īnd finding a solution can be difficult for the solver. Specify Initial GuessesĪ properly configured standard linear MPC optimization problem has a unique solution. N mv is the number of manipulated variables,Īnd the +1 accounts for the global slack variable. Therefore, the number of decision variables N Z U input argument of your cost and constraint functions. Manipulated variable columns in rows 1 through p of the Predicted manipulated variables from time ![]() Through p+1 of the X input argument of your costĪnd constraint functions, where p is the prediction horizon. Predicted state values from time t k+1 to
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