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nonlinear mpc design -凯发k8网页登录

design model predictive controllers with nonlinear prediction models, costs, and constraints

as in traditional linear mpc, nonlinear mpc calculates control actions at each control interval, using a combination of model-based prediction and constrained optimization. the key differences are:

  • the prediction model can be nonlinear and include time-varying parameters

  • the equality and inequality constraints can be nonlinear

  • the scalar cost function to be minimized can be a nonquadratic (linear or nonlinear) function of the decision variables.

by default, nonlinear mpc controllers solve a nonlinear programming problem using the fmincon function, which requires optimization toolbox™ software. if you do not have optimization toolbox software you can specify your own custom nonlinear solver.

for more information, see .

functions

nlmpcnonlinear model predictive controller
multistage nonlinear model predictive controller
examine prediction model and custom functions of nlmpc or nlmpcmultistage objects for potential problems
generate matlab jacobian functions for multistage nonlinear mpc using automatic differentiation
compute optimal control action for nonlinear mpc controller
option set for nlmpcmove function
create data structure to simulate multistage mpc controller with nlmpcmove
convert nlmpc object into one or more mpc objects
create simulink bus object and configure bus creator block for passing model parameters to nonlinear mpc controller block

blocks

simulate nonlinear model predictive controllers
simulate multistage nonlinear model predictive controllers

topics

nonlinear mpc basics


  • nonlinear model predictive controllers control plants using nonlinear prediction models, cost functions, or constraints.

  • to define a prediction model for a nonlinear mpc controller, specify the state and output functions.

  • nonlinear mpc controllers support generic cost functions, such as a combination of linear or nonlinear functions of the system states, inputs, and outputs.

  • you can specify custom linear and nonlinear constraints for your nonlinear mpc controller in addition to standard linear mpc constraints.
  • configure optimization solver for nonlinear mpc
    by default, nonlinear mpc controllers optimize their control move using the fmincon function from the optimization toolbox. you can also specify your own custom nonlinear solver.
  • trajectory optimization and control of flying robot using nonlinear mpc
    you can use nonlinear mpc for both optimal trajectory planning and closed-loop control applications.

  • plan an optimal rocket lander trajectory and perform closed-loop control of landing process using multistage nonlinear mpc.

feedback control


  • control a nonlinear plant as it transitions between operating points.

  • achieve swing-up and balancing control of an inverted pendulum on a cart using a nonlinear model predictive controller.

  • you can generate one or more linear mpc controllers from a nonlinear mpc controller and use these controllers for gain-scheduled control applications.

  • simulate nonlinear mpc controller as adaptive and time-varying mpc controller, and compare performance.

optimal planning


  • you can use nonlinear mpc controllers for optimal planning applications that require a nonlinear model with nonlinear costs or constraints.

  • use nonlinear mpc to plan and execute trajectories for a robot manipulator.

economic mpc


  • economic model predictive controllers optimize control actions to satisfy generic economic or performance cost functions.

  • maximize production of an ethylene oxide plant for profit using a nonlinear cost function and nonlinear constraints.

passivity-based mpc


  • enforce stability of a robotic manipulator by implementing passivity-based constraints in a nonlinear mpc controller.

  • control a system of four water tanks using passivity-based mpc.

related information


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