Passivity.OutputFcn - Name of the desired passivity output Passivity.InputPassivityIndex - A desired nonnegative Passivity.OutputPassivityIndex - A desired nonnegative For example, to compute the output variable Jacobians Yjacob Output function, as specified in the Jacobian.OutputFcn property of theĬontroller. To use output variable Jacobians in your constraint Jacobian functions, you must firstĭerive them from the state and input arguments using the Jacobian of the prediction model Jacobian of the inequality constraints with respect to the slack variable, Manipulated variable trajectories, returned as aĪrray, where Gmv ( i, j, l ) = ∂ c ( l ) / ∂ U ( i, M V ( j ) ) and MV( j) is theĬonstraints use U(p+1,:), you must include the impact of both Jacobian of the equality or inequality constraints with respect to the The second row to row p+1, ignoring the first row.

Jacobian of the equality or inequality constraints with respect to the stateĪrray, where G ( i, j, l ) = ∂ c ( l ) / ∂ X ( i + 1, j ). For example, toĬompute the output trajectory Y from time k to time In the Model.OutputFcn property of the controller. To use output variable values in your constraint functions, you must first derive themįrom the state and input arguments using the prediction model output function, as specified Satisfied when the corresponding output is less than or equal to Satisfied when the corresponding output is 0.Ĭomputed inequality constraint values, returned as a column vector of length (in Simulink ®) or to a simulation function such as nlmpcmove (in MATLAB).Ĭomputed equality constraint values, returned as a column vector of length These parameters to the Nonlinear MPC Controller block Parameters, you must specify the number of parameters using Model, custom cost function, and custom constraint functions of the controller.įor example, if the state function uses only parameter p1, theĬonstraint functions use only parameter p2, and the costįunction uses only parameter p3, then all three parameters are The same parameters are passed to the prediction

Optional parameters, specified as a comma-separated list (for example Unmeasured disturbance indices, as defined in theĭimensions.UDIndex property of the controller Measured disturbance indices, as defined in theĭimensions.MDIndex property of the controller Manipulated variables indices, as defined in theĭimensions.MVIndex property of the controller PredictionHorizon property of the controllerĭimensions.NumberOfStates property of theĭimensions.NumberOfOutputs property of theĭimensions.NumberOfInputs property of the Manipulated variable targets, as specified in the Reference values for plant outputs, as specified in the MV moves used in previous control, as specified in the Prediction model sample time, as defined in the TsĬurrent prediction model states, as specified in the To do so, specify theĬustom functions as one of the following. Optimization.CustomIneqConFcn respectively. To configure your nonlinear MPC controller to use custom equality or inequalityĬonstraints, set its Optimization.CustomEqConFcn or You can specify custom equality and inequality constraints for a nonlinear MPCĬontroller. Lower bounds on manipulated variable i rate of For more information on setting controller linear constraint properties, see For each of these constraints, you can specify a single bound that appliesĪcross the entire prediction horizon, or you can vary each constraint over the prediction The following table shows the standard linear constraints supported by nonlinear MPCĬontrollers. Instead, you implement suchĬonstraints within your custom equality or inequality constraint functions. Properties are not available for nonlinear MPC controllers. Linear MPC controllers have properties for defining custom constraints on linearĬombinations of inputs and outputs, as discussed in Constraints on Linear Combinations of Inputs and Outputs. Including the constraint functions and their Jacobians, using the validateFcns Passivity constraints are not supported for nlmpcMultistageīefore simulating your controller, it is best practice to validate your custom functions, You can enforce controller passivity by specifying passivityįor a multistage MPC controller, each stage has its own constraints, and each constraintįor a specific stage is function only of the decision variables and parameters at that stage. If your plant is passive, enforcing controller passivity guarantees that the resultingĬlosed loop system is stable. Require cumulative resource consumption to stay within specified limits Require the plant to reach a target state at the end of the prediction horizon

Soft horizon definition