Version 3.3 (R2011a) Model Predictive Control Toolbox Software

This table summarizes what's new in Version 3.3 (R2011a):

New Features and Changes	Version Compatibility Considerations	Fixed Bugs and Known Problems
Yes Details below	No	Bug Reports Includes fixes

New features and changes introduced in this version are:

• Support for Custom Constraints on MPC Controller Inputs and Outputs

• Ability to Specify Terminal Constraints and Weights on MPC Controller

• Ability to Access Optimal Cost and Optimal Control Sequence

Support for Custom Constraints on MPC Controller Inputs and Outputs

In addition to upper and lower bounds, you can now specify constraints on linear combinations of an MPC controller inputs (u(t)) and outputs (y(t)). Specify custom constraints, such as u1 + u2 < 1 or u + y < 2, in the mpc object using setconstraint.

For more information, see:

• Custom Constraints on Inputs and Outputs

• Custom Constraints in a Blending Process

• MPC Control with Constraints on a Combination of Input and Output Signals demo

Ability to Specify Terminal Constraints and Weights on MPC Controller

You can now specify weights and constraints on the terminal predicted states of an MPC controller.

Using terminal weights, you can achieve infinite horizon control. For example, you can design an unconstrained MPC controller that behaves in exactly the same way as a Linear-Quadratic Regulator (LQR). You can use terminal constraints as an alternative way to achieve closed-loop stability by defining a terminal region.

You can specify both weights and constraints using the setterminal command.

For more information, see:

• Terminal Weights and Constraints

• Using Terminal Penalty to Provide LQR Performance

• Implementing Infinite-Horizon LQR by Setting Terminal Weights in a Finite-Horizon MPC Formulation demo

Ability to Access Optimal Cost and Optimal Control Sequence

This release introduces two new parameters Enable optimal cost outport and Enable control sequence outport in the MPC Controller block. Using these parameters, you can access the optimal cost and control sequence along the prediction horizon. This information helps you analyze control performance.

You can also access the optimal cost and control sequence programmatically using the new Cost and Yopt fields, respectively, of the structure info returned by mpcmove.

For more information on using optimal cost and control sequence, see the following demos:

• MPC Control with Input Quantization Based on Comparing the Optimal Costs

• Analysis of Control Sequences Optimized by MPC on a Double Integrator System

Version 4.0 (R2011b) Model Predictive Control Toolbox Software

Version 3.2.1 (R2010b) Model Predictive Control Toolbox Software

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