Model Predictive Control 4 - Dual mode and infinite horizon predictive control

Uses MATLAB to illustrate the OMPC and SOMPC algorithms in the constraint free and regulation case. Both are guaranteed stable, however whereas OMPC gives zero perturbations (well-posed optimisation), SOMPC gives non-zero perturbations and a prediction mismatch. Nevertheless the SOMPC compensator tends towards OMPC and has a small prediction mismatch for high control horizons.

Earlier videos assumed the state and disturbance were known whereas in practice these need to be estimated. This video gives a brief review of he impact of including an observer. Critically, although nominal stability results are unaffected, performance can be seriously degraded. Also includes MATLAB comparisons with the independent model approach.

Extends the earlier videos to include non-zero targets and disturbances. Demonstrates how near identical algebra and optimisation may be applicable by making use of superposition and deviation variables. Makes use of unbiased predictions and performance indices discussed in chapter 1. MATLAB examples are included.

Earlier videos assumed the state and disturbance were known whereas in practice these need to be estimated. This video gives a brief review of he impact of using an independent model as an observer. Critically, although nominal stability results are unaffected, performance can be seriously degraded. Includes MATLAB comparisons.

Analyses each component of the performance index in turn and demonstrates through argument and MATLAB examples how this has a very simple (and well conditioned) structure. Shows the optimised control law is independent of the control horizon.

Introduces analysis and anticipation of closed-loop stability. Shows how the use of infinite horizons in conjunction with the tail enables guarantees of stability for the nominal case. Shows how dual-mode approaches, regardless of stabilising terminal mode, can meet these criteria.

Uses the concept of a dual-mode predictive in OMPC and allows some modification to give a more general dual-mode approach (Subopotimal MPC). Demonstrates how the algebra and computation is almost identical, but the performance index for SOMPC has a slightly more complex structure than OMPC which means the control law is also dependent on the control horizon.

Introduces concepts of consistent recursive decision making, that is how we ensure the optimisation at the current sample has a synergy with that taken at the previous sample. This is a prerequisiste for the expectation of good behaviour/stability. It is shown how the concept of the tail captures this requirement.

Demonstrates how predictions based around the global unconstrained optimal can be substituted into the performance index to give a simple cost function. Introduces the augmented model as a mechanism for keeping the algebra very simple and transparent. Uses Lyapunov to deal with summations over an infinite horizon. Includes MATLAB code for computing the parameters.

Shows how the 'global' unconstrained optimal can be embedded within system predictions and also how to add degrees of freedom which can be used for fine tuning. Gives fine details of the associated predictions which are analogous to developments in chapter 1.

Gives the human or philosophical thinking behind optimal predictive control and explains why this is an intuitively obvious approach to predictive control algorithm design. Implicitly builds on insights developed in chapter 3.