Download Model Predictive Control: Classical, Robust and Stochastic by Basil Kouvaritakis, Mark Cannon PDF

By Basil Kouvaritakis, Mark Cannon

For the 1st time, a textbook that brings jointly classical predictive regulate with remedy of up to date strong and stochastic techniques.

Model Predictive keep watch over describes the advance of tractable algorithms for doubtful, stochastic, restricted structures. the place to begin is classical predictive regulate and the suitable formula of functionality targets and constraints to supply promises of closed-loop balance and function. relocating directly to powerful predictive regulate, the textual content explains how related promises can be acquired for situations during which the version describing the method dynamics is topic to additive disturbances and parametric uncertainties. Open- and closed-loop optimization are thought of and the cutting-edge in computationally tractable tools in accordance with uncertainty tubes provided for structures with additive version uncertainty. eventually, the tube framework is additionally utilized to version predictive keep watch over difficulties regarding tough or probabilistic constraints for the situations of multiplicative and stochastic version uncertainty. The e-book provides:

  • extensive use of illustrative examples;
  • sample difficulties; and
  • discussion of novel regulate functions similar to source allocation for sustainable improvement and turbine-blade regulate for maximized strength trap with concurrently diminished possibility of turbulence-induced damage.

Graduate scholars pursuing classes in version predictive regulate or extra in general in complex or approach keep an eye on and senior undergraduates short of a really good therapy will locate Model Predictive keep an eye on an worthwhile advisor to the cutting-edge during this vital topic. For the trainer it presents an authoritative source for the development of courses.

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Extra info for Model Predictive Control: Classical, Robust and Stochastic

Example text

7 Autonomous Prediction Dynamics 41 Fig. 2 for N = 5, 10, 15, 20, 30, 40. The polytopic sets F10 and XT are shown (dashed lines) for comparison Fig. 16b) for the quadratic constraint z k ∈ Ez with N = 20 (blue o) and the linear constraints z k ∈ Z with N = 10 (red +). Left state trajectories and the feasible set Ex for N = 20. 1 has a convex quadratic objective function and linear constraints, and is therefore a convex Quadratic Program (QP). 2 is formulated in terms of linear constraints, then this also requires the online solution of a convex QP problem.

1, Fig. 2 for values of N in the range 5–40 and compares these with the polytopic feasible set F N for N = 10. As expected, the ellipsoidal feasible sets are smaller than the polytopic feasible sets of Fig. 6, a difference of only 1 %. On the other hand 36 linear constraints are needed to define the polytopic set Z for N = 10 whereas Ez is a single (quadratic) constraint. 2, comparing the responses obtained with the ellipsoidal constraint z k ∈ Ez against the responses obtained with the linear constraint set z k ∈ Z for N = 10.

27b) for i = 0, 1, . . 1a), with the controller state at the beginning of the prediction horizon containing the degrees of freedom, ck , in predictions (Fig. 6). 27b) are equivalent to the following constraints on the initial prediction system state z k = z 0|k : F + GK GE i z k ≤ 1, i = 0, 1, . . 28) Clearly this implies an infinite number of constraints that apply across an infinite prediction horizon. However, analogously to the definition of terminal invariant sets in Sect. 28) can be constructed by determining a positively invariant set for the dynamics z k+1 = z k and constraints 34 2 MPC with No Model Uncertainty F + G K G E z k ≤ 1.

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