Instant MPC for linear systems and dissipativity-based stability analysis
For control engineers, iMPC offers a computationally lighter alternative to standard MPC for linear systems, though it is an incremental improvement.
The paper introduces 'instant' model predictive control (iMPC) for linear systems, which determines control actions via an optimization process rather than the optimizer, reducing computational burden while emulating conventional MPC.
This letter is devoted to the concept of ``instant'' model predictive control (iMPC) for linear systems. An optimization problem is formulated to express the finite-time constrained optimal regulation control, like conventional MPC. Then, iMPC determines the control action based on the optimization process rather than the optimizer, unlike MPC. The iMPC concept is realized by a continuous-time dynamic algorithm of solving the optimization; the primal-dual gradient algorithm is directly implemented as a dynamic controller. On the basis of the dissipativity evaluation of the algorithm, the stability of the control system is analyzed. Finally, a numerical experiment is performed in order to demonstrate that iMPC emulates MPC and to show its less computational burden.