Approximate Dynamic Programming based Model Predictive Control of Nonlinear systems
CoRR(2023)
摘要
This paper studies the optimal control problem for discrete-time nonlinear
systems and an approximate dynamic programming-based Model Predictive Control
(MPC) scheme is proposed for minimizing a quadratic performance measure. In the
proposed approach, the value function is approximated as a quadratic function
for which the parametric matrix is computed using a switched system approximate
of the nonlinear system. The approach is modified further using a multi-stage
scheme to improve the control accuracy and an extension to incorporate state
constraints. The MPC scheme is validated experimentally on a multi-tank system
which is modeled as a third-order nonlinear system. The experimental results
show the proposed MPC scheme results in significantly lesser online computation
compared to the Nonlinear MPC scheme.
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