Approximate Dynamic Programming based Model Predictive Control of Nonlinear systems
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Model Predictive ControlSimilar Papers 제목 키워드 기반
Deep Learning Explicit Differentiable Predictive Control Laws for Buildings
We present a differentiable predictive control (DPC) methodology for learning constrained control laws for unknown nonlinear systems. DPC poses an approximate solution to multiparametric programming problems emerging fro…
Deep LearningModel Predictive ControlLearning the cost-to-go for mixed-integer nonlinear model predictive control
Application of nonlinear model predictive control (NMPC) to problems with hybrid dynamical systems, disjoint constraints, or discrete controls often results in mixed-integer formulations with both continuous and discrete…
Model Predictive ControlPredictionAdaptive Output-Feedback Model Predictive Control of Hammerstein Systems with Unknown Linear Dynamics
This paper considers model predictive control of Hammerstein systems, where the linear dynamics are a priori unknown and the input nonlinearity is known. Predictive cost adaptive control (PCAC) is applied to this system …
Model Predictive ControlReservoir Predictive Path Integral Control for Unknown Nonlinear Dynamics
Neural networks have found extensive application in data-driven control of nonlinear dynamical systems, yet fast online identification and control of unknown dynamics remain central challenges. To meet these challenges, …
Approximate infinite-horizon predictive control
Predictive control is frequently used for control problems involving constraints. Being an optimization based technique utilizing a user specified so-called stage cost, performance properties, i.e., bounds on the infinit…
Model-based Reinforcement Learning