Abstract:
In order to reduce the computational complexity of solving Quadratic Programming (QP), related to linear Model Predictive Control (MPC), a new approximated formulation of...Show MoreMetadata
Abstract:
In order to reduce the computational complexity of solving Quadratic Programming (QP), related to linear Model Predictive Control (MPC), a new approximated formulation of the QP with simple bounds is introduced in this paper. This formulation is based on the idea not to consider model dynamics as a hard constraint but rather modify the objective function of MPC by penalty to capture the violation of model dynamics. The system dynamics is usually uncertain and then it does not make sense to design the control law based on the exact model. Furthermore, the specific sparse structure of the approximated simple bounded QP formulation of the MPC problem is exploited in the new type of combined gradient/Newton step projection algorithm with linear complexity of each iteration with respect to prediction horizon. It is shown by examples that the proposed method is faster on tested problem than other state-of-the-art solvers while retaining a high performance level.
Published in: 22nd Mediterranean Conference on Control and Automation
Date of Conference: 16-19 June 2014
Date Added to IEEE Xplore: 20 November 2014
ISBN Information:
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- IEEE Keywords
- Index Terms
- Quadratic Programming ,
- System Dynamics ,
- Objective Function ,
- Optimal Control ,
- Model Predictive Control ,
- Prediction Horizon ,
- Hard Constraints ,
- Projection Algorithm ,
- Iteration Complexity ,
- Model Predictive Control Problem ,
- Optimization Problem ,
- Computation Time ,
- Numerical Experiments ,
- Singular Value Decomposition ,
- Work Settings ,
- Feasible Set ,
- Penalty Parameter ,
- Computational Demands ,
- Interior Point Method ,
- Cholesky Decomposition ,
- Input Constraints ,
- Gradient Projection ,
- Gradient Projection Method ,
- Quadratic Programming Problem ,
- Number Of Optimization Variables ,
- Application Of Step ,
- Line Search ,
- Active Constraints ,
- State Constraints ,
- Linear Weight
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Quadratic Programming ,
- System Dynamics ,
- Objective Function ,
- Optimal Control ,
- Model Predictive Control ,
- Prediction Horizon ,
- Hard Constraints ,
- Projection Algorithm ,
- Iteration Complexity ,
- Model Predictive Control Problem ,
- Optimization Problem ,
- Computation Time ,
- Numerical Experiments ,
- Singular Value Decomposition ,
- Work Settings ,
- Feasible Set ,
- Penalty Parameter ,
- Computational Demands ,
- Interior Point Method ,
- Cholesky Decomposition ,
- Input Constraints ,
- Gradient Projection ,
- Gradient Projection Method ,
- Quadratic Programming Problem ,
- Number Of Optimization Variables ,
- Application Of Step ,
- Line Search ,
- Active Constraints ,
- State Constraints ,
- Linear Weight