Use parallel computing and optimize problem formulation.

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Boosting the Efficiency of SNOPT Solver for Rapid MPC Trajectory Optimization

Sequential Quadratic Programming (SQP) is a well-known optimization method for model predictive control (MPC) problems. It utilizes a quadratic programming (QP) solver to achieve iteratively improving solutions for non-linear MPC problems. However, the efficiency of the SQP method depends greatly on the efficiency of the QP solver used. In this article, we will focus on improving the efficiency of the SQP method by enhancing the efficiency of the optimizer.

Maximizing Performance of SNOPT Solver in MPC Trajectory Optimization

MPC (Model Predictive Control) is a popular approach to control systems in various fields, including aerospace, robotics, and process industries. In MPC, a mathematical model of the system is used to predict its future behavior, and an optimization problem is solved to compute the optimal control inputs that minimize a cost function subject to constraints. The optimization problem is usually solved using numerical optimization algorithms, such as SNOPT (Sparse Nonlinear OPTimizer).

Optimizing the Performance of SNOPT Solver for High-Speed MPC Trajectory Planning

MPC or Model Predictive Control involves the use of mathematical optimization techniques to predict future system behavior and plan trajectories that optimize a specific objective. One common formulation of MPC involves using a nonlinear program solver to find the optimal sequence of system inputs that will satisfy a set of constraints and minimize cost.

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