TRACKING OF NON-STANDARD TRAJECTORIES USING MPC METHODS WITH CONSTRAINTS HANDLING ALGORITHM
DOI:
https://doi.org/10.37943/AEPO1273Keywords:
Model-Based Predictive Control, MPC, SSTO, trajectory tracking, finite horizon, PID, feedback loop, state-space models, Hessian matrix, cost function, LQR problemAbstract
In recent decades, a Model-Based Predictive Control (MPC) has revealed its dominance over other control methods such as having an ability of constraints handling and input optimization in terms of the value function. However, the complexity of the realization of the MPC algorithm on real mechatronic systems remains one of the major challenges. Traditional predictive control approaches are based on zero regulation or a step change. Nevertheless, more complicated systems still exist that need to track setpoint trajectories.
Currently, there is an active development of robotics and the creation of transport networks of movement without human participation. Therefore, the issue of programming the given trajectories of vehicles is relevant. In this article, authors reveal the alternative solution for tracking non-standard trajectories in spheres such as robotics, IT in mechatronics, etc., that could be used in self-driving cars, drones, rockets, robot arms and any other automized systems in factories.
The ability of Model-Based Predictive Control (MPC) such as the constraints handling and optimization of input in terms of the value function makes it extremely attractive in the industry. Nevertheless, the complexity of implementation of MPC algorithm on real mechatronic systems remains one of the main challenges.
Secondly, common predictive control algorithms are based on the regulation approach or a simple step shift. However, there exist systems that are more complicated where a setpoint to be tracked is given in the form of trajectories. In this project, there were made several modifications in order to improve an MPC algorithm to make better use of information about the trajectories.
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