TRACKING OF NON-STANDARD TRAJECTORIES USING MPC METHODS WITH CONSTRAINTS HANDLING ALGORITHM

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

Designing Digital Controllers for A Controlled Plant

This paper report contains an explanation of how to design a digital controller using the Laplace Transform to z-Transform conversion method. The objectives are that the controlled system should track step input with a reasonably small steady-state error and a settling time Faster than the open-loop settling time. Furthermore, it should do so with the minimum overshoot that is reasonably possible. The main contribution is to establish the feasibility and ease of the systematic design procedure and future work will focus in more detail on applying the Sampling Theorem and deadbeat controller. There are several objectives that the controlled system should reach: 1. Track step input with a reasonably small steady-state error and a settling time which should be Faster than the open-loop settling time. 2. Gain a small overshoot that is sufficiently possible. 3. Have a systematic design procedure. A method for finding the parameters of the deadbeat controller in the MatLab environment is presented. Based on the results obtained the simulation reveals that even when the control grows by one-step, the settling time of the system response could be less than that of the deadbeat controller. The work shows that deadbeat could be a powerful analysis tool since it is possible to grab the entire dynamic easily using several samples