Understanding the final-state control from the standpoint of the model predictive control and its application to a three-dimensional trajectory control problem

In many motion control problems of mechatronic equipment, the control performance of the final-state of the control period is strictly important for positioning or settling issues. Totani and Nishimura proposed a final-state control (FSC) method using the compensation input for achieving such requirements in 1994. The FSC technique has been improved and applied to various kinds of actual mechanical motion control problems. In the same way, there is a similar method to solve these kinds of problems called model predictive control (MPC). However, the difference between FSC and MPC has not been fully clarified yet. This paper shows the relationship between the FSC and MPC methods. First, an updating-type FSC (UFSC) proposed by a part of the authors is introduced. Then, this paper analytically shows that the control input of UFSC agrees in the input of MPC under some conditions. This analysis makes clear the meaning of “updating” in the FSC technique for actual mechanical motion control applications. Moreover, this paper shows an application of the UFSC to a three-dimensional positioning problem with a fixed-wing airplane and performs numerical simulations to help the understanding the characteristics of the UFSC. Through the discussions of this paper, the characteristic of the FSC is clarified.

[1]  Toshiyuki Ohtsuka,et al.  A continuation/GMRES method for fast computation of nonlinear receding horizon control , 2004, Autom..

[2]  Mitsuo Hirata,et al.  Trajectory Design of Galvano Scanner Considering Voltage Constraint of Current Amplifier , 2017 .

[3]  Susumu Hara,et al.  Proposal of updating final-state control and its application to a connection control problem , 2014, 2014 IEEE 13th International Workshop on Advanced Motion Control (AMC).

[4]  Mitsuo Hirata,et al.  Final-State Control Using a Time-Symmetric Polynomial Input , 2012, IEEE Transactions on Control Systems Technology.

[5]  Susumu Hara,et al.  603 Study on Motion Trajectory Generation Based on Updating Final-State Control , 2016 .

[6]  Noritaka Sato,et al.  2C13 Manual motion control for connection and cooperation of plural mechanical systems(The 12th International Conference on Motion and Vibration Control) , 2014 .

[7]  Hidekazu Nishimura,et al.  Final-State Control Using Compensation Input , 1994 .

[8]  Jan Roskam,et al.  Airplane Flight Dynamics and Automatic Flight Controls , 2018 .

[9]  Kenzo Nonami,et al.  Short Track-seeking Control of Hard Disk Drives by Using Final-state Control , 2005 .

[10]  Fumitoshi Matsuno,et al.  Trajectory Generation Based on Model Predictive Control with Obstacle Avoidance between Prediction Time Steps , 2009 .

[11]  M Hirata,et al.  Final-state control using a polynomial and time-series data , 2010, 2010 APMRC.

[12]  Jan M. Maciejowski,et al.  Predictive control : with constraints , 2002 .

[13]  Kikuko Miyata,et al.  Comparison of self-moving cart motion control methods for collision avoidance problems , 2017, 2017 56th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE).

[14]  Nobutomo Matsunaga,et al.  A Design Method of Robust Control System for Three-inertia Benchmark Problem Using Model Error Compensator and Frequency Shaped Final-state Control , 2014 .

[15]  Susumu Hara,et al.  Effectiveness Evaluation of Updating Final-State Control for Automated Guided Vehicles Motion Control with Collision Avoidance Problems , 2018, IEEJ Journal of Industry Applications.