Semi-active predictive control of non-linear structures with controlled stiffness devices and friction dampers

2004 ◽  
Vol 13 (2) ◽  
pp. 165-178 ◽  
Author(s):  
Yuri Ribakov
Keyword(s):  
Predictive Control ◽  
Linear Structures ◽  
Friction Dampers ◽  
Non Linear

scholarly journals Dynamic analysis of space-related linear and non-linear structures

1992 ◽  
Vol 44 (5) ◽  
pp. 1145-1148 ◽  
Author(s):  
P.A. Bosela ◽  
F.J. Shaker ◽  
D.G. Fertis
Keyword(s):  
Dynamic Analysis ◽  
Linear Structures ◽  
Non Linear

Non-linear model based predictive control

1999 ◽  
Vol 72 (10) ◽  
pp. 919-928 ◽  
Author(s):  
B. Kouvaritakis ◽  
M. Cannon ◽  
J. A. Rossiter
Keyword(s):  
Linear Model ◽  
Predictive Control ◽  
Model Based ◽  
Non Linear

A tube-based robust MPC for duty-cycled rotation needle steering systems with bounded disturbances

2021 ◽  
pp. 014233122110430
Author(s):  
Zhi Qi ◽  
Qianyue Luo ◽  
Hui Zhang
Keyword(s):  
Model Predictive Control ◽  
Linear Model ◽  
Predictive Control ◽  
Control Method ◽  
Controller Design ◽  
Control Approach ◽  
Bounded State ◽  
Non Linear ◽  
System States ◽  
Trajectory Tracking Controller

In this paper, we aim to design the trajectory tracking controller for variable curvature duty-cycled rotation flexible needles with a tube-based model predictive control approach. A non-linear model is adopted according to the kinematic characteristics of the flexible needle and a bicycle method. The modeling error is assumed to be an unknown but bounded disturbance. The non-linear model is transformed to a discrete time form for the benefit of predictive controller design. From the application perspective, the flexible needle system states and control inputs are bounded within a robust invariant set when subject to disturbance. Then, the tube-based model predictive control is designed for the system with bounded state vector and inputs. Finally, the simulation experiments are carried out with tube-based model predictive control and proportional integral derivative controller based on the particle swarm optimisation method. The simulation results show that the tube-based model predictive control method is more robust and it leads to much smaller tracking errors in different scenarios.

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