scholarly journals Modeling and Control of Soft Robots Using the Koopman Operator and Model Predictive Control

2019 ◽  
Author(s):  
Daniel Bruder ◽  
Brent Gillespie ◽  
C. David Remy ◽  
Ram Vasudevan
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Soft Robots ◽  
Modeling And Control ◽  
Koopman Operator ◽  
And Control

scholarly journals Modeling and Control of Crane Overload Protection During Marine Lifting Operation Based on Model Predictive Control

2017 ◽  
Author(s):  
Zhengru Ren ◽  
Roger Skjetne ◽  
Zhen Gao
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Pid Controller ◽  
Degrees Of Freedom ◽  
Multiple Shooting ◽  
Control Input ◽  
Lumped Mass ◽  
Modeling And Control ◽  
Overload Protection ◽  
And Control

This paper deals with a nonlinear model predictive control (NMPC) scheme for a winch servo motor to overcome the sudden peak tension in the lifting wire caused by a lumped-mass payload at the beginning of a lifting off or a lowering operation. The crane-wire-payload system is modeled in 3 degrees of freedom with the Newton-Euler approach. Direct multiple shooting and real-time iteration (RTI) scheme are employed to provide feedback control input to the winch servo. Simulations are implemented with MATLAB and CaSADi toolkit. By well tuning the weighting matrices, the NMPC controller can reduce the snatch loads in the lifting wire and the winch loads simultaneously. A comparative study with a PID controller is conducted to verify its performance.

Simulation Studies Comparing Feedback Predictive Control to Model Predictive Control for Unmeasured Disturbances in the Artificial Pancreas Application

2019 ◽  
Vol 141 (9) ◽  
Author(s):  
Yong Mei ◽  
Trinh Huynh ◽  
Rachel Khor ◽  
Derrick K. Rollins
Keyword(s):  
Standard Deviation ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Artificial Pancreas ◽  
Simulation Studies ◽  
Modeling And Control ◽  
Carbohydrate Consumption ◽  
And Control ◽  
Measured Input

The artificial pancreas (AP) is an electro-mechanical device to control glucose (G) levels in the blood for people with diabetes using mathematical modeling and control system technology. There are many variables not measured and modeled by these devices that affect G levels. This work evaluates the effectiveness of two control systems for the case where critical inputs are unmeasured. This work compares and evaluates two predictive feedback control (FBC) algorithms in two unmeasured input studies. In the first study, the process is a dynamic transfer function model with one measured input variable and one unmeasured input variable. The process for the second study is a diabetes simulator with insulin feed rate (IFR) measured and carbohydrate consumption (CC) unmeasured. The feedback predictive control (FBPC) approach achieved much better control performance than model predictive control (MPC) in both studies. In the first study, MPC was shown to get worse as the process lag increases but FBPC was unaffected by process lag. In the diabetes simulation study, for five surrogate type 1 diabetes subjects, the standard deviation of G about its mean (standard deviation) (i.e., the set point) was 133% larger for MPC relative to FBPC. For FBPC, its standard deviation was less than 10% larger for unmeasured CC versus measured CC. Thus, FBPC appears to be a more effective AP control algorithm than MPC for unmeasured disturbances and may not perform much worse in practice when CC is measured versus when it is unmeasured since CC can be very inaccurate in real situations.

An Effective Modeling and Control Strategy for Supply Chain Design and Planning

2011 ◽  
Vol 467-469 ◽  
pp. 1132-1135
Author(s):  
Hai Dong ◽  
Wei Ling Zhao ◽  
Yan Ping Li
Keyword(s):  
Supply Chain ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Control Strategy ◽  
Planning Process ◽  
Supply Chain Design ◽  
Supply Chain Network ◽  
Modeling And Control ◽  
Work Model ◽  
And Control

The dynamics and uncertainty of the business and the market makes difficult to coordinate the activities of a supply chain. Therefore, it is important to review systematically and to take into account the variability in the planning formulation in order to manage a supply chain network efficiently. A novel stochastic multi-period design and planning MILP model of a multiechelon supply chain network is used as a predictive model in this work. Model predictive control is presented as a way to manage supply chain in the presence of uncertainty by incorporating unforeseen events into the planning process. Illustrative example shows control strategy based on model predictive control framework is effective in the supply chain network design and planning.

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