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.

Dynamic Modeling and Control Techniques for a Quadrotor

2019 ◽  
pp. 20-66
Author(s):  
Heba Elkholy ◽  
Maki K. Habib
Keyword(s):  
Pid Controller ◽  
Degrees Of Freedom ◽  
Sliding Mode ◽  
Dynamic Performance ◽  
Simulation Environment ◽  
Modeling And Control ◽  
Controller Gain ◽  
Aerial Vehicle ◽  
And Control ◽  
The Mathematical Model

This chapter presents the detailed dynamic model of a Vertical Take-Off and Landing (VTOL) type Unmanned Aerial Vehicle (UAV) known as the quadrotor. The mathematical model is derived based on Newton Euler formalism. This is followed by the development of a simulation environment on which the developed model is verified. Four control algorithms are developed to control the quadrotor's degrees of freedom: a linear PID controller, Gain Scheduling-based PID controller, nonlinear Sliding Mode, and Backstepping controllers. The performances of these controllers are compared through the developed simulation environment in terms of their dynamic performance, stability, and the effect of possible disturbances.

scholarly journals A Crane Overload Protection Controller for Blade Lifting Operation Based on Model Predictive Control

Energies ◽  
2018 ◽  
Vol 12 (1) ◽  
pp. 50 ◽  
Author(s):  
Zhengru Ren ◽  
Roger Skjetne ◽  
Zhen Gao
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Control Algorithm ◽  
Three Dimensional ◽  
Sensitivity Study ◽  
Multiple Shooting ◽  
Peak Tension ◽  
Overload Protection ◽  
Turbulent Wind Field ◽  
Blade Model

Lifting is a frequently used offshore operation. In this paper, a nonlinear model predictive control (NMPC) scheme is proposed to overcome the sudden peak tension and snap loads in the lifting wires caused by lifting speed changes in a wind turbine blade lifting operation. The objectives are to improve installation efficiency and ensure operational safety. A simplified three-dimensional crane-wire-blade model is adopted to design the optimal control algorithm. A crane winch servo motor is controlled by the NMPC controller. The direct multiple shooting approach is applied to solve the nonlinear programming problem. High-fidelity simulations of the lifting operations are implemented based on a turbulent wind field with the MarIn and CaSADi toolkit in MATLAB. By well-tuned weighting matrices, the NMPC controller is capable of preventing snap loads and axial peak tension, while ensuring efficient lifting operation. The performance is verified through a sensitivity study, compared with a typical PD controller.

scholarly journals Model-Predictive Control and Closed-Loop Stability Considerations for Nonlinear Plants Described by Local ARX-Type Models

2013 ◽  
Author(s):  
Michael E. Cholette ◽  
Dragan Djurdjanovic
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Optimization Problem ◽  
Closed Loop ◽  
Type Model ◽  
Control Input ◽  
Dual Purpose ◽  
Nonlinear Plants ◽  
And Control ◽  
Local Input

In this paper, a model-predictive control (MPC) method is detailed for the control of nonlinear systems with stability considerations. It will be assumed that the plant is described by a local input/output ARX-type model, with the control potentially included in the premise variables, which enables the control of systems that are nonlinear in both the state and control input. Additionally, for the case of set point regulation, a suboptimal controller is derived which has the dual purpose of ensuring stability and enabling finite-iteration termination of the iterative procedure used to solve the nonlinear optimization problem that is used to determine the control signal.

Dynamic Modeling and Control Techniques for a Quadrotor

2015 ◽  
pp. 408-454
Author(s):  
Heba Elkholy ◽  
Maki K. Habib
Keyword(s):  
Pid Controller ◽  
Degrees Of Freedom ◽  
Sliding Mode ◽  
Dynamic Performance ◽  
Simulation Environment ◽  
Modeling And Control ◽  
Controller Gain ◽  
Aerial Vehicle ◽  
And Control ◽  
The Mathematical Model

This chapter presents the detailed dynamic model of a Vertical Take-Off and Landing (VTOL) type Unmanned Aerial Vehicle (UAV) known as the quadrotor. The mathematical model is derived based on Newton Euler formalism. This is followed by the development of a simulation environment on which the developed model is verified. Four control algorithms are developed to control the quadrotor's degrees of freedom: a linear PID controller, Gain Scheduling-based PID controller, nonlinear Sliding Mode, and Backstepping controllers. The performances of these controllers are compared through the developed simulation environment in terms of their dynamic performance, stability, and the effect of possible disturbances.

scholarly journals Applicant hierarchical fuzzy controller for concentration control of simulated moving bed

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Chaofan Xie ◽  
Yang-jie Tang
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Pid Controller ◽  
Fuzzy Controller ◽  
Continuous Process ◽  
Simulated Moving Bed ◽  
Moving Bed ◽  
Prediction And Control ◽  
Precise Prediction ◽  
And Control

AbstractSimulated moving bed (SMB) is a kind of continuous process which can increase the efficiency of adsorbents in the adsorbent bed. It contains several sectors of flow rate, the switching time of valves and many other possible influencing variables, moreover, these parameters are highly sensitive, so it is very difficult to achieve precise prediction and control. Model predictive control and PID controller are often used in industrial system. Model predictive control needs a lot of accurate industry experience data, and PID controller depends on the selection of control parameters. Therefore, SMB needs an intelligent controller to bypass those complex mechanisms and parameter adjustment processes. This paper we propose the hierarchical fuzzy controller fuzzy controller which is applied to the SMB system to observe the final concentration. Compared with the PID and MPC controller, it is found that the hierarchical fuzzy controller can control good without knowing the system parameters too accurately.

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.

Sign in / Sign up

Export Citation Format

Share Document