scholarly journals Nonlinear Model Predictive Controller and Feasible Path Planning for Autonomous Robots

2016 ◽  
Vol 6 (1) ◽  
pp. 178-186
Author(s):  
Vu Trieu Minh
Keyword(s):  
Predictive Control ◽  
Nonlinear Model ◽  
Autonomous Robots ◽  
Open Loop ◽  
Tracking Errors ◽  
Physical Constraints ◽  
Predictive Controller ◽  
Feasible Path ◽  
The Stability ◽  
And Control

AbstractThis paper develops the nonlinear model predictive control (NMPC) algorithm to control autonomous robots tracking feasible paths generated directly from the nonlinear dynamic equations.NMPC algorithm can secure the stability of this dynamic system by imposing additional conditions on the open loop NMPC regulator. The NMPC algorithm maintains a terminal constrained region to the origin and thus, guarantees the stability of the nonlinear system. Simulations show that the NMPC algorithm can minimize the path tracking errors and control the autonomous robots tracking exactly on the feasible paths subject to the system’s physical constraints.

Position Tracking Control of Flexible Robot Joints Using Model Predictive Control

2014 ◽  
Vol 607 ◽  
pp. 799-802
Author(s):  
Nan Zhe Wei ◽  
Han Xu Sun ◽  
Qing Xuan Jia ◽  
Ping Ye
Keyword(s):  
Predictive Control ◽  
Tracking Control ◽  
Position Tracking ◽  
Harmonic Drive ◽  
Flexible Robot ◽  
Physical Constraints ◽  
Predictive Controller ◽  
Position Tracking Control ◽  
The Stability ◽  
Control Methodology

In this paper, a model predictive controller for the position tracking of flexible robot joints with harmonic drive gears is presented. The control methodology enables the drive's safety and physical constraints on torque and speed variables while guaranteeing the stability of the system. The effect of the predictive horizon on the drive performance is examined. Moreover, the influence of parameter changes is tested in this paper. Experimental results demonstrate the proposed controller is very effective in tracking position references while meeting the demanding drive constraints requirements during operation.

scholarly journals Sensitivity-Based Warmstarting for Nonlinear Model Predictive Control With Polyhedral State and Control Constraints

2020 ◽  
Vol 65 (10) ◽  
pp. 4288-4294
Author(s):  
Dominic Liao-McPherson ◽  
Marco M. Nicotra ◽  
Asen L. Dontchev ◽  
Ilya V. Kolmanovsky ◽  
Vladimir. M. Veliov
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Nonlinear Model Predictive Control ◽  
Control Constraints ◽  
And Control ◽  
State And Control Constraints

scholarly journals Nonlinear model predictive control of a boiler unit: A fault tolerant control study

2012 ◽  
Vol 22 (1) ◽  
pp. 225-237 ◽  
Author(s):  
Krzysztof Patan ◽  
Józef Korbicz
Keyword(s):  
Control System ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Fault Tolerant ◽  
Fault Tolerant Control ◽  
Nonlinear Model Predictive Control ◽  
Boiler Unit ◽  
Predictive Controller ◽  
One Step

Nonlinear model predictive control of a boiler unit: A fault tolerant control studyThis paper deals with a nonlinear model predictive control designed for a boiler unit. The predictive controller is realized by means of a recurrent neural network which acts as a one-step ahead predictor. Then, based on the neural predictor, the control law is derived solving an optimization problem. Fault tolerant properties of the proposed control system are also investigated. A set of eight faulty scenarios is prepared to verify the quality of the fault tolerant control. Based of different faulty situations, a fault compensation problem is also investigated. As the automatic control system can hide faults from being observed, the control system is equipped with a fault detection block. The fault detection module designed using the one-step ahead predictor and constant thresholds informs the user about any abnormal behaviour of the system even in the cases when faults are quickly and reliably compensated by the predictive controller.

Modeling and control through leadership of a refined flocking system

2014 ◽  
Vol 25 (02) ◽  
pp. 255-282 ◽  
Author(s):  
Alfio Borzì ◽  
Suttida Wongkaew
Keyword(s):  
Predictive Control ◽  
Control Strategy ◽  
Social Systems ◽  
Open Loop ◽  
Modeling And Control ◽  
Control Scheme ◽  
Nonlinear Conjugate Gradient ◽  
Optimality Systems ◽  
And Control ◽  
Conjugate Gradient Solver

A new refined flocking model that includes self-propelling, friction, attraction and repulsion, and alignment features is presented. This model takes into account various behavioral phenomena observed in biological and social systems. In addition, the presence of a leader is included in the system in order to develop a control strategy for the flocking model to accomplish desired objectives. Specifically, a model predictive control scheme is proposed that requires the solution of a sequence of open-loop optimality systems. An accurate Runge–Kutta scheme to discretize the optimality systems and a nonlinear conjugate gradient solver are implemented and discussed. Numerical experiments are performed that investigate the properties of the refined flocking model and demonstrate the ability of the control strategy to drive the flocking system to attain a desired target configuration and to follow a given trajectory.

scholarly journals Dual Quaternion Based Close Proximity Operation for In-Orbit Assembly via Model Predictive Control

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Chuqi Sun ◽  
Yan Xiao ◽  
Zhaowei Sun ◽  
Dong Ye
Keyword(s):  
Control System ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Coupling Effect ◽  
Suboptimal Control ◽  
Dual Quaternion ◽  
Close Proximity ◽  
The Stability ◽  
Six Degree Of Freedom ◽  
And Control

This paper studies the problem of guidance and control for autonomous in-orbit assembly. A six-degree-of-freedom (6-DOF) motion control for in-orbit assembly close proximity operation between a service satellite and a target satellite is addressed in detail. The dynamics based on dual quaternion are introduced to dispose the coupling effect between translation and rotation in a succinct frame, in which relevant perturbation and disturbance are involved. With the consideration of economical principle for fuel consume, a generic control system based on model predictive control (MPC) is then designed to generate a suboptimal control sequence for rendezvous trajectory considering actuator output saturation. The stability and robustness issues of the MPC-based control system are analyzed and proved. Numerical simulations are presented to demonstrate the effectiveness and robustness of the proposed control scheme, while additional comparisons for diverse horizons of the MPC are further conducted.

Design and control of cooperative ball juggling DELTA robots without visual guidance

Robotica ◽  
2015 ◽  
Vol 35 (2) ◽  
pp. 384-400 ◽  
Author(s):  
Zeeshan Shareef ◽  
Viktor Just ◽  
Heinrich Teichrieb ◽  
Ansgar Trächtler
Keyword(s):  
Visual System ◽  
Simulation Study ◽  
Autonomous Robots ◽  
Vital Role ◽  
Control Loop ◽  
Visual Guidance ◽  
Computationally Expensive ◽  
The Stability ◽  
And Control

SUMMARYCooperative ball juggling is one of the most difficult tasks when performed through autonomous robots. States of the ball (position and velocity) play a vital role for the stability and duration of a long rally. Cameras are normally used in ball juggling to calculate these parameters, the use of which is not only computationally expensive but also requires a lot of hardware to determine. In this paper, we propose a control loop for cooperative ball juggling using parallel DELTA robots without visual guidance. In contrast to using a visual system for ball states feedback, an observer based on the reflection laws is designed to calculate the continuous position and velocity of the ball during juggling. Besides the conventional controller blocks, the proposed control loop consists of the ball prediction and the plate striking movement generation blocks. Two controllers are designed for the stability and tracking of variable reference height of the ball during juggling: One controller calculates the velocity of the striking plate to achieve the reference height of the ball during juggling and the second controls the actuator angles. A simulation study and hardware experiments show applicability of the designed observer and validation of the proposed control loop.

Vehicle stability enhancement and rollover prevention by a nonlinear predictive control method

2018 ◽  
Vol 41 (8) ◽  
pp. 2135-2149 ◽  
Author(s):  
M. Selçuk Arslan ◽  
Mert Sever
Keyword(s):  
Predictive Control ◽  
Control Method ◽  
Vertical Load ◽  
Steering Control ◽  
Yaw Rate ◽  
Rollover Prevention ◽  
Nonlinear Predictive Control ◽  
Predictive Controller ◽  
The Stability ◽  
Stability Enhancement

In this study, a nonlinear predictive control method is developed for the active steering control of a sport utility vehicle. The method is tested on a nonlinear mathematical model of an 11-degree-of-freedom vehicle. The system performance is evaluated by considering that the control law must keep the actual yaw rate close to the desired yaw rate and minimizing the vertical load changes at each wheel. The latter is proposed for this work. The vertical load changes play an important role in the dynamics and the stability of the system. The effectiveness of the control method is demonstrated through numerical simulation by using a vehicle model that includes three case studies: rapid lane change at low and high velocities and the fishhook manoeuvre. The results show that the stability of the vehicle is maintained and its rollover propensity is decreased. In addition, the proposed controller is compared with a well-known linear model predictive controller.

Model Predictive Control for Gas Turbine Engines

2018 ◽  
Author(s):  
Amit Pandey ◽  
Maurício de Oliveira ◽  
Robert H. Moroto
Keyword(s):  
Model Predictive Control ◽  
Gas Turbine ◽  
Predictive Control ◽  
Closed Loop ◽  
Open Loop ◽  
Turbine Engines ◽  
Gas Turbine Engines ◽  
Physical Constraints ◽  
Classical Control ◽  
Predictive Controllers

The use of Model Predictive Control (MPC) is commonplace in many industrial applications. The anticipative nature of MPC and the inclusion of physical constraints into the control framework presents many advantages over classical control strategies. Despite these advantages, obtaining an accurate open-loop model of the underlying process is often a difficult and time consuming process. In this paper, a methodology is introduced to identify linear open-loop models of gas turbine engines from closed-loop data. The closed-loop data can be obtained by any sufficiently informative experiment from a plant in operation or simulation. We present simulation results here. These open-loop models are then used in the design of model predictive controllers at a number of operating points of the turbine. The predictive controllers we designed include physical constraints on the fuel and air flow into the turbine. The performance of these predictive controllers is compared in simulation against existing classical control techniques in a number of typical operating scenarios including off loads, on loads and set point changes.

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