Nonlinear Model Predictive Tracking Control for Nonholonomic AGV Based on Bio-Inspired Neurodynamics

2013 ◽  
Author(s):  
Xiao-Hong Yin ◽  
Can Yang
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Control Method ◽  
Kinematic Model ◽  
Input Saturation ◽  
Stability Control ◽  
Control Approach ◽  
Velocity Jump ◽  
Control Principle

Considering nonholonomic constraint and input saturation of the Automatic Guided Vehicle (AGV) kinematic model, in the present work the nonlinear model predictive control was applied and a combined tracking/stability control approach was proposed. In addition, the bio-inspired neurodynamics model was applied to generate smooth forward velocities so that the sharp velocity jump can be overcome by the proposed controller. Specifically, an optimal sub-control method consisting of cost function and constraints were obtained based on the model predictive control principle, and a terminal sub-control method was designed to make the control system stable. Finally, the effectiveness of the proposed control strategy was demonstrated through comparison studies with simulations.

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.

Double layered nonlinear model predictive control method based on the T-S fuzzy model

2017 ◽  
Author(s):  
Hongbin Cai ◽  
Ping Li ◽  
Chengli Su ◽  
Jiangtao Cao ◽  
Zongke Shi
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Control Method ◽  
Fuzzy Model ◽  
Nonlinear Model Predictive Control

Chaos Control of Atomic Force Microscope System Using Nonlinear Model Predictive Control

2016 ◽  
Vol 33 (3) ◽  
pp. 405-415 ◽  
Author(s):  
J. Keighobadi ◽  
J. Faraji ◽  
S. Rafatnia
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Chaos Control ◽  
Control Method ◽  
Nonlinear Model Predictive Control ◽  
Stable Range ◽  
Operational Conditions ◽  
Simulation Results ◽  
Micro Cantilever

AbstractOwing to robust and optimal specification, model predictive control method has received wide attentions over recent years. Since in certain operational conditions, an Atomic/scanning Force Microscope (AFM) shows chaos behavior, the chaos feedback control of the AFM system is considered. According to the nonlinear model of forces interacting between the tip of micro cantilever and the substrate of AFM; the nonlinear control methods are proposed. In the paper, the chaos control of a micro cantilever AFM based on the nonlinear model predictive control (NMPC) technique is presented. Through software simulation results, the effectiveness of the designed NMPC of the AFM is assessed. The simulation results together with analytical stability proofs indicate that the proposed method is effective in keeping the system in a stable range.

Tracking control of multiple unmanned aerial vehicles incorporating disturbance observer and model predictive approach

2019 ◽  
Vol 42 (5) ◽  
pp. 951-964 ◽  
Author(s):  
Boyang Zhang ◽  
Xiuxia Sun ◽  
Shuguang Liu ◽  
Xiongfeng Deng
Keyword(s):  
Model Predictive Control ◽  
Unmanned Aerial Vehicle ◽  
Predictive Control ◽  
Disturbance Observer ◽  
Control Method ◽  
Differential Evolution Algorithm ◽  
Reference Trajectory ◽  
Control Approach ◽  
Unknown Disturbances ◽  
Aerial Vehicle

This paper studies the disturbance observer-based model predictive control approach to deal with the unmanned aerial vehicle formation flight with unknown disturbances. The distributed control problem for a class of multiple unmanned aerial vehicle systems with reference trajectory tracking and disturbance rejection is formulated. Firstly, a local distributed controller is designed by using the model predictive control method to achieve stable tracking, where the local optimization problem is solved by an adaptive differential evolution algorithm. Then, a feedforward compensation controller is introduced by using a disturbance observer to estimate and compensate disturbances, and improve the ability of anti-interference. Besides, the stability of the proposed composite controller is analyzed as well. Finally, the simulation examples are provided to illustrate the validity of proposed control structure.

scholarly journals Path following of underactuated surface ships based on model predictive control with neural network

2020 ◽  
Vol 17 (4) ◽  
pp. 172988142094595
Author(s):  
Ronghui Li ◽  
Ji Huang ◽  
Xinxiang Pan ◽  
Qionglei Hu ◽  
Zhenkai Huang
Keyword(s):  
Neural Network ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Input Saturation ◽  
Path Following ◽  
Environmental Disturbances ◽  
Control Approach ◽  
Underactuated Ship ◽  
Mmg Model ◽  
The Mathematical Model

A model predictive control approach is proposed for path following of underactuated surface ships with input saturation, parameters uncertainties, and environmental disturbances. An Euler iterative algorithm is used to reduce the calculation amount of model predictive control. The matter of input saturation is addressed naturally and flexibly by taking advantage of model predictive control. The mathematical model group (MMG) model as the internal model improves the control accuracy. A radial basis function neural network is also applied to compensate the total unknowns including parameters uncertainties and environmental disturbances. The numerical simulation results show that the designed controller can force an underactuated ship to follow the desired path accurately in the case of input saturation and time-varying environmental disturbances including wind, current, and wave.

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