scholarly journals Clothoid curve optimization for parallel parking path planning and tracking control

2021 ◽  
Vol 2093 (1) ◽  
pp. 012004
Author(s):  
Feiying Zhao ◽  
Kaiqi Huang
Keyword(s):  
Path Planning ◽  
State Equation ◽  
Error Model ◽  
Reference Trajectory ◽  
Model Predictive Controller ◽  
Parallel Parking ◽  
Predictive Controller ◽  
Heading Angle ◽  
Straight Lines ◽  
Curve Optimization

Abstract In order to solve the problem of abrupt curvature change at the connection between arcs and straight lines in circle-line-circle (C-L-C) combined parallel parking paths, curvature optimization was carried out by using a cycloid curve, and the trajectory curvature and heading Angle were made to meet the pose requirements of parallel parking. By establishing the kinematics model state equation and taking the optimized C-L-C trajectory as the reference trajectory, the error model was obtained. The model predictive controller based on the error model was designed, and the effectiveness of the path planning and model predictive controller was verified on Carsim and Matlab/Simulink co-simulation platform.

Ship Motion Control System for Replenishment Operation

2016 ◽  
Vol 817 ◽  
pp. 214-222
Author(s):  
Witold Gierusz ◽  
Anna Miller
Keyword(s):  
Control System ◽  
Motion Control ◽  
Main Part ◽  
Ship Motion ◽  
The Other ◽  
Reference Trajectory ◽  
Model Predictive Controller ◽  
Motion Control System ◽  
Parallel Motion ◽  
Predictive Controller

Underway Replenishment is a procedure whose importance is rising in shipping. It is applied both to the naval and civil vessels. That is the reason why research in this area was undertaken. In this paper idea of the ship motion control system for replenishment operations was presented. The outline of the system is described in a detail way. This system incorporates Model Predictive Controller as a main part of the proposed algorithm. The other important part of the control system is a reference trajectory for the approaching ship generation. Conducted computer simulations prove that there is a possibility to synthesize MPC controller to maintain the pair of ships parallel motion during the UNREP operation.

Control Parameters Tuning Method of Nonlinear Model Predictive Controller Based on Quantitatively Analyzing

2016 ◽  
Vol 28 (5) ◽  
pp. 695-701 ◽  
Author(s):  
Tomohiro Henmi ◽  
Keyword(s):  
Nonlinear Model ◽  
Tracking Control ◽  
Parameter Tuning ◽  
Reference Trajectory ◽  
Control Input ◽  
Control Parameters ◽  
Model Predictive Controller ◽  
Tuning Method ◽  
Predictive Controller ◽  
System Output

[abstFig src='/00280005/11.jpg' width='300' text='ANMPC controller' ] The parameter-tuning method we discuss is for an Adaptive Nonlinear Model Predictive Controller (ANMPC). The MPC is optimization-based controller and decides control input to realize system output that tracks a reference trajectory through “optimal computation.” The reference trajectory is ideal trajectory of system output to converge on a desired value, i.e. controlled system performance depends on the reference trajectory. As a MPC controller which applies to the nonlinear systems, our group has already proposed an adaptive nonlinear MPC (ANMPC) for a tracking control problem of nonlinear two-link planar manipulators. This ANMPC uses a new reference trajectory having control parameters that must be tuned based on the desired controlled system’s responses and properties. To reduce troublesome parameter tuning, we propose new parameter-tuning method for ANMPC by a quantitative analysis of the relationship between a system’s behavior and ANMPC parameters. Numerically simulating the two-link nonlinear manipulator’s tracking control under various conditions demonstrates that proposed tuning method tunes the ANMPC effectively.

Three-layer Aircraft Control: Path-planning, Guidance, Model Predictive Controller as Autopilot

2010 ◽  
Vol 43 (15) ◽  
pp. 81-86
Author(s):  
A. Voelker ◽  
M. Dördelmann ◽  
K. Kouramas ◽  
E.N. Pistikopoulos ◽  
M. Shanmugavel ◽  
...  
Keyword(s):  
Path Planning ◽  
Aircraft Control ◽  
Model Predictive Controller ◽  
Predictive Controller ◽  
Guidance Model

A Quantum-behaved Pigeon-Inspired Optimization approach to Explicit Nonlinear Model Predictive Controller for quadrotor

2018 ◽  
Vol 11 (1) ◽  
pp. 47-63 ◽  
Author(s):  
Ning Xian ◽  
Zhilong Chen
Keyword(s):  
Nonlinear Model ◽  
Discrete Systems ◽  
Linear Interpolation ◽  
State Equation ◽  
Interpolation Error ◽  
Optimization Approach ◽  
Model Predictive Controller ◽  
Content Type ◽  
Predictive Controller ◽  
Sample Points

Purpose The purpose of this paper is to simplify the Explicit Nonlinear Model Predictive Controller (ENMPC) by linearizing the trajectory with Quantum-behaved Pigeon-Inspired Optimization (QPIO). Design/methodology/approach The paper deduces the nonlinear model of the quadrotor and uses the ENMPC to track the trajectory. Since the ENMPC has high demand for the state equation, the trajectory needed to be differentiated many times. When the trajectory is complicate or discontinuous, QPIO is proposed to linearize the trajectory. Then the linearized trajectory will be used in the ENMPC. Findings Applying the QPIO algorithm allows the unequal distance sample points to be acquired to linearize the trajectory. Comparing with the equidistant linear interpolation, the linear interpolation error will be smaller. Practical implications Small-sized quadrotors were adopted in this research to simplify the model. The model is supposed to be accurate and differentiable to meet the requirements of ENMPC. Originality/value Traditionally, the quadrotor model was usually linearized in the research. In this paper, the quadrotor model was kept nonlinear and the trajectory will be linearized instead. Unequal distance sample points were utilized to linearize the trajectory. In this way, the authors can get a smaller interpolation error. This method can also be applied to discrete systems to construct the interpolation for trajectory tracking.

Enlarging the region of stability in robust model predictive controller based on dual-mode control

2021 ◽  
pp. 014233122110123
Author(s):  
Fatemeh Khani ◽  
Mohammad Haeri
Keyword(s):  
Stability Region ◽  
Linear Matrix ◽  
Input State ◽  
Dual Mode ◽  
Model Predictive Controller ◽  
Mode Control ◽  
Robust Model ◽  
Output Constraints ◽  
Predictive Controller ◽  
The Stability

Industrial processes are inherently nonlinear with input, state, and output constraints. A proper control system should handle these challenging control problems over a large operating region. The robust model predictive controller (RMPC) could be an linear matrix inequality (LMI)-based method that estimates stability region of the closed-loop system as an ellipsoid. This presentation, however, restricts confident application of the controller on systems with large operating regions. In this paper, a dual-mode control strategy is employed to enlarge the stability region in first place and then, trajectory reversing method (TRM) is employed to approximate the stability region more accurately. Finally, the effectiveness of the proposed scheme is illustrated on a continuous stirred tank reactor (CSTR) process.

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