scholarly journals An Autonomous Attack Guidance Method with High Aiming Precision for UCAV Based on Adaptive Fuzzy Control under Model Predictive Control Framework

2020 ◽  
Vol 10 (16) ◽  
pp. 5677
Author(s):  
Zhen Yang ◽  
Zhixiao Sun ◽  
Haiyin Piao ◽  
Yiyang Zhao ◽  
Deyun Zhou ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Adaptive Fuzzy Control ◽  
Control Variable ◽  
Superior Performance ◽  
Fire Control ◽  
Guidance Control ◽  
Error Angle ◽  
Adaptive Fuzzy ◽  
Air Combat

With its superior performance, the unmanned combat air vehicle (UCAV) will gradually become an important combat force in the future beyond-visual-range (BVR) air combat. For the problem of UCAV using the BVR air-to-air missile (AAM) to intercept the highly maneuvering aerial target, an autonomous attack guidance method with high aiming precision is proposed. In BVR air combat, the best launching conditions can be formed through the attack guidance and aiming of fighters, which can give full play to the combat effectiveness of BVR AAMs to the greatest extent. The mode of manned fighters aiming by manual control of pilots is inefficient and obviously not suitable for the autonomous UCAV. Existing attack guidance control methods have some defects such as low precision, poor timeliness, and too much reliance on manual experience when intercepting highly maneuvering targets. To address this problem, aiming error angle is calculated based on the motion model of UCAV and the aiming model of BVR attack fire control in this study, then target motion prediction information is introduced based on the designed model predictive control (MPC) framework, and the adaptive fuzzy guidance controller is designed to generate control variable. To reduce the predicted aiming error angle, the algorithm iteratively optimizes and updates the actual guidance control variable online. The simulation results show that the proposed method is very effective for solving the autonomous attack guidance problem, which has the characteristics of adaptivity, high timeliness, and high aiming precision.

Autonomous Underwater Vehicle Motion Planning via Sampling Based Model Predictive Control

2014 ◽  
Vol 670-671 ◽  
pp. 1370-1377 ◽  
Author(s):  
Lin Lin Wang ◽  
Hong Jian Wang ◽  
Li Xin Pan
Keyword(s):  
Model Predictive Control ◽  
Motion Planning ◽  
Predictive Control ◽  
Autonomous Underwater Vehicle ◽  
Angular Rate ◽  
Control Variable ◽  
Underwater Vehicle ◽  
Planning Optimization ◽  
Sampling Points ◽  
Rolling Optimization

In order to improve the ability of independent planning for AUV (Autonomous Underwater Vehicle), a new method of motion planning based on SBMPC (Sampling Based Model Predictive Control) is proposed, which is combined with model predictive control theory. Input sampling is directly made in control variable space, and sampling data is substituted into the predictive model of AUV motion. Then surge velocity and yaw angular rate in next sampling time are obtained through calculations. If predictive states are evaluated according to the performance index previously defined, optimal prediction of AUV states in next sampling can be used to realize motion planning optimization. Effects of three sampling methods (viz. uniform sampling, Halton sampling and CVT sampling) on motion planning performance are also compared in simulations. Statistical analysis demonstrates that CVT sampling points has the most uniform coverage in two-dimensional plane when amount of sampling points is the same for three methods. Simulation results show that it is effective and feasible to plan a route for AUV by using CVT sampling and rolling optimization of MPC (Model Predictive Control).

scholarly journals Active damping method for voltage source inverter-based distributed generator using multivariable finite-control-set model predictive control

2021 ◽  
Vol 12 (1) ◽  
pp. 334
Author(s):  
Jonggrist Jongudomkarn ◽  
Warayut Kampeerawat
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Active Damping ◽  
Superior Performance ◽  
Voltage Source ◽  
Lcl Filter ◽  
Grid Voltage ◽  
Grid Current ◽  
Finite Control ◽  
Control Set

Despite its advantages, the LCL filter can significantly distort the grid current and constitute a substantially more complex control issue for the grid-connected distributed generators (DGs). This paper presents an active damping approach to deal with the LCL filter's oscillation for the finite-control-set model predictive control (FCS-MPC)-three-phase voltage source inverters (VSIs)-based DG. The new approaches use the multivariable control of the inverter side's filter current and capacitor voltage to suppress the LCL filter resonance. The proposed method has been tested in steady-state and under grid voltage disturbances. The comparative study was also conducted with the existing virtual resistance active damping approaches for an FCS-MPC algorithm. The study validates the developed control schemes' superior performance and shows its ability to eliminate lower-order grid current harmonics and decrease sensitivity to grid voltage distortion.

scholarly journals Model predictive control approach for a CPAP-device

2017 ◽  
Vol 3 (2) ◽  
pp. 313-316 ◽  
Author(s):  
Mathias Scheel ◽  
Andreas Berndt ◽  
Olaf Simanski
Keyword(s):  
Respiratory Tract ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Control Variable ◽  
Upper Airway ◽  
Obstructive Sleep Apnoea Syndrome ◽  
Upper Respiratory Tract ◽  
Control Approach ◽  
Obstructive Sleep ◽  
Upper Respiratory

AbstractThe obstructive sleep apnoea syndrome (OSAS) is characterized by a collapse of the upper respiratory tract, resulting in a reduction of the blood oxygen- and an increase of the carbon dioxide (CO2) - concentration, which causes repeated sleep disruptions. The gold standard to treat the OSAS is the continuous positive airway pressure (CPAP) therapy. The continuous pressure keeps the upper airway open and prevents the collapse of the upper respiratory tract and the pharynx. Most of the available CPAP-devices cannot maintain the pressure reference [1]. In this work a model predictive control approach is provided. This control approach has the possibility to include the patient’s breathing effort into the calculation of the control variable. Therefore a patient-individualized control strategy can be developed.

Composite Path Tracking Control for Tractor–Trailer Vehicles Via Constrained Model Predictive Control and Direct Adaptive Fuzzy Techniques

2017 ◽  
Vol 139 (11) ◽  
Author(s):  
Ming Yue ◽  
Xiaoqiang Hou ◽  
Wenbin Hou
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Control Strategy ◽  
Tracking Control ◽  
Path Tracking ◽  
Reference Trajectory ◽  
Control Effect ◽  
Adaptive Fuzzy ◽  
Dynamic Controller ◽  
Fuzzy Techniques

Tractor–trailer vehicles will suffer from nonholonomic constraint, uncertain disturbance, and various physical limits, when they perform path tracking maneuver autonomously. This paper presents a composite path tracking control strategy to tackle the various problems arising from not only vehicle kinematic but also dynamic levels via two powerful control techniques. The proposed composite control structure consists of a model predictive control (MPC)-based posture controller and a direct adaptive fuzzy-based dynamic controller, respectively. The former posture controller can make the underactuated trailer midpoint follow an arbitrary reference trajectory given by the earth-fixed frame, as well as satisfying various physical limits. Meanwhile, the latter dynamic controller enables the vehicle velocities to track the desired velocities produced by the former one, and the global asymptotical convergence of dynamic controller is strictly guaranteed in the sense of Lyapunov stability theorem. The simulation results illustrate that the presented control strategy can achieve a coordinated control effect for the sophisticated tractor–trailer vehicles, thereby enhancing their movement performance in complex environments.

scholarly journals Research on SBMPC Algorithm for Path Planning of Rescue and Detection Robot

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Lin-Lin Wang ◽  
Li-Xin Pan
Keyword(s):  
Path Planning ◽  
Model Predictive Control ◽  
Cost Function ◽  
Predictive Control ◽  
Autonomous Navigation ◽  
Control Variable ◽  
Robot Motion ◽  
Rescue Work ◽  
Kinematics Model ◽  
Optimum Solution

This research aims to improve autonomous navigation of coal mine rescue and detection robot, eliminate the danger for rescuers, and enhance the security of rescue work. The concept of model predictive control is introduced into path planning of rescue and detection robot in this paper. Sampling-Based Model Predictive Control (SBMPC) algorithm is proposed basing on the construction of cost function and predictive kinematics model. Firstly, input sampling is conducted in control variable space of robot motion in order to generate candidate path planning solutions. Then, robot attitude and position in future time, which are regarded as output variables of robot motion, can be calculated through predictive kinematics model and input sampling data. The optimum solution of path planning is obtained from candidate solutions through continuous moving optimization of the defined cost function. The effects of the three sampling methods (viz., uniform sampling, Halton’s sampling, and CVT  sampling) on path planning performance are compared in simulations. Statistical analysis demonstrates that CVT sampling has the most uniform coverage in two-dimensional plane when sample amount is the same for three methods. Simulation results show that SBMPC algorithm is effective and feasible to plan a secure route for rescue and detection robot under complex environment.

scholarly journals Collision-free guidance control of multiple small UAVs based on distributed model predictive control

2017 ◽  
Vol 4 (4) ◽  
pp. 17-00117-17-00117 ◽  
Author(s):  
Satoshi SUZUKI ◽  
Masamitsu SHIBATA ◽  
Takashi SASAOKA ◽  
Kojiro IIZUKA ◽  
Takashi KAWAMURA
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Distributed Model ◽  
Guidance Control ◽  
Distributed Model Predictive Control

scholarly journals Application of multivariable model predictive control to overcome the intervariable interaction in CO2 removal process

2018 ◽  
Vol 67 ◽  
pp. 03049 ◽  
Author(s):  
Abdul Wahid ◽  
Fitriani Meizvira ◽  
Yoga Wiranoto
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Mimo System ◽  
Multiple Input Multiple Output ◽  
Control Variable ◽  
Pi Controller ◽  
Co2 Removal ◽  
Multivariable Model ◽  
Gas Treatment ◽  
Removal Process

Multivariable model predictive control (MMPC) was applied in CO2 removal process in a natural gas treatment from an industry located in Subang field, which used chemical absorption. MMPC is a variation of model predictive control (MPC) which can account for more than one control variable at once and is classified in advanced control category. MMPC is expected to give a better performance in handling the process as well as being able to overcome intervariable interaction that is prone to happen in multiple input multiple output (MIMO) system. MMPC was applied in the process to get a better process control performance compared to the one using PI controller and to make any intervariable interaction in the process more manageable. The indicator for each goal was integral square error (ISE). The result showed that identified intervariable interaction was between the pressure of gas feed in and the flow of make-up water to absorber. By using MMPC, the ISE of controller’s performance was improved from the PI-controller that was used in the plant. The improvement for ISE was 32.62% (PIC-1101) and 72.67% (FIC-1102) in the SP tracking, and 52.54% (PIC-1101) and 57.41% (FIC-1102) in the disturbance rejection. MMPC implementation also showed a better response in handling intervariable interaction in the process.

Sign in / Sign up

Export Citation Format

Share Document