Fuzzy Adaptive Control Law for Trajectory Tracking Based on a Fuzzy Adaptive Neural PID Controller of a Multi-rotor Unmanned Aerial Vehicle

Abigail María Elena Ramírez Mendoza,Wen Yu 제어·로봇·시스템학회 2023 International Journal of Control, Automation, and Vol.21 No.2

This article presents a fuzzy adaptive control law (FACL) designed for tracking the trajectory of a low-scale unmanned aerial vehicle (UAV), based on a new fuzzy adaptive neural proportional integral derivative (FANPID) controller. FACL estimates the angles of rotation, if the reference trajectory is proposed, applying the adaptivity of the new FANPID-Lyapunov controller. UAV parameters were previously identified using the fuzzy adaptive neurons (FAN) method and experimental aerodynamic data. FANPID-Lyapunov controller optimizes trajectory tracking and stability analysis is performed. The FACL simulation results obtained in Matlab®/Simulink show the effectiveness, adaptivity and optimization of the flight control system, because it self-tunes the angles satisfactorily, adapts the gains and parameter for the FANPID-Lyapunov-Fuzzy controller, and reduces the error considerably compared to the controllers PID-Fixed gains, PID-Fuzzy adaptive gains, PID-Lyapunov-Fixed gains, and FOPID-Lyapunov-Fuzzy adaptive gains and parameters.

Enhanced Robust Motion Tracking Control for 6 Degree-of-freedom Industrial Assembly Robot with Disturbance Adaption

Li Pan,Tao Gao,Fang Xu,Libin Zhang 제어·로봇·시스템학회 2018 International Journal of Control, Automation, and Vol.16 No.2

Industrial assembly robots are designed to enable accurate and repeatable tracking of the positions and orientations of the robot’s end-effector and are hence often required to perform complex tasks within uncertain environments. Therefore, trajectory tracking control is vital to the wide range of applications of industrial robotic systems. This paper presents an enhanced robust motion tracking controller with disturbance adaption for trajectory tracking control of industrial assembly robot. The dynamics of the industrial assembly robotic system is formulated in the working space of the end-effector and the tracking control problem is then formulated. The enhanced robust motion tracking control is synthesized by using disturbance adaption and modified iterative control terms, which is a refined version of conventional robust adaptive control with only disturbance rejection. The capability and effectiveness of the enhanced robust motion tracking control have been evaluated based on an industrial robotic platform. The comparative results clearly show that the proposed control can be used to better minimize the trajectory tracking errors in finite time as compared with conventional proportional derivative control.

Distributed Adaptive Robust Tracking and Model Matching Control with Actuator Faults and Interconnection Failures

Xiao-Zheng Jin,Guang-Hong Yang 제어·로봇·시스템학회 2009 International Journal of Control, Automation, and Vol.7 No.5

: In this paper, direct adaptive-state feedback control schemes are developed to solve the robust tracking and model matching control problem for a class of distributed large scale systems with actuator faults, faulty and perturbed interconnection links, and external disturbances. The adaptation laws are proposed to update the controller parameters on-line when all the eventual faults, the upper bounds of perturbations and disturbances are assumed to be unknown. Then a class of distributed state feedback controllers is constructed to automatically compensate the fault, perturbation and disturbance effects based on the information from adaptive schemes. The proposed distributed adaptive tracking controller can ensure that the resulting adaptive closed-loop large-scale system is stable and the tracking error decreases asymptotically to zero in the presence of uncertain faults of actuators and interconnections, perturbations in interconnection channels, and disturbances. The proposed adaptive design technique is finally evaluated in the light of a simulation example.

Model-free Adaptive Dynamic Programming Based Near-optimal Decentralized Tracking Control of Reconfigurable Manipulators

Bo Zhao,Yuanchun Li 제어·로봇·시스템학회 2018 International Journal of Control, Automation, and Vol.16 No.2

In this paper, a model-free near-optimal decentralized tracking control (DTC) scheme is developed for reconfigurable manipulators via adaptive dynamic programming algorithm. The proposed controller can be divided into two parts, namely local desired controller and local tracking error controller. In order to remove the normboundedness assumption of interconnections, desired states of coupled subsystems are employed to substitute their actual states. Using the local input/output data, the unknown subsystem dynamics of reconfigurable manipulators can be identified by constructing local neural network (NN) identifiers. With the help of the identified dynamics, the local desired control can be derived directly with corresponding desired states. Then, for tracking error subsystems, the local tracking error control is investigated by the approximate improved local cost function via local critic NN and the identified input gain matrix. To overcome the overall error caused by the substitution, identification and critic NN approximation, a robust compensation is added to construct the improved local cost function that reflects the overall error, regulation and control simultaneously. Therefore, the closed-loop tracking system can be guaranteed to be asymptotically stable via Lyapunov stability theorem. Two 2-degree of freedom reconfigurable manipulators with different configurations are employed to demonstrate the effectiveness of the proposed modelfree near-optimal DTC scheme.

Adaptive Fractional Order PID Controller Based MPPT for PV Connected Grid System Under Changing Weather Conditions

Nasir Ali,Rasool Imran,Sibtain Daud,Kamran Raheel 대한전기학회 2021 Journal of Electrical Engineering & Technology Vol.16 No.5

This paper is presenting an Adaptive Fractional Order PID (AFOPID) controller for PV connected grid system. The proposed controller is designed to harvest maximum power from the PV source. AFOPID controller contain the property of conventional PID controller, where the controller contains an adaptive property to optimize gain parameter on the basis of generator and grid side parameter in view. In the suggested work, the AFOPID controller imparted with the characteristic to get tune with particle swarm optimization to track maximum power point tracking, dc link voltage, and current control and quadrature axis modeling. Furthermore, the current control functionalities are performed through an AFOPID, where the controlling parameters are updated by measured error at every instant. The aim of this research is to attain maximum power from the PV source under varying weather conditions with minimum total harmonic distortion, which validate the performance of the proposed controller. The proposed work is compared with, FOPID, FLC, PI, GA and ACO tune FOPID controllers.

Policy Iteration Algorithm Based Fault Tolerant Tracking Control: An Implementation on Reconfigurable Manipulators

Li, Yuanchun,Xia, Hongbing,Zhao, Bo The Korean Institute of Electrical Engineers 2018 Journal of Electrical Engineering & Technology Vol.13 No.4

This paper proposes a novel fault tolerant tracking control (FTTC) scheme for a class of nonlinear systems with actuator failures based on the policy iteration (PI) algorithm and the adaptive fault observer. The estimated actuator failure from an adaptive fault observer is utilized to construct an improved performance index function that reflects the failure, regulation and control simultaneously. With the help of the proper performance index function, the FTTC problem can be transformed into an optimal control problem. The fault tolerant tracking controller is composed of the desired controller and the approximated optimal feedback one. The desired controller is developed to maintain the desired tracking performance at the steady-state, and the approximated optimal feedback controller is designed to stabilize the tracking error dynamics in an optimal manner. By establishing a critic neural network, the PI algorithm is utilized to solve the Hamilton-Jacobi-Bellman equation, and then the approximated optimal feedback controller can be derived. Based on Lyapunov technique, the uniform ultimate boundedness of the closed-loop system is proven. The proposed FTTC scheme is applied to reconfigurable manipulators with two degree of freedoms in order to test the effectiveness via numerical simulation.

Output-feedback Robust Tracking Control of Uncertain Systems via Adaptive Learning

Jun Zhao,Yongfeng Lv 제어·로봇·시스템학회 2023 International Journal of Control, Automation, and Vol.21 No.4

This paper presents an adaptive learning method to achieve the output-feedback robust tracking control of systems with uncertain dynamics, which uses the techniques developed for optimal control. An augmented system is first constructed using the system state and desired output trajectory. Then, the robust tracking control problem is equivalent to the optimal tracking control problem with an appropriate cost function. To design the output-feedback optimal tracking control, an output tracking algebraic Riccati equation (OTARE) is then constructed, which can be used in the online learning process. To obtain the solution of the derived OTARE, an online adaptive learning method is proposed, where the input gain matrix is removed. In this learning algorithm, only the system output information is required and the observers widely used in the output-feedback optimal control design are removed. Simulations based on the power system are given to test the proposed method.

Backstepping Based Trajectory Tracking Control of a TBM Steel Arch Splicing Manipulator

Yuxi Chen,Guofang Gong,Xinghai Zhou,Yakun Zhang,Weiqiang Wu 제어·로봇·시스템학회 2024 International Journal of Control, Automation, and Vol.22 No.2

At present, the splicing of steel arches for open-type TBM suffers from the problems of labor-intensive, time-consuming, low efficiency and greater potencial risk to workers. Rock-fall and collapse caused by untimely support is still one of the main construction accidents. In this paper, a novel steel arch splicing manipulator is developed for unmanned and automated steel arch splicing, and a backstepping method based cascade control strategy is proposed to improve the trajectory tracking control performance. Firstly, the inner-loop controller is designed to compensate the flow coupling between each joint-driven hydraulic cylinder based on dynamic analysis and feedback linearization. Secondly, the adaptive robust controller is adopted for outer-loop controller design to deal with parametric uncertainties and external disturbances. Finally, the system stability is proved by Lyapunov function, then comparative experiments are conducted to verify the effectiveness and superiority of the proposed control scheme. It can be concluded that the proposed controller has a better trajectory tracking control performance, while the control input is much smoother than that of traditional PID controller.

Adaptive neural network sliding mode control for serially connected hydraulic cylinders of a heavy-duty hydraulic manipulator

Xinping Guo,Hengsheng Wang,Liang Wang,Hua Liu 대한기계학회 2023 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.37 No.7

A sliding mode control based on adaptive neural network is proposed aiming at the automatic control problem of the heavy-duty hydraulic manipulator, which is widely applied in construction machinery. The simplified state space model is established for the two hydraulic cylinders connected in series for the parallel movement of the boom of a rock drilling jumbo manipulator. By using the square of the norm of the neural network weight vector to replace the elements of the weight vector as the adaptive parameter, the computational burden of the controller is reduced and hence becomes more suitable for practical applications. The control law is designed by combining adaptive neural network with sliding mode control, and Lyapunov stability analysis is performed theoretically for the proposed control algorithm. Simulations are conducted to verify the feasibility of the designed controller. Extensive experimental studies are carried out on the heavy-duty hydraulic manipulator of a rock drilling jumbo. When tracking sinusoidal position, the error of the proposed controller is reduced by 53 % and 71 % compared with the traditional sliding mode controller and PID controller, respectively, thereby proving the effectiveness and practicality of the proposed control algorithm.

An Adaptive Autopilot for Course-keeping and Track-keeping Control of Ships using Adaptive Neural Network (Part I: Theoretical Study)

Nguyen Phung-Hung,Jung Yun-Chul Korean Institute of Navigation and Port Research 2005 한국항해항만학회지 Vol.29 No.9

This paper presents a new adaptive autopilot for ships based on the Adaptive Neural Networks. The proposed adaptive autopilot is designed with some modifications and improvements from the previous studies on Adaptive Neural Networks by Adaptive Interaction (ANNAI) theory to perform course-keeping, turning and track-keeping control. A strategy for automatic selection of the neural network controller parameters is introduced to improve the adaptation ability and the robustness of new ANNAI autopilot. In Part II of the paper, to show the effectiveness and feasibility of the proposed ANNAI autopilot, computer simulations of course-keeping and track-keeping tasks with and without the effects of measurement noise and external disturbances will be presented.