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Boundary Control of a Coupled Nonlinear Flexible Marine Riser
Shuzhi Sam Ge,Wei He,How, Bernard Voon Ee,Yoo Sang Choo IEEE 2010 IEEE transactions on control systems technology Vol.18 No.5
<P>In this paper, boundary control for a coupled nonlinear flexible marine riser with two actuators in transverse and longitudinal directions is developed to reduce the riser's vibrations. The dynamic behavior of the flexible riser is represented by a distributed parameter system with partial differential equations (PDEs) and the control is applied at the top boundary of the riser based on Lyapunov's direct method to regulate the riser's vibrations. With the proposed boundary control, uniform boundedness under ocean current disturbance, and exponential stability under free vibration condition can be achieved. The proposed control is independent of system parameters, which ensures the robustness of the system to variations in parameters. Numerical simulations for demonstrating the effectiveness of the proposed control are presented.</P>
A Modular Designed Bolt Tightening Shaft Based on Adaptive Fuzzy Backstepping Control
Sibang Liu,Shuzhi Sam Ge,Zhongliang Tang 제어·로봇·시스템학회 2016 International Journal of Control, Automation, and Vol.14 No.4
This paper presents a modular designed autonomous bolt tightening shaft system with an adaptive fuzzybackstepping control approach developed for it. The bolt tightening shaft is designed for the autonomous bolttightening operation, which has huge potential for industry application. Due to the inherent nonlinear and uncertainproperties, the bolt tightening shaft and the bolt tightening process are mathematically modeled as an uncertain strictfeedback system. With the adaptive backstepping and approximation property of fuzzy logic system, the controlleris recursively designed. Based on the Lyapunov stability theorem, all signals in the closed-loop system are proved tobe uniformly ultimately bounded and the torque tracking error exponentially converges to a small residue. And theeffectiveness and performance of the proposed autonomous system are verified by the simulation and experimentresults on the bolt tightening shaft system.
Min Wang,Shuzhi Sam Ge,Keum-Shik Hong IEEE 2010 IEEE transactions on neural networks Vol.21 No.11
<P>This paper presents adaptive neural tracking control for a class of non-affine pure-feedback systems with multiple unknown state time-varying delays. To overcome the design difficulty from non-affine structure of pure-feedback system, mean value theorem is exploited to deduce affine appearance of state variables as virtual controls , and of the actual control . The separation technique is introduced to decompose unknown functions of all time-varying delayed states into a series of continuous functions of each delayed state. The novel Lyapunov-Krasovskii functionals are employed to compensate for the unknown functions of current delayed state, which is effectively free from any restriction on unknown time-delay functions and overcomes the circular construction of controller caused by the neural approximation of a function of and . Novel continuous functions are introduced to overcome the design difficulty deduced from the use of one adaptive parameter. To achieve uniformly ultimate boundedness of all the signals in the closed-loop system and tracking performance, control gains are effectively modified as a dynamic form with a class of even function, which makes stability analysis be carried out at the present of multiple time-varying delays. Simulation studies are provided to demonstrate the effectiveness of the proposed scheme.</P>
Layered Formation-containment Control of Multi-agent Systems in Constrained Space
Dongyu Li,Shuzhi Sam Ge,Guangfu Ma,Wei He 제어·로봇·시스템학회 2020 International Journal of Control, Automation, and Vol.18 No.3
This paper addresses the layered formation-containment (LFC) problem for multiagents in the constrained space with a directed communication topology. The formation-containment problem is first defined using a layered framework, and a layered distributed finite-time estimator is proposed to acquire the target states for agents in each layer. Based on the proposed framework, the formation configuration and the mechanism of the information flow can be explored and designed naturally. To avoid collisions with borders, obstacles, as well as the other agents in the constrained space, an artificial potential function is designed based on the Dirac delta function. Further, a disturbance observer and adaptive neural networks (NNs) are applied to respectively tackle the external disturbance and the model uncertainties. The desired formation of each layer can be achieved while no collision occurs in the constrained space. The semi-global uniform ultimate boundedness of closed-loop errors is guaranteed by Lyapunov stability theory. Simulation results are given to show the effectiveness of the proposed approaches.
General Fight Rule-based Trajectory Planning for Pairwise Collision Avoidance in a Known Environment
Gang Wang,Shuzhi Sam Ge 제어·로봇·시스템학회 2014 International Journal of Control, Automation, and Vol.12 No.4
This paper presents a general flight rule-based autonomous trajectory planning scheme for two aerial vehicles to avoid obstacles and collisions in known environments in low-altitude airspace for general aviation. Flight rules in low-altitude airspace are first introduced based on the general flight rules in US, UK and China, and then the suitable flight rules are embedded into the trajectory planning algorithm. It is supposed that the flight parameters, such as positions and velocities, are all available to the aerial vehicles involved in the possible conflict. Then the trajectory of each aerial vehicle is calculated by optimizing an objective function, such as distance and fuel consumption, with the constraints corresponding to the airspace traffic rules. The optimization problem is solved by receding horizon control (RHC) based mixed integer linear programming (MILP). Compared with other collision avoid-ance algorithms, the proposed algorithm can be adapted to plan the autonomous trajectory to avoid pairwise collision and obstacles as proposed general flight rules. Simulations show the feasibility of the proposed scheme.
Trajectory Tracking Control of a Quadrotor Aerial Vehicle in the Presence of Input Constraints
Trong-Toan Tran,Shuzhi Sam Ge,Wei He,Pham Luu-Trung-Duong,Nguyen-Vu Truong 제어·로봇·시스템학회 2018 International Journal of Control, Automation, and Vol.16 No.6
In this paper, we address the control problem of a Quadrotor Aerial Vehicle (QAV) in the presence of the input constraints. For this purpose, a separation principle is applied in the control design. The QAV model is decoupled and constructed as a cascaded structure to handle its underactuated property. By imposing the constraints on the orientation angles, we show that the QAV will be never overturned. Then, a combination of the backstepping method, barrier Lyapunov and saturation functions is used in the control design for each subsystem to deal withboth input and output constraints. Our design renders the cascaded system of the QAV into the form in which an Input-to-State Stable (ISS) subsystem is driven by an asymptotic subsystem, and hence the stability of the overall cascaded system of the QAV is ensured. In addition, the tracking errors are guaranteed to converge to the origin. Simulation results are provided to illustrate the effectiveness of the proposed control.
Yuxiang Zhang,Xiaoling Liang,Shuzhi Sam Ge,Bingzhao Gao,Tong Heng Lee 제어로봇시스템학회 2021 제어로봇시스템학회 국제학술대회 논문집 Vol.2021 No.10
Guaranteed safety and performance under various circumstances remain technically critical and practically challenging for the wide deployment of autonomous vehicles. For such safety-critical systems, it will certainly be a requirement that safe performance should be ensured even during the reinforcement learning period in the presence of system uncertainty. To address this issue, a Barrier Lyapunov Function-based safe reinforcement learning algorithm (BLF- SRL) is proposed here for the formulated nonlinear system in strict-feedback form. This approach appropriately arranges the Barrier Lyapunov Function item into the optimized backstepping control method to constrain the state-variables in the designed safety region during learning when unknown bounded system uncertainty exists. More specifically, the overall system control is optimized with the optimized backstepping technique under the framework of Actor-Critic, which optimizes the virtual control in every backstepping subsystem. Wherein, the optimal virtual control is decomposed into Barrier Lyapunov Function items; and also with an adaptive item to be learned with deep neural networks, which achieves safe exploration during the learning process. Eventually, the principle of Bellman optimality is satisfied through iteratively updating the independently approximated actor and critic to solve the Hamilton-Jacobi-Bellman equation in adaptive dynamic programming. More notably, the variance of control performance under uncertainty is also reduced with the proposed method. The effectiveness of the proposed method is verified with motion control problems for autonomous vehicles through appropriate comparison simulations.