scholarly journals Adaptive Neural Control of Hypersonic Vehicles with Actuator Constraints

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Changxin Luo ◽  
Humin Lei ◽  
Dongyang Zhang ◽  
Xiaojun Zou
Keyword(s):  
Control Problem ◽  
Neural Control ◽  
Control Method ◽  
Input Saturation ◽  
Control Input ◽  
Input Constraints ◽  
State Variables ◽  
Control Effect ◽  
Adaptive Neural Control ◽  
Loop Control

An adaptive neural control method is proposed in this paper for the flexible air-breathing hypersonic vehicle (AHV) with constraints on actuators. This scheme firstly converts the original control problem with input constraints into a new control problem without input constraints based on the control input saturation function. Secondly, on the basis of the implicit function theorem, the radial basis function neural network (RBFNN) is introduced to approximate the uncertain items of the model. And the minimal-learning-parameter (MLP) technique is adopted to design the adaptive law for the norm of network weight vector, which significantly reduces calculations. Meanwhile, the finite-time convergence differentiator (FD) is introduced, through which the model state variables and their derivatives are accurately estimated to ensure the control effect. Finally, it is theoretically proved that the closed-loop control system is stable. And the effectiveness of the designed controller is verified by simulation.

scholarly journals Adaptive Neural Control for Nonaffine Pure-Feedback System Based on Extreme Learning Machine

2019 ◽  
Vol 2019 ◽  
pp. 1-13
Author(s):  
Chenyang Xu ◽  
Humin Lei ◽  
Jiong Li ◽  
Jikun Ye ◽  
Dongyang Zhang
Keyword(s):  
Extreme Learning Machine ◽  
Neural Control ◽  
Control Method ◽  
Tracking Error ◽  
Adjustment Process ◽  
Equivalent Transformation ◽  
State Variables ◽  
Feedback Systems ◽  
Adaptive Neural Control ◽  
Learning Machine

For nonaffine pure-feedback systems, an adaptive neural control method based on extreme learning machine (ELM) is proposed in this paper. Different from the existing methods, this scheme firstly converts the original system into a nonaffine system containing only one unknown term by equivalent transformation, thus avoiding the cumbersome and complex indirect design process of traditional backstepping methods. Secondly, a high-performance finite-time-convergence-differentiator (FD) is designed, through which the system state variables and their derivatives are accurately estimated to ensure the control effect. Thirdly, based on the implicit function theorem, the ELM neural network is introduced to approximate the uncertain items of the system, which simplifies the repeated adjustment process of the network training parameters. Meanwhile, the minimum learning parameter algorithm (MLP) is adopted to design the adaptive law for the norm of the network weight vector, which significantly reduces calculations. And it is theoretically proved that the closed-loop control system is stable and the tracking error is bounded. Finally, the effectiveness of the designed controller is verified by simulation.

scholarly journals The Adaptive Neural Control for a Class of High-Order Uncertain Stochastic Nonlinear Systems

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Xiaoyan Qin
Keyword(s):  
Nonlinear Systems ◽  
Neural Control ◽  
Closed Loop ◽  
Control Method ◽  
High Order ◽  
Stochastic Nonlinear Systems ◽  
Closed Loop System ◽  
Adaptive Neural Control ◽  
Control Scheme ◽  
Approximation Capability

This paper studies the problem of the adaptive neural control for a class of high-order uncertain stochastic nonlinear systems. By using some techniques such as the backstepping recursive technique, Young’s inequality, and approximation capability, a novel adaptive neural control scheme is constructed. The proposed control method can guarantee that the signals of the closed-loop system are bounded in probability, and only one parameter needs to be updated online. One example is given to show the effectiveness of the proposed control method.

scholarly journals Underactuated robotics: A review

2019 ◽  
Vol 16 (4) ◽  
pp. 172988141986216 ◽  
Author(s):  
Bin He ◽  
Shuai Wang ◽  
Yongjia Liu
Keyword(s):  
Control Method ◽  
Constraint Equation ◽  
Research Direction ◽  
Control Flow ◽  
Future Research ◽  
Control Input ◽  
Research Directions ◽  
Loop Control ◽  
Future Research Directions ◽  
Underactuated Robot

Underactuated robotics is an emerging research direction in the field of robotics. The control input of the underactuated robot is less than the degree of freedom of the system. It has the advantages of lightweight, low energy consumption, excellent performance, and broad development prospects. This article reviews the state of the art on underactuated robotics. On the basis of previous studies, this article takes the non-holonomic constraint equation as the entry point to classify and summarize underactuated robot and their common mechanisms. The controllability of underactuated robot is further discussed. The control flow of underactuated robot is described based on the open–closed control method. In the closed-loop control, the control method based on the fuzzy system is mainly used. Finally, the difficulties in the current research of underactuated robot are summarized, and the future research directions are prospected.

scholarly journals Adaptive Finite-Time Congestion Control for Uncertain TCP/AQM Network with Unknown Hysteresis

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Weimin Zheng ◽  
Yanxin Li ◽  
Xiaowen Jing ◽  
Shangkun Liu
Keyword(s):  
Congestion Control ◽  
Finite Time ◽  
Neural Control ◽  
Closed Loop ◽  
Control Method ◽  
Transmission Control Protocol ◽  
External Disturbance ◽  
Closed Loop System ◽  
Transmission Control ◽  
Adaptive Neural Control

The issue of adaptive practical finite-time (FT) congestion control for the transmission control protocol/active queue management (TCP/AQM) network with unknown hysteresis and external disturbance is considered in this paper. A finite-time congestion controller is designed by the backstepping technique and the adaptive neural control method. This controller guarantees that the queue length tracks the desired queue in finite-time, and it is semiglobally practical finite-time stable (SGPFS) for all the signals of the closed-loop system. At last, the simulation results show that the control strategy is effective.

Robust Control of Automotive Active Seat-Suspension System Subject to Actuator Saturation

2014 ◽  
Vol 136 (4) ◽  
Author(s):  
Zhou Gu ◽  
Shumin Fei ◽  
Yaqin Zhao ◽  
Engang Tian
Keyword(s):  
Controller Design ◽  
Actuator Saturation ◽  
Input Saturation ◽  
Output Feedback Control ◽  
Suspension System ◽  
Linear Matrix ◽  
Control Input ◽  
State Variables ◽  
Sampled Data ◽  
Seat Suspension

This paper deals with the problem of robust sampled-data control for an automotive seat-suspension system subject to control input saturation. By using the nature of the sector nonlinearity, a sampled-data based control input saturation in the control design is studied. A passenger dynamic behavior is considered in the modeling of seat-suspension system, which makes the model more precisely and brings about uncertainties as well in the developed model. Robust output feedback control strategy is adopted since some state variables, such as, body acceleration and body deflection, are unavailable. The desired controller can be achieved by solving the corresponding linear matrix inequalities (LMIs). Finally, a design example has been given to demonstrate the effectiveness and advantages of the proposed controller design approach.

Design of Missile Longitudinal Control System Based on Backstepping Control

2014 ◽  
Vol 496-500 ◽  
pp. 1401-1406
Author(s):  
Mei Hong Li ◽  
Jian Yin ◽  
Xue Yang Sun ◽  
Jin Xiang Xu ◽  
Mei Mei Zhang
Keyword(s):  
Control System ◽  
Control Method ◽  
Input Saturation ◽  
Lyapunov Stability Theory ◽  
Feedback System ◽  
Backstepping Control ◽  
Control Input ◽  
Backstepping Method ◽  
Longitudinal Control ◽  
And Control

Missile control system is not block strict feedback system which is suitable to use backstepping method. So in this paper, a backstepping control method is proposed to design a missile longitudinal autopilot and is proved to be asymptotically stable by Lyapunov stability theory. The simulation results show that the designed system can still track commands quickly and accurately and is robust with aerodynamic perturbation and control input saturation.

scholarly journals Linear Control of Fractional-Order Financial Chaotic Systems with Input Saturation

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Junhai Luo ◽  
Guanjun Li ◽  
Heng Liu
Keyword(s):  
Fractional Order ◽  
Chaotic Systems ◽  
Control Method ◽  
Input Saturation ◽  
Sufficient Conditions ◽  
State Feedback Control ◽  
Linear Control ◽  
Control Input ◽  
Saturated Control ◽  
Laplace Transform Technique

In this paper, control of fractional-order financial chaotic systems with saturated control input is investigated by means of state-feedback control method. The saturation problem is tackled by using Gronwall-Bellman lemma and a memoryless nonlinearity function. Based on Gronwall inequality and Laplace transform technique, two sufficient conditions are achieved for the asymptotical stability of the fractional-order financial chaotic systems with fractional orders 0 <α≤ 1 and 1 <α< 2, respectively. Finally, simulation studies are carried out to show the effectiveness of the proposed linear control method.

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