Quaternion-Based Adaptive Trajectory Tracking Control of a Rotor-Missile with Unknown Parameters Identification

This paper investigates the adaptive trajectory tracking control problem and the unknown parameter identification problem of a class of rotor-missiles with parametric system uncertainties.First,considering the uncertainty of structural and aerodynamic parameters,the six-degree-of-freedom(6Do F) nonlinear equations describing the position and attitude dynamics of the rotor-missile are established,respectively,in the inertial and body-fixed reference frames.Next,a hierarchical adaptive trajectory tracking controller that can guarantee closed-loop stability is proposed according to the cascade characteristics of the 6Do F dynamics.Then,a memory-augmented update rule of unknown parameters is proposed by integrating all historical data of the regression matrix.As long as the finitely excited condition is satisfied,the precise identification of unknown parameters can be achieved.Finally,the validity of the proposed trajectory tracking controller and the parameter identification method is proved through Lyapunov stability theory and numerical simulations.

Identification of time-varying system and energy-based optimization of adaptive control in seismically excited structure

The combination of structural health monitoring and vibration control is of great importance to provide components of smart structures.While synthetic algorithms have been proposed,adaptive control that is compatible with changing conditions still needs to be used,and time-varying systems are required to be simultaneously estimated with the application of adaptive control.In this research,the identification of structural time-varying dynamic characteristics and optimized simple adaptive control are integrated.First,reduced variations of physical parameters are estimated online using the multiple forgetting factor recursive least squares(MFRLS)method.Then,the energy from the structural vibration is simultaneously specified to optimize the control force with the identified parameters to be operational.Optimization is also performed based on the probability density function of the energy under the seismic excitation at any time.Finally,the optimal control force is obtained by the simple adaptive control(SAC)algorithm and energy coefficient.A numerical example and benchmark structure are employed to investigate the efficiency of the proposed approach.The simulation results revealed the effectiveness of the integrated online identification and optimal adaptive control in systems.

An adaptive control strategy for microgrid secondary frequency based on parameter identification

Complex microgrid structures and time-varying conditions, among other factors, cause problems in the mechanical modeling of microgrids, making model-based controller optimization difficult. Therefore, this study proposed a secondary frequency adaptive control strategy based on parameter identification, which uses an online parameter identification method to identify the parameters in the microgrid in real-time. The identified parameters are then used in the secondary frequency adaptive controller to optimize the real-time controller performance. The proposed method realizes adaptive optimization of the controller in the microgrid operation state and is applied to a microgrid with unknown parameters to adjust the controller parameters. Finally, a simulation experiment was conducted to verify the model accuracy and the frequency regulation effect of the proposed adaptive control strategy.

Adaptive proportional integral differential control based on radial basis function neural network identification of a two-degree-of-freedom closed-chain robot

A closed-chain robot has several advantages over an open-chain robot, such as high mechanical rigidity, high payload, high precision. Accurate trajectory control of a robot is essential in practical-use. This paper presents an adaptive proportional integral differential （PID） control algorithm based on radial basis function （RBF） neural network for trajectory tracking of a two-degree-of-freedom （2-DOF） closed-chain robot. In this scheme, an RBF neural network is used to approximate the unknown nonlinear dynamics of the robot, at the same time, the PID parameters can be adjusted online and the high precision can be obtained. Simulation results show that the control algorithm accurately tracks a 2-DOF closed-chain robot trajectories. The results also indicate that the system robustness and tracking performance are superior to the classic PID method.

Synchronization and Parameters Identification of Chaotic Systems via Adaptive Control

Based on Lyapunov stability theory, a novel adaptive controller is designed for a class of chaotic systems .The parameters identification and synchronization of chaotic systems can be carried out simultaneously. The controller and the updating law of parameters identification are directly constructed by analytic formula. Simulation results with Chen’s system and R?ssler system show the effectiveness of the proposed controller.

Persistent excitation in adaptive parameter identification of uncertain chaotic system

This paper studies the parameter identification problem of chaotic systems. Adaptive identification laws are pro- posed to estimate the parameters of uncertain chaotic systems. It proves that the asymptotical identification is ensured by a persistently exciting condition. Additionally, the method can be applied to identify the uncertain parameters with any number. Numerical simulations are given to validate the theoretical analysis.

Identification and Control of Dynamical Systems Using Modified Neural Networks

Aim To study the identification and control of nonlinear systems using neural networks. Methods A new type of neural network in which the dynamical error feedback is used to modify the inputs of the network was employed to reduce the inherent network approximation error. Results A new identification model constructed by the proposed network and stable filters was derived for continuous time nonlinear systems, and a stable adaptive control scheme based on the proposed networks was developed. Conclusion Theory and simulation results show that the modified neural network is feasible to control a class of nonlinear systems.

Adaptive Cascade Generalized Predictive Control

Cascade control is one of the most popular structures for process control as it is a special architecture for dealing with disturbances. However, the drawbacks of cascade control are obvious that primary controller and secondary controller should be tuned together, which influences each other. In this paper, a new Adaptive Cascade Generalized Predictive Controller (ACGPC) is introduced. ACGPC is a method issued from GPC and the inner and outer controllers of a cascade system are replaced by one cascade generalized predictive controller, where both loops model are updated by Recursive Least Squares method. Compared with existing methods, the new method is simpler and yet more effective. It can be directly integrated into commercially available industrial auto-tuning systems. Some examples are given to illustrate the effectiveness and robustness of the proposed method.

Adaptive Control by Using Neural Networks

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FUZZY ADAPTIVE CONTROL MODEL FOR PROCESS IN NICKEL MATTE SMELTING FURNACE

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Ad Hoc中的TCP改进方案——Adaptive ADTCP

在分析无线自组网特点及其对TCP性能影响的基础上,提出了一种能够自适应无线自组网状态的TCP改进方案(Adaptive ADTCP)。Adaptive ADTCP在明确辨识网络状态的前提下,首先根据前向路径跳数自适应地调整拥塞窗口增长因子,限制源节点TCP拥塞窗口过分增长,避免造成网络拥塞;同时源节点TCP根据当前拥塞窗口自适应改变发送的分组长度,充分利用网络资源。仿真实验表明,在网络重负载和节点高速移动情况下,Adaptive ADTCP对数据传输的吞吐量有较大提升。

IMPROVED FXLMS ALGORITHM BASED ON ADAPTIVE NOTCH FILTER APPROACH

An improved algorithm is presented to identify the secondary path based on the adaptive notch filter approach. Since the interference from the narrow band excitation signal is suppressed by the adaptive notch filter, the convergent speed of the on-line control path identification process is significantly improved. As a result, the controller performance is greatly enhanced. Besides the algorithm development, some important factors, such as the influence of reference signal on the controller convergent speed, are also discussed. The effectiveness of the algorithm is verified by experimental results.

Partial Topology Identification of Stochastic Multi-Weighted Complex Networks Based on Graph-Theoretic Method and Adaptive Synchronization

This article aims to identify the partial topological structures of delayed complex network.Based on the drive-response concept,a more universal model,which includes nonlinear couplings,stochastic perturbations and multi-weights,is considered into drive-response networks.Different from previous methods,we obtain identification criteria by combining graph-theoretic method and adaptive synchronization.After that,the partial topological structures of stochastic multi-weighted complex networks with or without time delays can be identified successfully.Moreover,response network can reach synchronization with drive network.Ultimately,the effectiveness of the proposed theoretical results is validated through numerical simulations.

基于路面等级识别的车辆半主动悬架内外环控制

针对车辆在不同路面等级下对悬架动态性能与馈能特性需求不同的问题,提出一种基于RF-XGBoost路面等级识别算法的半主动悬架内外环控制策略。利用随机森林(Random Forest,RF)模型对极端梯度提升(Extreme Gradient Boosting,XGBoost)算法进行优化,搭建RF-XGBoost算法模型对路面等级进行识别。将路面等级与悬架控制策略相结合,设计外环为天地棚控制,内环为自适应滑模控制的内外环控制,实现非线性悬架的自适应控制。仿真结果表明,相比传统混合天地棚控制的悬架,内外环控制下的悬架在A级路面下簧载质量加速度降低15.52%,并实现50.4 W的振动能量回收,在B、C级路面下簧载质量加速度分别降低15.09%、16.72%,轮胎动载荷分别降低11.63%、11.42%,在D级路面下轮胎动载荷降低14.12%。台架试验的结果与仿真分析的结果基本一致,表明所设计的自适应内外环控制有效。

基于地形特征时频变换的无人特种车辆速度自适应控制方法

为提高复杂环境下无人特种车辆安全性、自主性及作业能力,面向在崎岖地形下无人特种车辆应用需求,提出一种基于地形特征时频变换的速度自适应控制方法。通过度量地形特征崎岖度、建立崎岖度和车速数学模型,实现无人特种车辆在崎岖地形的自主、自适应车速规划。针对崎岖地形和坡度引起的点云数据失真问题,融合惯性测量单元传感器数据对点云进行补偿,获得了车辆前方地形的精准点云数据;针对可视距离与跟踪精度冲突问题,不同于传统横向曲率计算方式,采用以线到面的方式,将激光雷达纵向剖面点云数据进行时频变换后,在频域内选取次频区域的积分面积作为崎岖度量化值,实现对不同地形下的崎岖度量化;此外,基于上述获得的崎岖度,采用迭代搜索的方式建立速度与崎岖度数学模型,并采用滑动窗口的方式更新崎岖度,实现车速到崎岖度之间的连续映射。以可控震源野外勘探特种车为研究对象,采用上述方法在实际野外地形环境中进行多次实验。实验结果表明,所提出的方法在崎岖地形具有良好的安全性、自主性,可以识别地形和自适应控制车速。

一阶系统模型参考自适应辨识及位置控制

针对新建系统或负载变化导致的模型参数不确定问题,对交流伺服系统进行研究,提出模型参考自适应辨识(model reference adaptive identification,MRAI)算法,对一阶系统模型参数进行在线辨识。首先对一阶系统进行数学模型构建,然后根据李雅普诺夫(Lyapunov)稳定理论选择参考模型,并设计自适应控制律,在此基础上对一阶系统进行模型参数在线辨识。当参数收敛后,根据系统要求的自然频率和阻尼比,在线设计位置控制器参数,并进行跟踪定位控制。仿真和实验结果表明,模型参考自适应在线辨识算法能快速精准地辨识出系统模型参数,通过在线设计控制器参数实现了对参考信号的快速跟踪。

基于参数辨识的永磁辅助同步磁阻电机电流无差拍控制

针对永磁辅助同步磁阻电机电感参数失配引起传统无差拍预测电流控制器受扰动的问题,该文提出一种电感参数自适应的无差拍预测电流控制方案。所提算法利用模型参考自适应系统对电感参数进行在线辨识,并将辨识结果代替控制器中的原始电感模型参数,以确保控制器的模型参数和电机实际参数相匹配,进而增强无差拍预测电流控制系统的鲁棒性。根据永磁辅助同步磁阻的数学模型,对电机本体进行了建模,以实现对所提算法的仿真验证。最后在以TMS320F28335控制器为核心的实验平台上进行了实验验证。仿真和实验结果均表明,当电机参数与模型参数不匹配时,所提算法相较于传统的无差拍预测电流控制算法在电流波动峰峰值、电流跟踪静差及系统鲁棒性方面都有明显的改善。

Deep adaptive control with online identification for industrial robots

Derivation of control equations from data is a critical problem in numerous scientific and engineering fields.The inverse dynamic control of robot manipulators in the field of industrial robot research is a key example.Traditionally,researchers needed to obtain the robot dynamic model through physical modeling methods before developing controllers.However,the robot dynamic model and suitable control methods are often elusive and difficult to tune,particularly when dealing with real dynamical systems.In this paper,we combine an enhanced online sparse Bayesian learning(OSBL)algorithm and a model reference adaptive control method to obtain a data-driven modeling and control strategy from data containing noise;this strategy can be applied to dynamical systems.In particular,we use a sparse Bayesian approach,relying only on some prior knowledge of its physics,to extract an accurate mechanistic model from the measured data.Unmodeled parameters are further identified from the modeling error through a deep neural network(DNN).By combining the identification model with a model reference adaptive control approach,a general deep adaptive control(DAC)method is obtained,which can tolerate unmodeled dynamics.The adaptive update law is derived from Lyapunov’s stability criterion,which guarantees the asymptotic stability of the system.Finally,the Enhanced OSBL identification method and DAC scheme are applied on a six-degree-of-freedom industrial robot,and the effectiveness of the proposed method is verified.

基于镜像修正FxLMS控制算法的船舶管路振动主动控制

针对船舶管路减振和抗冲击的需求,本文根据镜像修正自适应滤波算法,设计出了一种管路振动主动控制策略,能够有效地控制管路在低频下的振动,并且在次级通道发生突变时,控制系统可再次快速收敛,进行稳定控制。本文先对镜像修正自适应滤波算法进行理论研究,分析算法的迭代及控制过程;再通过仿真分别验证算法在不同参考信号输入下的收敛性及稳定性;最后搭建实验台架,通过试验验证算法的实际控制效果。试验结果表明:该控制策略在管路振动主动控制中能够降低15.37%的振动强度,比自适应滤波算法控制策略的控制效果好8.85%。所以镜像修正自适应滤波算法能够及时有效地进行管路振动控制。

An Integrated Observer Framework Based Mechanical Parameters Identification for Adaptive Control of Permanent Magnet Synchronous Motor

An integrated observer framework based mechanical parameters identification approach for adaptive control of permanent magnet synchronous motors is proposed in this paper.Firstly,an integrated observer framework is established for mechanical parameters’estimation,which consists of an extended sliding mode observer(ESMO)and a Luenberger observer.Aiming at minimizing the influence of parameters coupling,the viscous friction and the moment of inertia are obtained by ESMO and the load torque is identified by Luenberger observer separately.After obtaining estimates of the mechanical parameters,the optimal proportional integral(PI)parameters of the speed-loop are determined according to third-order best design method.As a result,the controller can adjust the PI parameters in real time according to the parameter changes to realize the adaptive control of the system.Meanwhile,the disturbance is compensated according to the estimates.Finally,the experiments were carried out on simulation platform,and the experimental results validated the reliability of parameter identification and the efficiency of the adaptive control strategy presented in this paper.