Robust fuzzy 3D path following for autonomous underwater vehicle subject to uncertainties

This paper addresses a three-dimensional (3D) path following control problem for underactuated autonomous underwater vehicle (AUV) subject to both internal and external uncertainties. A two-layered framework synthesizing the 3D guidance law and heuristic fuzzy control is proposed to achieve robust adaptive following along a predefined path. In the first layer, a 3D guidance controller for underactuated AUV is presented to guarantee the stability of path following in the kinematics stage. In the second layer, a heuristic adaptive fuzzy algorithm based on the guidance command and feedback linearization Proportional-Integral-Derivative (PID) controller is developed in the dynamics stage to account for the nonlinear dynamics and system uncertainties, including inaccuracy modelling parameters and time-varying environmental disturbances. Furthermore, the sensitivity analysis of the heuristic fuzzy controller is presented. Against most existing methods for 3D path following, the proposed robust fuzzy control scheme reduces the design and implementation costs of complicated dynamics controller, and relaxes the knowledge of the accuracy dynamics modelling and environmental disturbances. Finally, numerical simulation results validate the effectiveness of the proposed control framework and illustrate the outperformance of the proposed controller as well.     

Position Control of a Flexible Manipulator Using a New Nonlinear Self-Tuning PID Controller

In this paper, a new nonlinear self-tuning PID controller(NSPIDC) is proposed to control the joint position and link deflection of a flexible-link manipulator(FLM) while it is subjected to carry different payloads. Since, payload is a critical parameter of the FLM whose variation greatly influences the controller performance. The proposed controller guarantees stability under change in payload by attenuating the non-modeled higher order dynamics using a new nonlinear autoregressive moving average with exogenous-input(NARMAX) model of the FLM. The parameters of the FLM are identified on-line using recursive least square(RLS) algorithm and using minimum variance control(MVC) laws the control parameters are updated in real-time. This proposed NSPID controller has been implemented in real-time on an experimental set-up. The joint tracking and link deflection performances of the proposed adaptive controller are compared with that of a popular direct adaptive controller(DAC). From the obtained results, it is confirmed that the proposed controller exhibits improved performance over the DAC both in terms of accurate position tracking and quick damping of link deflections when subjected to variable payloads.     

Globally Stable Adaptive Dynamic Surface Control for Cooperative Path Following of Multiple Underactuated Autonomous Underwater Vehicles

The cooperative path following problem of multiple underactuated autonomous underwater vehicles (AUVs) involves two tasks. The first one is to force each AUV to converge to the desired parameterized path. The second one is to satisfy the requirement of a cooperative behavior along the paths. In this paper, both of the tasks have been further studied. For the first one, a simplified path following controller is proposed by incorporating the dynamic surface control (DSC) technique to avoid the calculation of derivatives of virtual control signals. Besides, in order to handle the uncertain dynamics, a new type of neural network (NN) adaptive controller is derived, and then an NN based energy‐efficient path following controller is firstly proposed, which consists of an adaptive neural controller dominating in the neural active region and an extra robust controller working outside the neural active region. For the second one, in order to reduce the amount of communications between multiple AUVs, a distributed estimator for the reference common speed is firstly proposed as determined by the communications topology adopted, which means the global knowledge of the reference speed is relaxed for the problem of cooperative path following. The overall algorithm ensures that all the signals in the closed‐loop system are globally uniformly ultimately bounded (GUUB) and the output of the system converges to a small neighborhood of the reference trajectory by properly choosing the design parameters. Simulation results validate the performance and robustness of the proposed strategy.     

基于SSA-模糊PID的AUV姿态控制研究

针对AUV高精度、高稳定性姿态控制的提升需求,提出一种结合麻雀算法(SSA)和模糊PID控制的姿态控制器。采用麻雀算法对模糊PID控制器参数进行优化,并将寻优后模型用于算法的反馈补偿,有效解决了模糊PID控制过度依赖经验,难以应对水下复杂工况的问题。仿真结果表明,SSA-模糊PID控制器在AUV姿态调节中表现出较传统模糊PID控制器更好的响应速度和抗干扰能力,有效改善了AUV姿态控制性能。经实际应用验证,控制器在复杂工况下可快速收敛至期望姿态并维持稳定。     

自主式水下航行器三维路径跟踪的神经网络H∞鲁棒自适应控制方法

本文研究了存在模型不确定以及外界未知扰动情况下的自主式水下航行器(AUV)的三维路径跟踪控制问题. 针对此问题, 首先利用时标分离原理及正交投影Serret-Frenet坐标系建立了描述AUV质心运动及姿态运动的的仿射非线性数学模型. 其次, 在控制器设计中运用神经网络H∞鲁棒自适应算法克服了模型的不确定性及扰动, 同时在控制器设计中利用了主导输入的思想, 降低了闭环系统的复杂度, 减少了实时计算工作量, 便于工程应用. 基于Lyapunov理论的分析保证了系统的稳定性. 仿真结果表明, 路径跟踪控制律可以保证AUV沿期望路径运动, 并且具有良好的动态性能.     

基于自适应Backstepping的欠驱动AUV三维航迹跟踪控制

为了实现欠驱动自治水下机器人（AUV）三维航迹跟踪控制,基于非完整系统理论分析了AUV缺少横向推进器时的欠驱动控制系统特性,并验证了欠驱动AUV存在加速度约束不可积性.基于李亚普诺夫稳定性理论,利用自适应Backstepping设计连续时变的航迹点跟踪控制器,以抑制外界海流的干扰.仿真实验表明,所设计的控制器能实现欠驱动AUV对一序列三维航迹点的渐近镇定,并且航迹跟踪的精确性和鲁棒性明显优于PID控制.     

Fault tolerant control of multivariable processes using auto-tuning PID controller.

Fault tolerant control of dynamic processes is investigated in this paper using an auto-tuning PID controller. A fault tolerant control scheme is proposed composing an auto-tuning PID controller based on an adaptive neural network model. The model is trained online using the extended Kalman filter (EKF) algorithm to learn system post-fault dynamics. Based on this model, the PID controller adjusts its parameters to compensate the effects of the faults, so that the control performance is recovered from degradation. The auto-tuning algorithm for the PID controller is derived with the Lyapunov method and therefore, the model predicted tracking error is guaranteed to converge asymptotically. The method is applied to a simulated two-input two-output continuous stirred tank reactor (CSTR) with various faults, which demonstrate the applicability of the developed scheme to industrial processes.     

ANN based self tuned PID like adaptive controller design for high performance PMSM position control

Proportional-integral-derivative (PID) being the most simple and the widely deployed controller in the industrial drives is not quite amenable to the solution for high performance drives as these drives are subjected to the parametric uncertainty, unmodeled dynamics and variable load conditions during operation. In order to expand the robustness and adaptive capabilities of conventional PID controller, a neural network based PID (NNPID) like controller which is tuned when the controller is operating in an on line mode for high performance permanent magnet synchronous motor (PMSM) position control is proposed in this paper. The NN based PID like controller is composed of a mixed locally recurrent neural network and contains at most three hidden nodes which form a PID like structure. A novel training algorithm for the PID controller gain initialization based upon the minimum norm least square solution is proposed. An on line sequential training algorithm based on recursive least square is then derived to update controller gains in an on line manner. The proposed controller is not only easy to implement but also requires least number of parameters to be tuned prior to the implementation. The performance of the proposed controller is evaluated in the presence of parametric uncertainties and load disturbances also the result outcomes are compared with the conventional PID controller, optimized using Cuckoo search based optimization method.     

自适应神经模糊推理结合PID控制的并联机器人控制方法

针对6自由度液压驱动并联机器人的精确控制问题,提出一种结合自适应神经模糊推理系统(ANFIS)和比例积分微分(PID)控制的机器人控制方法。首先,利用浮动坐标系描述法(FFRF)来模拟机器人柔性组件,并构建并联机器人的拉格朗日动力学模型。然后,根据模糊推理中的模糊规则来自适应调整PID控制器参数。最后,利用神经自适应学习算法使模糊逻辑能计算隶属度函数参数,从而使模糊推理系统能追踪给定的输入和输出数据。将该控制器与传统PID控制器、模糊PID控制器进行比较,结果表明,ANFIS自整定PID控制器大大减小了末端器位移误差,能很好的控制并联机器人末端机械手的运动。     

非线性离散系统的自适应PID控制器

针对非线性离散系统设计了利用TSK(Takagi Sugeno Kang)模糊模型的自适应PID控制器。利用模糊模型预测控制信号误差,通过控制信号误差自适应PID控制器参数。比较系统输出和模糊模型输出自适应模糊模型的参数。该方法可以弥补系统参数的模糊性、数学模型的模型误差和系统参数的变化。非线性离散系统的仿真实验验证了所设计的自适应PID控制器对非线性离散系统控制的有效性。     

基于神经网络的水下机器人三维航迹跟踪控制

本文研究了水下机器人三维航迹跟踪控制问题.在充分考虑了模型中不确定水动力系数和外界海流干扰的基础上,提出了基于神经网络的自适应输出反馈控制方法.控制器由3部分组成:基于动态补偿器的输出反馈控制项、神经网络自适应控制项和鲁棒控制项.神经网络所需的自适应学习信号由线性观测器提供.基于Lyapunov稳定性理论证明了控制系统的稳定性.最后针对某AUV进行了空间三维航迹跟踪控制仿真实验,结果表明设计的控制器可以较好地克服时变非线性水动力阻尼对系统的影响,并对外界海流干扰有较好的抑制作用,可以实现三维航迹的精确跟踪.     

Dissolved oxygen control through an adaptive non-linear model approach: a simulation study

Several exact linearization methods were applied to a simplified non-linear model for the concentration of dissolved oxygen in a waste water treatment plant, and digital control algorithms were derived based on these linearizations. A non-linear adaptive control algorithm is proposed and compared with a well tuned PID, a linear adaptive controller, and a non-adaptive non-linear controller. The proposed algorithm shows a better performance under a variety of perturbations. However, users must be careful in the choice of the appropriate model parameters to be estimated.     

Adaptive Fourier Series Neural Network PID Controller

In this work, an Adaptive Neural Networks PID controller structure, called Adaptive Fourier Series Neural Networks PID controller (AFSNNPID), is developed. The main objective is to obtain a simple controller for nonlinear systems that can be tuned online to reject perturbations effect and compensate the system parameters variation. Due to its simple architecture and very attractive proprieties, the Fourier Series Neural Network (FSNN) is used to online adjust the parameters of the PID controller. Furthermore, using the delta-rule algorithm, the adaptation dynamics of the FSNN is globally stable. The design procedure of the proposed controller and the stability analysis of the closed loop system using the small gain theorem are given. To assess the effectiveness of the proposed control scheme, the control of a 3-DOF robot arm manipulator is considered and a comparative study, using the adaptive neural network PID controller and the particle swarm optimization based PID controller, is carried out. The obtained results, through the experimental study, indicate that the AFSNNPID controller presents better control performance than the other controllers.

     

基于多种群遗传算法的水面无人艇航迹控制方法

针对水面无人艇(USV)的航迹控制问题,提出了一种由视线导向法和多种群遗传算法整定的PID航向控制器组成的航迹跟踪控制方法.该方法采用多种群遗传算法克服了传统遗传算法容易陷入局部最优的问题,增强了算法的全局寻优能力;并根据模型特点改进了适应度函数,使得对控制器性能的评价更加合理.与标准遗传算法和粒子群算法的对比仿真表明,多种群遗传算法在PID参数整定方面寻优能力更强、稳定性更高;同时,整定出的PID控制器针对不同的模型参数,均表现出收敛速度快、无超调、无稳态误差的优良特性.航迹仿真结果表明,设计的航迹控制方法能够有效跟踪给定航迹.     

欠驱动AUV模糊神经网络L2增益鲁棒跟踪控制

提出基于模糊神经网络欠驱动水下自主机器人(AUV)的L2增益鲁棒跟踪控制方法,该方法通过在线学习逼近动力学模型的不确定项.控制器克服了由于缺少横向推力对跟踪误差的影响,在考虑未知海流干扰情况下,实现了系统对模糊神经网络逼近误差的L2增益小于γ.利用Lyapunov稳定性理论证明了闭环控制系统误差信号一致最终有界.最后,通过精确模型参数和参数扰动仿真实验验证了该控制方法具有很好的跟踪效果和较强的鲁棒性.     

具有H_∞跟踪性能的机器人自适应PID控制

针对非线性不确定机器人系统的轨迹跟踪控制问题,提出一种鲁棒自适应PID控制算法.该控制器由主控制器和监督控制器组成.主控制器以常规PID控制为基础,基于滑模控制思想设计PID参数的自适应律,根据误差实时修正PID参数.基于Lyapunov函数设计的监督控制器补偿自适应PID控制器与理想控制器之间的差异,使系统具有设定的H_∞的跟踪性能.最后,两关节机器人的仿真实验结果表明了算法的有效性.

Abstract：

A robust adaptive PID control algorithm is proposed for trajectory tracking of robot manipulators with nonlinear uncertainties.The controller is composed of a main controller and a supervisory controller.The main controller is designed based on the traditional PID controller.The parameters of the PID controller are updated online according to the system running errors with the adaptation law based on the sliding mode control.The supervisory controller is proposed to compensate the error between the adaptive PID controller and the ideal controller in the sense of the Lyapunov function with the specified H_∞ tracking performance.Finally, the simulation results based on a two-joint robot manipulator show the effectiveness of the presented controller.     

基于遗传算法的半导体制冷器非线性PID设计

针对常规线性PID对具有非线性特征的半导体制冷器温度控制系统存在快速性和超调量难以兼得、抗干扰能力差的问题,提出将非线性PID控制用于半导体制冷器温度控制的策略.通过对线性PID存在问题以及PID各增益参数与偏差信号之间非线性关系的分析,构建了增益参数的非线性函数,针对非线性函数中参数较多问题提出了自适应遗传寻优的求解方法.仿真和实验结果表明,基于此遗传算法寻优的非线性PID控制器相比线性PID控制器,调节时间缩短,超调量减小,抗干扰能力更强.     

焦炉集气管压力控制的一种新方法

针对焦炉集气管压力系统的特点,提出一种基于神经元的自适应PID控制算法;将神经元与PID结合,在线调整PID参数;对单神经元自适应PID控制器进行了研究,并利用Matlab/Simulink进行了仿真,结果表明单神经元自适应PID控制算法明显优于常规PID控制算法;用本文提出的单神经元自适应PID控制器控制焦炉集气管压力,使集气管压力系统的抗干扰能力得到提高,取得了良好的控制效果。     

基于布谷鸟搜索算法优化的BLDCM角度控制

当前,经典比例积分微分(PID)控制在无刷直流电机(BLDCM)控制领域仍然占据十分重要的地位.为了解决传统PID控制器参数优化费时、最佳控制性能难以保证的问题,提出使用布谷鸟搜索(CS)算法优化PID控制器(CS-PID)构成电机的角度位置控制.其次,选用时间乘绝对误差积分(ITAE)函数作为CS算法的适应性函数,为...     

Intelligent control of static synchronous series compensator via an adaptive self-tuning PID controller for suppression of torsional oscillations

In this paper, an intelligent controller is proposed to control a static synchronous series compensator (SSSC) in order to mitigate subsynchronous resonance (SSR) oscillations in a power system. This intelligent controller is an adaptive self-tuning PID controller. To train the PID controller, the gradient descent method is employed where the learning rate is adapted in every iteration in order to accelerate the speed of convergence. This control scheme also requires a wavelet neural network (WNN) to identify the controlled system dynamic. To update the parameters of WNN, the gradient descent (GD) along with the adaptive learning rates derived by the Lyapunov method is used. The computer simulations are used to show the ability of the proposed controller. In addition, the performance of the proposed controller is compared with another self-tuning PID controller. The results demonstrate that the proposed controller has a successful performance in minimizing the SSR.