A robust fault diagnosis and accommodation scheme for robot manipulators

This paper investigates an algorithm for robust fault diagnosis (FD) in uncertain robotic systems by using a neural sliding mode (NSM) based observer strategy. A step by step design procedure will be discussed to determine the accuracy of fault estimation. First, an uncertainty observer is designed to estimate the uncertainties based on a first neural network (NN1). Then, based on the estimated uncertainties, a fault diagnosis scheme will be designed by using a NSM observer which consists of both a second neural network (NN2) and a second order sliding mode (SOSM), connected serially. This type of observer scheme can reduce the chattering of sliding mode (SM) and guarantee finite time convergence of the neural network (NN). The obtained fault estimations are used for fault isolation as well as fault accommodation to self-correct the failure systems. The computer simulation results for a PUMA560 robot are shown to verify the effectiveness of the proposed strategy.     

Neural-network-based robust fault diagnosis in robotic systems

Fault diagnosis plays an important role in the operation of modern robotic systems. A number of researchers have proposed fault diagnosis architectures for robotic manipulators using the model-based analytical redundancy approach. One of the key issues in the design of such fault diagnosis schemes is the effect of modeling uncertainties on their performance. This paper investigates the problem of fault diagnosis in rigid-link robotic manipulators with modeling uncertainties. A learning architecture with sigmoidal neural networks is used to monitor the robotic system for any off-nominal behavior due to faults. The robustness and stability properties of the fault diagnosis scheme are rigorously established. Simulation examples are presented to illustrate the ability of the neural-network-based robust fault diagnosis scheme to detect and accommodate faults in a two-link robotic manipulator.     

Improved adaptive fault‐tolerant control design for hypersonic vehicle based on interval type‐2 T‐S model

This study proposes an improved adaptive fault estimation and accommodation algorithm for a hypersonic flight vehicle that uses an interval type‐2 Takagi‐Sugeno fuzzy model and a quantum switching module. First, an interval type‐2 Takagi‐Sugeno fuzzy model for the hypersonic flight vehicle system with elevator faults is developed to process the nonlinearity and parameter uncertainties. An improved adaptive fault estimation algorithm is then constructed by adding an adjustable parameter. The quantum switching module is also applied to the estimation part to select an appropriate algorithm in different fault cases. The estimation results from the given fuzzy observer are used to design a type‐2 fuzzy fault accommodation controller to stabilize the fuzzy system. The stability of the proposed scheme is analyzed using the Lyapunov stability theory. Finally, the validity and availability of the method are verified by a series of comparisons on numerical simulation results.     

Design of a novel knowledge-based fault detection and isolation scheme

In this paper, a real-time fault detection and isolation (FDI) scheme for dynamical systems is developed, by integrating the signal processing technique with neural network design. Wavelet analysis is applied to capture the fault-induced transients of the measured signals in real-time, and the decomposed signals are pre-processed to extract details about a fault. A Regional Self-Organizing feature Map (R-SOM) neural network is synthesized to classify the fault types. The R-SOM neural network adopts two regions adjustment in the learning algorithm, thus it has high precision in clustering and matching, especially when the noise, disturbance and other uncertainties exist in the systems. As a result, the proposed FDI scheme is robust and accurate. The design is implemented on a stirred tank system and satisfactory online testing results are obtained.     

Fault Detection, Isolation, and Accommodation Control in Robotic Systems

In this paper, fault diagnosis and accommodation control are developed for robotic systems. First, a nonlinear observer in the proposed method is designed based on the available model. The fault detection is carried out by comparing the observer states with their signatures. Secondly, state observers are constructed based on possible fault function sets. Thirdly, the accommodation control design is developed using a normal controller plus a neural network compensator to capture the nonlinear characteristics of faults. Finally, if the fault isolation is completed successfully, the second fault accommodation controller is presented based on the fault information obtained by the isolation scheme.      

Adaptive fuzzy neural network finite-time command filtered control of n-link robotic systems with actuator saturation

The finite time tracking control of n-link robotic system is studied for model uncertainties and actuator saturation. Firstly, a smooth function and adaptive fuzzy neural network online learning algorithm are designed to address the actuator saturation and dynamic model uncertainties. Secondly, a new finite-time command filtered technique is proposed to filter the virtual control signal. The improved error compensation signal can reduce the impact of filtering errors, and the tracking errors of system quickly converge to a smaller compact set within finite time. Finally, adaptive fuzzy neural network finite-time command filtered control achieves finite-time stability through Lyapunov stability criterion. Simulation results verify the effectiveness of the proposed control.     

A Neural Net Predictive Control for Telerobots with Time Delay

This paper extends the Smith Predictor feedback control structure to unknown robotic systems in a rigorous fashion. A new recurrent neural net predictive control (RNNPC) strategy is proposed to deal with input and feedback time delays in telerobotic systems. The proposed control structure consists of a local linearized subsystem and a remote predictive controller. In the local linearized subsystem, a recurrent neural network (RNN) with on-line weight tuning algorithm is employed to approximate the dynamics of the time-delay-free nonlinear plant. The remote controller is a modified Smith predictor for the local linearized subsystem which provides prediction and maintains the desirable tracking performance. Stability analysis is given in the sense of Lyapunov. The result is an adaptive compensation scheme for unknown telerobotic systems with time delays, uncertainties, and external disturbances. A simulation of a two-link robotic manipulator is provided to illustrate the effectiveness of the proposed control strategy.     

An observer-based adaptive neural network tracking control of robotic systems

Robotic systems have inherently nonlinear phenomena as joints undergo sliding and/or rotating. This in turn requires that the system running states be predicted correctly. This paper makes a full analysis of the robot states by applying observer-based adaptive wavelet neural network (OBAWNN) tracking control scheme to tackle these phenomena such as system uncertainties, multiple time-delayed state uncertainties, and external disturbances such that the closed loop system signals must obey uniform ultimate boundedness and achieve H^∞ tracking performance. The recurrent adaptive wavelet neural network model is used to approximate the dynamics of the robotic system, while an observer-based adaptive control scheme is to stabilize the system. The advantage of employing adaptive wavelet neural dynamics is that we can utilize the neuron information by activation functions to on-line tune the hidden-to-output weights, and the adaptation parameters to estimate the robot parameters and the bounds of the gains of delay states directly using linear analytical results. It is shown that the stability of the closed-loop system is guaranteed by some sufficient conditions derived from Lyapunov criterion and Riccati-inequality. Finally, a numerical example of a three-links rolling cart is given to illustrate the effectiveness of the proposed control scheme.     

Robust fuzzy tracking control for robotic manipulators

In this paper, a stable adaptive fuzzy-based tracking control is developed for robot systems with parameter uncertainties and external disturbance. First, a fuzzy logic system is introduced to approximate the unknown robotic dynamics by using adaptive algorithm. Next, the effect of system uncertainties and external disturbance is removed by employing an integral sliding mode control algorithm. Consequently, a hybrid fuzzy adaptive robust controller is developed such that the resulting closed-loop robot system is stable and the trajectory tracking performance is guaranteed. The proposed controller is appropriate for the robust tracking of robotic systems with system uncertainties. The validity of the control scheme is shown by computer simulation of a two-link robotic manipulator.     

Direct adaptive neural control of nonlinear systems with extreme learning machine

A direct adaptive neural control scheme for a class of nonlinear systems is presented in the paper. The proposed control scheme incorporates a neural controller and a sliding mode controller. The neural controller is constructed based on the approximation capability of the single-hidden layer feedforward network (SLFN). The sliding mode controller is built to compensate for the modeling error of SLFN and system uncertainties. In the designed neural controller, its hidden node parameters are modified using the recently proposed neural algorithm named extreme learning machine (ELM), where they are assigned random values. However, different from the original ELM algorithm, the output weight is updated based on the Lyapunov synthesis approach to guarantee the stability of the overall control system. The proposed adaptive neural controller is finally applied to control the inverted pendulum system with two different reference trajectories. The simulation results demonstrate good tracking performance of the proposed control scheme.     

考虑驱动系统动态的机械手神经网络控制及应用

针对结构和参数均未知的机械手控制问题, 提出了考虑驱动系统动态的机械手神经网络控制方法, 采用稳定的径向基(Radial basis function, RBF)神经网络辨识机械手未知动态, 而附加的鲁棒控制可以保证存在神经网络的建模误差和外部干扰时系统的稳定性和性能, 并且该方法使机械手闭环系统一致最终有界. 同时开发了基于半实物仿真技术的机械手控制系统, 最后, 将本文方法与经典的PD控制器和自适应控制器在同一机械手平台上进行了实验验证与分析, 实验结果表明该方法具有良好的控制性能.     

基于智能解析余度的容错飞控系统设计

常规的解析余度容错方法容易受到不确定因素和随机干扰的影响,本文以飞行控制系统为研究对象,提出基于智能解析余度的容错飞行控制系统设计方案,使用径向基神经网络的在线学习和全局逼近的性能,建立飞行控制系统传感器之间的解析余度关系,利用不相同传感器之间的解析关系进行残差分析从而进行传感器的故障隔离与信号重构.这样有效地抑制了测量噪声和模型不确定性.应用某型飞机进行仿真,实现了传感器的在线故障隔离与重构,验证了该方法的有效性.     

基于扰动观测器的机器人自适应神经网络跟踪控制研究

为解决机器人动力学模型未知问题并提升系统鲁棒性,本文基于扰动观测器,考虑动力学模型未知的情况,设计了一种自适应神经网络（Neural network,NN）跟踪控制器.首先分析了机器人运动学和动力学模型,针对模型已知的情况,提出了刚体机械臂通用模型跟踪控制策略;在考虑动力学模型未知的情况下,利用径向基函数（Radial basis function,RBF）神经网络设计基于全状态反馈的自适应神经网络跟踪控制器,并通过设计扰动观测器补偿系统中的未知扰动.利用李雅普诺夫理论证明所提出的控制策略可以使闭环系统误差信号半全局一致有界（Semi-globally uniformly bounded,SGUB）,并通过选择合适的增益参数可以将跟踪误差收敛到零域.仿真结果证明所提出算法的有效性并且所提出的控制器在Baxter机器人平台上得到了实验验证.     

基于神经网络的不确定机器人自适应滑模控制

提出一种机器人轨迹跟踪的自适应神经滑模控制。该控制方案将神经网络的非线性映射能力与变结构控制理论相结合,利用RBF网络自适应学习系统不确定性的未知上界,神经网络的输出用于自适应修正控制律的切换增益。这种新型控制器能保证机械手位置和速度跟踪误差渐近收敛于零。仿真结果表明了该方案的有效性。     

Robust adaptive neural sliding mode control of MEMS triaxial gyroscope with angular velocity estimation

In this paper, a robust adaptive sliding mode control strategy of micro electro-mechanical system (MEMS) triaxial gyroscope using radial basis function (RBF) neural network is presented for the system identification of MEMS gyroscope. A key property of this scheme is that the prior knowledge of the upper bound of the system uncertainties is not required. An adaptive RBF neural network controller is used to learn the unknown upper bound of model uncertainties and external disturbances. The adaptive RBF neural network is incorporated into the adaptive sliding mode control in the Lyapunov sense, and the stability of the proposed adaptive neural sliding mode control can be established. The dynamics and angular velocities of gyroscope can be identified in real time. Numerical simulations are investigated to verify the effectiveness of the proposed adaptive neural sliding mode control scheme, showing that the designed control system has better robust performance in its insensitivity to system nonlinearities; moreover, system parameters including angular velocity can be consistently estimated and tracking errors converge to zero asymptotically.     

Adaptive manipulator trajectory control using neural networks

A unified study of adaptive control and neural network based control schemes for the trajectory tracking problem of robot manipulators is presented. Efficacy of parametrized adaptive algorithms in compensating the structured uncertainties in robot dynamics is verified through extensive simulation. The ability of neural networks to provide a robust adaptive framework in the presence of both structured and unstructured uncertainties is investigated. A case study is carried out in support of a parametrized adaptive scheme using neural networks. Simulation results clearly indicate that the neural network based adaptive controller achieves better tracking in the presence of parametric uncertainties as well as unmodelled effects compared to the simple direct adaptive scheme.     

Hierarchical intelligent control for robotic motion

This paper presents a new scheme for intelligent control of robotic manipulators. This scheme is a hierarchically integrated approach to neuromorphic and symbolic control of robotic manipulators. This includes an applied neural network for servo control and knowledge-based approximation. The neural network in the servo control level is based on a numerical manipulation, while the knowledge based part is symbolic manipulation. The knowledge base part develops control strategies symbolically for the servo level. The neural network compensates for vagueness in the control strategies, nonlinearities of the system and uncertainties in its environment using neuromorphic control.     

Fault Accommodation for a Class of Nonlinear Uncertain Systems With Event-Triggered Input

The event-triggered fault accommodation problem for a class of nonlinear uncertain systems is considered in this paper.The control signal transmission from the controller to the system is determined by an event-triggering scheme with relative and constant triggering thresholds.Considering the event-induced control input error and system fault threat,a novel eventtriggered active fault accommodation scheme is designed,which consists of an event-triggered nominal controller for the time period before detecting the occurrence of faults and an adaptive approximation based event-triggered fault accommodation scheme for handling the unknown faults after detecting the occurrence of faults.The closed-loop stability and inter-event time of the proposed fault accommodation scheme are rigorously analyzed.Special cases for the fault accommodation design under constant triggering threshold are also derived.An example is employed to illustrate the effectiveness of the proposed fault accommodation scheme.     

一类非线性不确定系统的神经网络控制

针对一类非线性不确定系统,提出了一种自适 应神经网络控制方案．被控系统是部分已知的,其中系统已知的动态特性被用来设计保证标 称模型稳定的反馈控制器,而基于神经网络的动态补偿器则用于补偿系统的非线性不确定性 ,从而可以保证系统输出跟踪误差渐近收敛于0．     

Concise adaptive neural control of uncertain nonlinear systems with periodically nonlinear time-varying parameterized disturbances

This note makes effort at the problem of robust adaptive control for uncertain nonlinear systems with periodically nonlinear time-varying parameterized disturbances with known common period. A concise adaptive neural control scheme is developed by fusion of the Backstepping method and a novel MLN (minimum learning network) technique. In the control scheme, the intermediate variables, i.e., the virtual controls, do not appear in the finally actual control effort, and only one neural network is introduced to compensate sum of the uncertainties in the whole system. Thus, the outstanding advantage of the corresponding scheme is that the control law with a concise structure is model-independent and easy to implement in the process industries due to less computational burden. Based on the Lyapunov synthesis, it is proven that with the developed concise adaptive controller, all the signals in the closed-loop system converge to a small neighborhood of zero. Finally, three comparison examples demonstrate the effectiveness of the proposed algorithm.