Dynamic modeling and adaptive robust boundary control of a flexible robotic arm with 2‐dimensional rigid body rotation

This paper investigates the control of a single‐link flexible robot manipulator with a tip payload appointed to rotate about 2 perpendicular axes in space. The control objective is to regulate the rigid body rotation of the manipulator with guaranteeing the stability of its vibration in the presence of exogenous disturbances. To achieve this, a Lyapunov‐based control design procedure is used and accomplished in some steps. First, the partial differential equation (PDE) dynamic model governing the rigid‐flexible hybrid motion of the arm is derived by applying Hamilton's principle. Next, based on the developed PDE model, an adaptive robust boundary control is established using the Lyapunov redesign approach. To this end, an adaptation mechanism is proposed so that the robust boundary control gains are dynamically updated online and there is no need for prior knowledge of disturbance upper bounds. The actuators and sensors are fully implemented at the arm boundary without using distributed actuators or sensors. Furthermore, in order to avoid control errors resulting from the spillover, control design is directly based on infinite‐dimensional PDE model without resorting to model truncation. Simulation results illustrate the efficacy of the considered method.     

Neural network‐based adaptive attitude tracking control for flexible spacecraft with unknown high‐frequency gain

Adaptive control design using neural networks (a) is investigated for attitude tracking and vibration stabilization of a flexible spacecraft, which is operated at highly nonlinear dynamic regimes. The spacecraft considered consists of a rigid body and two flexible appendages, and it is assumed that the system parameters are unknown and the truncated model of the spacecraft has finite but arbitrary dimension as well, for the purpose of design. Based on this nonlinear model, the derivation of an adaptive control law using neural networks (NNs) is treated, when the dynamics of unstructured and state‐dependent nonlinear function are completely unknown. A radial basis function network that is used here for synthesizing the controller and adaptive mechanisms is derived for adjusting the parameters of the network and estimating the unknown parameters. In this derivation, the Nussbaum gain technique is also employed to relax the sign assumption for the high‐frequency gain for the neural adaptive control. Moreover, systematic design procedure is developed for the synthesis of adaptive NN tracking control with L₂ ‐gain performance. The resulting closed‐loop system is proven to be globally stable by Lyapunov's theory and the effect of the external disturbances and elastic vibrations on the tracking error can be attenuated to the prescribed level by appropriately choosing the design parameters. Numerical simulations are performed to show that attitude tracking control and vibration suppression are accomplished in spite of the presence of disturbance torque/parameter uncertainty. Copyright © 2009 John Wiley & Sons, Ltd.     

Hierarchical structure-based adaptive fault-tolerant consensus control for multiple 3-DOF laboratory helicopters

This study investigates the consensus problem of multiple 3-DOF laboratory helicopters modeled with system nonlinearity, uncertainty, and actuator faults. The simultaneous additives and partial loss of effectiveness actuator faults are considered. The fault detection hierarchy, the healthy control hierarchy, and the fault-tolerant control hierarchy constitute the hierarchical structure of multihelicopter systems. The fault-tolerant consensus protocol is switched from the healthy control hierarchy once the actuator fault is detected in the fault detection hierarchy. An adaptive fault-tolerant consensus control scheme is developed on the basis of the instantaneous and integral estimations to compensate simultaneously for system nonlinearity, uncertainty, and actuator faults and to guarantee the mean-square consensus in a completely distributed form. Simulation results are presented to validate the effectiveness of the proposed adaptive fault-tolerant consensus control algorithm.     

Fault tolerant synchronization for a class of complex interconnected neural networks with delay

This paper is concerned with the fault tolerant synchronization problem for a class of complex interconnected neural networks against sensor faults. As sensor faults may lead to performance degradation or even instability of the whole network, fault tolerant control laws are designed to guarantee the controlled synchronization of the complex interconnected neural networks. On the basis of Lyapunov stability theory and adaptive schemes, three kinds of fault tolerant control laws are designed on the basis of linear matrix inequality technique. One is the passive fault tolerant control law, the other two are adaptive fault tolerant control laws. The latter two methods use the adaptive adjusting mechanism of the coupling coefficients to ensure the synchronization of the networks in the presence of sensor faults. Simulation results are given to verify the effectiveness of the proposed methods. Copyright © 2013 John Wiley & Sons, Ltd.     

A model reference tracking based on an active fault tolerant control for LPV systems

This work deals with the problem of a model reference tracking based on the design of an active fault tolerant control for linear parameter‐varying systems affected by actuator faults and unknown inputs. Linear parameter‐varying systems are described by a polytopic representation with measurable gain scheduling functions. The main contribution is to design an active fault tolerant controller whose control law is described by an adaptive proportional integral structure. This one requires 3 types of online information, which are reference outputs, measured real outputs, and the fault estimation provided by a model reference, sensors, and an adaptive polytopic observer, respectively. These types of information are used to reconfigure the designed controller, which is able to compensate the fault effects and to make the closed‐loop system able to track reference outputs in spite of the presence of actuator faults and disturbances. The controller and the observer gains are obtained by solving a set of linear matrices inequalities. Performances of the proposed method are compared to another previous method to underline the relevant results.     

Distributed adaptive neural network fixed-time leader-follower attitude consensus control for multiple rigid spacecraft

In this paper, the problem of fixed-time attitude consensus control is addressed for a group of rigid spacecraft in the presence of inertia uncertainties and external disturbances. By applying the adaptive technique and neural network approximation technique to handle the disturbances and uncertainties, an adaptive neural network-based distributed protocol is proposed to achieve attitude consensus control for multiple rigid spacecraft. The proposed distributed attitude consensus protocol is composed of a group of distributed fixed-time observers for followers to estimate the leader's information and an adaptive neural network-based fixed-time sliding mode control law to realize attitude tracking control. Rigorous proofs are provided to demonstrate that the estimation errors of the proposed observers are convergent in a fixed time. Further, it is also proven that attitude tracking errors reach some adjustable regions in a fixed time under the proposed attitude consensus protocol. Numerical simulations are conducted to illustrate the performance of the proposed distributed attitude consensus protocol.     

Observer-based finite time adaptive fault tolerant control for nonaffine nonlinear systems with actuator faults and disturbances

This paper studies the problem of observer-based finite time adaptive fault tolerant control for nonaffine nonlinear systems with actuator faults and disturbances. Based on mean value theorem and convex combination method, a adaptive neural observer with virtual control coefficients is designed to estimate the systems states. Then, by using funnel Lyapunov function and backstepping method, a finite time control scheme is designed in the presence of disturbances and actuator faults. The stability analysis proves that tracking errors can converge to the prescribed performance bound in a finite time and all signals are uniformly ultimately bounded. Finally, simulation results verify efficiency of the studied approach.     

Nonlinear partial differential equation modeling and adaptive fault-tolerant vibration control of flexible rotatable manipulator in three-dimensional space

In this article, we investigate the problem of nonlinear modeling and adaptive boundary vibration control with actuator failure for a flexible rotatable manipulator in three-dimensional space, which is made up of a rotatable base and a flexible manipulator. In order to accurately reflect the characteristics of the distributed parameters, the Hamilton principle is introduced to derive the dynamic model expressed by partial differential equations (PDEs). Based on the model, an innovative boundary control scheme is proposed to eliminate the deflection and vibration simultaneously, and to guarantee that the rotatable base and the flexible manipulator can track the desired angle respectively. The adaptive law is developed to estimate the loss of the actuator. The effectiveness of the designed controller is verified from both theoretical analysis and numerical simulation.     

Adaptive fault tolerant control against actuator faults

This paper investigates the problem of adaptive fault tolerant control for a class of dynamic systems with unknown un‐modeled actuator faults. The fault model is assumed to be an unknown nonlinear function of control input, not in the traditional form in which the faults can be described as gain and/or bias faults. Using the property of the basic function of neural networks and the implicit function theorem, a novel neural networks‐based fault tolerant controller is designed. Finally, the lateral dynamics of a front‐wheeled steered vehicle is used to demonstrate the efficiency of the proposed design techniques. Copyright © 2016 John Wiley & Sons, Ltd.     

Regulation-triggered adaptive control of a hyperbolic PDE-ODE model with boundary interconnections

We present a certainty equivalence-based adaptive boundary control scheme with a regulation-triggered batch least-squares identifier, for a heterodirectional transport partial differential equation-ordinary differential equation (PDE-ODE) system where the transport speeds of both transport PDEs are unknown. We use a nominal controller which is fed piecewise-constant parameter estimates from an event-triggered parameter update law that applies a least-squares estimator to data “batches” collected over time intervals between the triggers. A parameter update is triggered by an observed growth in the norm of the PDE state. The proposed triggering-based adaptive control guarantees: (1) the absence of a Zeno phenomenon; (2) parameter estimates are convergent to the true values in finite time (from most initial conditions); (3) exponential regulation of the plant states to zero. The effectiveness of the proposed design is verified by a numerical example.     

Adaptive finite‐time actuator fault‐tolerant coordinated attitude control of multispacecraft with input saturation

In this study, for nonrigid spacecraft formation, a distributed adaptive finite‐time actuator fault‐tolerant (FTAFT) coordinated attitude tracking control (CATC) issue is addressed. Aiming at stabilizing the spacecraft formation flying system during a limited time, two distributed adaptive FTAFT CATC strategies are presented. Initially, on basis of distributed finite‐time observer (DFTO), adaptive control, consensus approach, graph theory, and finite‐time theory, we develop a distributed adaptive FTAFT coordinated attitude tracking controller to repress the impact of the external state‐dependent and state‐independent disturbance, unknown time‐varying inertia uncertainty, and actuator fading or fault. Then, combining with the proposed controller, a distributed adaptive FTAFT control law with input saturation subjected to physical limitations of actuator is further designed. In addition, a self‐adjusting matrix (SAM) is proposed to improve the actuators' performance. With the two proposed CATC strategies, the followers can synchronize with the leader. Simulations demonstrated the validity of the designed control laws.     

Approximation-based adaptive fault compensation backstepping control of fractional-order nonlinear systems: An output-feedback scheme

An observer-based adaptive fuzzy backstepping approach is proposed for nonlinear systems with respect to fractional-order differential equations, unmatched uncertainties, unmeasured states, and actuator faults. The approximation capability of fuzzy logic system and minimal learning parameter approaches are applied to identify uncertain functions in a simultaneous manner. For estimating the unavailable conditions, a fuzzy fractional-order state-observer is extended. Applying fault-tolerant approach in a backstepping design methodology would provide a new fault-tolerant adaptive fuzzy output-feedback approach for fractional-order strict-feedback systems. This control structure would assure the considered system stability through selection of the appropriate Lyapunov candidate function. Two numerical simulations are run to exhibit the validity herein.     

Disturbance observer-based adaptive boundary iterative learning control for a rigid-flexible manipulator with input backlash and endpoint constraint

In this article, an observer-based adaptive boundary iterative learning control law is developed for a class of two-link rigid-flexible manipulator with input backlash, the unknown external disturbance, and the endpoint constraint. To tackle the backlash nonlinearities and ensure the vibration suppression, the disturbance observers based upon the iterative learning conception are considered in the adaptive boundary control design. A barrier Lyapunov function is incorporated with boundary control law to restrict the endpoint state. Based on the defined barrier composite energy function, the tracking angle error convergence of the rigid part is guaranteed, and the vibrations of the flexible part are suppressed through the rigorous analysis. Finally, a numerical simulation is provided to illustrate the effectiveness of the proposed control.     

双神经网络交流传动自适应规划控制

将Hopfield神经网络应用于交流传动系统的自适应控制，通过神经网络来规划交流调速系统的速度控制器动态输出；并将Hopfield神经网络控制器代替矢量控制系统中的转速调节器，使速度控制器具有对某些参数变化良好的鲁棒性。对于不可控的负载转矩分量，加入神经网络负载转矩在线跟踪控制器，形成参数自动跟踪神经网络，构成具有参数在线跟踪功能的交流传动双神经网络自适应规划控制模式，进一步提高了系统的性能．仿真结果证明了该控制方案的有效性．     

Active fault‐tolerant control for near space vehicles based on reference model adaptive sliding mode scheme

In this paper, an adaptive sliding mode (ASM) scheme is proposed for fault identification and fault‐tolerant control of near space vehicles (NSVs). First, the attitude dynamic model is introduced, and a baseline controller based on reference sliding mode scheme is designed in the case of no faults. Then fault parameterizations with actuator dynamics is presented for several classes of faults: lock‐in‐place, float, hard‐over, and loss of effectiveness. On the basis of adaptive observer design, fault parameters can be accurately estimated on‐line. Furthermore, an ASM fault‐tolerant controller is designed for both cases of actuator dynamic faults and control effector damage. Finally, simulation experiments show that the proposed ASM scheme is able to quickly and accurately identify faults and reconfigure the controller, resulting in excellent overall system performance. Copyright © 2012 John Wiley & Sons, Ltd.     

Adaptive neural tracking control for high angle of attack maneuver with average dwell time

This article attempts to study the high angle of attack maneuver from the perspective of switched system control. In view of the complex aerodynamic characteristics, an improved longitudinal attitude motion model is presented, which is a switched stochastic nonstrict feedback nonlinear system with distributed delays. The significant design difficulty is the completely unknown diffusion and drift terms and distributed delays with all state variables. Based on a technical lemma and neural networks, an improved smooth state feedback control law for nonstrict feedback systems is proposed without any growth assumptions. To eliminate the influence of distributed delays, an improved Lyapunov–Krasovskii function is constructed, which skillfully removes the constraint of the upper bound of the delay change rate. Then, by combining the average dwell-time scheme and stochastic backstepping technique, an adaptive neural network tracking control law is designed, which extends a newly proposed switched system stability condition to the stochastic switched system. Theoretical analysis and flight control simulation experiments are provided to illustrate the effectiveness of the proposed control method.     

Observer-based single-network incremental adaptive dynamic programming for fault-tolerant control of nonlinear systems with actuator faults

A model-free incremental adaptive fault-tolerant control (FTC) scheme is proposed for a class of nonlinear systems with actuator faults. To deal with actuator faults and guarantee the approximate optimal performance of the nominal nonlinear system without any prior knowledge of system dynamics, a single-network incremental adaptive dynamic programming (SIADP) algorithm based on incremental neural network observer is developed to design an active fault-tolerant control (AFTC) policy. An approximate linear time-varying system is obtained by incremental nonlinear technique, in which the relevant matrix parameters are identified by recursive least square estimation. Then, a SIADP algorithm-based fault-tolerant controller is developed. Based on the redundancy characteristic and function of actuators, a grouping scheme of actuators is introduced. An incremental neural network observer is designed to approximate the actuator faults. The novel SIADP scheme is constructed with a simplified single critic neural network to shorten the learning time and decrease the computational burden in the control process, in which the norm of the weight estimations of critic neural network is updated. Moreover, based on the Lyapunov theorem, the uniformly ultimately bounded stability of the closed-loop incremental system is proved. Finally, simulations are given to verify the effectiveness of the proposed FTC scheme.     

解耦自适应反步法永磁直线同步电机混沌控制

针对永磁直线同步电机运行过程中存在的混沌动态行为,构建电机混沌模型,编写程序,计算其任意参数下的最大Lyapunov指数.通过分析电机的混沌动态特性,构造状态反馈解耦,降低混沌系统阶数.基于永磁同步电机的状态反馈解耦模型设计反步法混沌控制器.针对系统中可能含有的不确定性参数,提出解耦模型下的自适应反步法混沌控制,构造虚拟控制量,设计控制律,实时跟踪预测系统参数.为使系统状态快速稳定收敛至零点,构造Lyapunov方程进行稳定性分析.仿真结果表明,所提解耦自适应反步法能使永磁同步电机混沌系统快速恢复到稳定运行状态,鲁棒性强,响应速度快、控制精度高.     

Multiple characteristic model‐based golden‐section adaptive control: Stability and optimization

The characteristic model‐based golden‐section adaptive control (CM‐GSAC) law has been developed for over 20 years in China with a broad range of applications in various fields. However, quite a few theoretical problems remain open despite its satisfying performance in practice. This paper revisits the stability of the CM‐GSAC from its very beginning and explores the underlying implications of the so‐called golden‐section parameter l₂≈0.618. The closed‐loop system, which consists of the CM and the GSAC, is a discrete time‐varying system, and its stability is discussed from three perspectives. First, attentions have been paid to select the optimal controller coefficients such that the closed‐loop system exhibits the best transient performance in the worst case. Second, efforts are made to improve the robustness in the presence of parameter estimation errors, which provide another choice when designing the adaptive controller. Finally, by measuring the slowly time‐varying nature in an explicit inequality form, a bridge is built between the instantaneous stability and the time‐varying stability. In order to relax the constraints on the parameter bounds of the CM, the GSAC is further extended to multiple CMs, which shows more satisfying tracking performance than that of the traditional multiple model adaptive control method. Copyright © 2014 John Wiley & Sons, Ltd.     

Distributed output estimation error observer-based adaptive fault-tolerant consensus tracking control of multi-agent systems

The purpose of this study is to discuss the fully distributed design of output estimation error observer and fault-tolerant consensus tracking control for a class of multi-agent systems with Lipschitz nonlinear dynamics and actuator faults. Firstly, based on the relative output measurements of neighboring agents, the distributed output estimation error observer is developed to adaptively estimate the state and fault information of each agent, and further overcome the difficulties of online updating the adaptive estimations of unknown hyper-parameters. Secondly, to achieve the state consensus tracking goal and compensate for the negative effects of actuator faults, the distributed fault-tolerant consensus tracking control scheme is proposed on the basis of the state estimation and adaptive fault estimation information, and has excellent robustness and consensus tracking control performance. Moreover, sufficient criteria can ensure that consensus tracking error of each agent converges to a small set near the origin. Finally, numerical simulations are provided to show the effectiveness of the proposed fully distributed algorithm.