Trajectory tracking for an autonomous airship using fuzzy adaptive sliding mode control

We present a novel control approach for trajectory tracking of an autonomous airship.First,the dynamics model and the trajectory control problem of an airship are formulated.Second,the sliding mode control law is designed to track a time-varying reference trajectory.To achieve better control performance,fuzzy adaptive sliding mode control is proposed in which the control gains are tuned according to fuzzy rules,and an adaptation law is used to guarantee that the control gains can compensate for model uncertainties of the airship.The stability of the closed-loop control system is proven via the Lyapunov theorem.Finally,simulation results illustrate the effectiveness and robustness of the proposed control scheme.     

Robust control of variable speed autonomous underwater vehicle

Set point tracking control of autonomous underwater vehicle (AUV) via robust model predictive control (RMPC) is considered. Input-constrained RMPC with integral action, which has been developed in our previous work, is used to control the AUV in this study. In order to derive a RMPC control rule, non-linear dynamics of AUV with six degree of freedom is linearized at certain operating points. So, horizontal and vertical plane dynamics of system are represented by linear models which have polytopic uncertainties. Since the derived control rule will be used in real time, the computation time should be reduced. To overcome this computational time problem and get rid of trial–error step of Algorithm 1, a new algorithm is proposed here. The simulations are carried out using the control rule based on this algorithm and these results are presented.     

水下航行器智能航向滑模控制

为了实现对自主水下航行器参考航向的平滑、快速跟踪,提出了一种基于干扰观测器的航向跟踪自适应反演滑模控制算法。针对水池试验中不同程度的水流干扰,利用一种干扰观测器观测系统的不确定性和外部干扰,未观测到的部分干扰采用自适应滑模控制器进行补偿。控制器的设计消除了传统滑模控制的"抖振"现象,且保证了闭环系统的稳定性。同时该控制系统可智能选取控制策略,通过判断水流循环等外界干扰的程度,自动选取合适的控制策略,最终消除外界干扰,提高了跟踪的稳定性和快速性。仿真结果表明,该控制策略能很好地实现智能航向跟踪控制,使跟踪误差在有限时间内快速收敛到零,对外界扰动的变化具有强鲁棒性和良好的自适应性。     

Model predictive and adaptive neural sliding mode control for three-dimensional path following of autonomous underwater vehicle with input saturation

With model uncertainties and input saturation, a novel control method is developed to steer an underactuated autonomous underwater vehicle that realizes the following of the planned path in three-dimensional (3D) space. Firstly, Serret–Frenet frame is applied as virtual target, and the path following errors model in 3D is built. Secondly, the control method which includes kinematic controller and dynamic controller was presented based on cascade control strategy. The kinematic controller, which is responsible for generating a series of constrained velocity signals, is designed based on model predictive control. The adaptive radial basis function neural network is used to estimate the model uncertainty caused by hydrodynamic parameters. Moreover, sliding mode control technology is applied in the design of dynamic controller to improve its robustness. Then, the control effect is compared with that of LOS guidance law and PID controller by simulation experiment. The comparison results show that the proposed algorithm can improve path following effect and reduce input saturation.

     

Robust adaptive fuzzy sliding mode trajectory tracking control for serial robotic manipulators

The ever increasingly stringent performance requirements of industrial robotic applications highlight significant importance of advanced robust control designs for serial robots that are generally subject to various uncertainties and external disturbances. Therefore, this paper proposes and investigates the design and implementation of a robust adaptive fuzzy sliding mode controller in the task space for uncertain serial robotic manipulators. The sliding mode control is well known for its robustness to system parameter variations and external disturbances, and is thus a highly desirable and cost-effective approach to achieve high precision control task for serial robots. The proposed controller is designed based on a fuzzy logic approximation to accomplish trajectory tracking with high accuracy and simultaneously attenuate effects from uncertainties. In the controller, the high-frequency uncertain term is approximated by using a fuzzy logic system while the low-frequency term is adaptively updated in real time based on a parametric adaption law. The control efficacy and effectiveness of the proposed control algorithm are comparatively verified against a recently proposed conventional controller. The test results demonstrate that the proposed controller has better trajectory tracking performances and is more robust against large disturbances than the conventional controller under the same operating conditions.     

SCARA机器人模糊自适应滑模控制

为了提高SCARA机器人的轨迹跟踪控制性能,提出了一种基于非奇异终端滑模面和改进的快速趋近律的滑模控制策略。设计了一种非奇异的快速终端滑模面,可在任意初始状态下收敛到平衡点;改进滑模控制的趋近律,在保证快速趋近的同时可有效抑制抖振,并采用双曲正切函数代替传统的符号函数,有效地消除了高频抖振。采用模糊控制调节趋近律参数,改善初始状态误差过大时引起的力矩冲击问题;基于SCARA机器人模型仿真实验表明,实验结果跟踪性能良好,输出力矩平滑。     

Adaptive tracking control of an autonomous underwater vehicle

This paper presents the trajectory tracking control of an autonomous underwater vehicle(AUV). To cope with parametric uncertainties owing to the hydrodynamic effect, an adaptive control law is developed for the AUV to track the desired trajectory. This desired state-dependent regressor matrix-based controller provides consistent results under hydrodynamic parametric uncertainties.Stability of the developed controller is verified using the Lyapunov s direct method. Numerical simulations are carried out to study the efficacy of the proposed adaptive controller.     

An adaptive fuzzy sliding mode controller for remotely operated underwater vehicles

Sliding mode control is a very attractive control scheme because of its robustness against both structured and unstructured uncertainties as well as external disturbances. In this way, it has been widely employed for the dynamic positioning of remotely operated underwater vehicles. Nevertheless, in such situations the discontinuities in the control law must be smoothed out to avoid the undesirable chattering effects. The adoption of properly designed boundary layers has proven effective in completely eliminating chattering, however, leading to an inferior tracking performance. This work describes the development of a dynamic positioning system for remotely operated underwater vehicles. The adopted approach is primarily based on the sliding mode control strategy and enhanced by an adaptive fuzzy algorithm for uncertainty/disturbance compensation. Using the Lyapunov stability theory and Barbalat’s lemma, the boundedness and convergence properties of the closed-loop signals are analytically proven. The performance of the proposed control scheme is also evaluated by means of numerical simulations.     

基于模糊混合控制的自治水下机器人路径跟踪控制

基于模糊混合控制策略,本文提出了一种用于非线性欠驱动自治水下机器人的鲁棒路径跟踪控制方法.利用Sugeno型模糊推理系统,将PD滑模控制器与非奇异终端滑模控制器光滑连接,构造了模糊混合控制器.它能充分融合这两类控制器的优势,无论系统远离平衡点还是在其附近,都能取得快速收敛的效果.如果,借助于非时间参考量,将该混合控制器用于自治水下机器人路径跟踪控制,将有利于提高它在不确定环境中的跟踪能力.最后,通过仿真计算结果验证了该控制策略的有效性.     

Neural network based adaptive fuzzy PID-type sliding mode attitude control for a reentry vehicle

This work investigates the attitude control of reentry vehicle under modeling inaccuracies and external disturbances. A robust adaptive fuzzy PID-type sliding mode control (AFPID-SMC) is designed with the utilization of radial basis function (RBF) neural network. In order to improve the transient performance and ensure small steady state tracking error, the gain parameters of PID-type sliding mode manifold are adjusted online by using adaptive fuzzy logic system (FLS). Additionally, the designed new adaptive law can ensure that the closed-loop system is asymptotically stable. Meanwhile, the problem of the actuator saturation, caused by integral term of sliding mode manifold, is avoided even under large initial tracking error. Furthermore, to eliminate the need of a priori knowledge of the disturbance upper bound, RBF neural network observer is used to estimate the disturbance information. The stability of the closed-loop system is proved via Lyapunov direct approach. Finally, the numerical simulations verify that the proposed controller is better than conventional PID-type SMC in terms of improving the transient performance and robustness.

     

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.     

自适应模糊全局快速Terminal滑模控制方法

提出了一种自适应模糊全局快速Terminal滑模控制方法,在参数不确定性和外干扰情况下,为解决系统的非线性不确定性提供了一种新途径。与传统模糊Terminal滑模控制相比,通过采用模糊逻辑系统来逼近未知系统函数和开关项;鲁棒自适应律用来减小逼近误差,从而有效降低抖振;证明了该控制方案的稳定性,并将该方案应用在倒立摆系统中。仿真结果验证了该方案的有效性。     

Decentralized adaptive fuzzy sliding mode control for reconfigurable modular manipulators

A stable decentralized adaptive fuzzy sliding mode control scheme is proposed for reconfigurable modular manipulators to satisfy the concept of modular software. For the development of the decentralized control, the dynamics of reconfigurable modular manipulators is represented as a set of interconnected subsystems. A first‐order Takagi–Sugeno fuzzy logic system is introduced to approximate the unknown dynamics of subsystem by using adaptive algorithm. The effect of interconnection term and fuzzy approximation error is removed by employing an adaptive sliding mode controller. All adaptive algorithms in the subsystem controller are derived from the sense of Lyapunov stability analysis, so that resulting closed‐loop system is stable and the trajectory tracking performance is guaranteed. The simulation results are presented to show the effectiveness of the proposed decentralized control scheme. Copyright © 2009 John Wiley & Sons, Ltd.     

基于模糊控制的高超声速飞行器二阶滑模姿态控制

考虑高超声速飞行器飞行过程中气动参数变动导致的不确定,将模糊控制与二阶滑模控制相结合,形成自适应模糊二阶滑模控制器,用于控制高超声速飞行器姿态的飞行系统中.依据奇异摄动理论,设计快速和慢速双闭环系统控制角速率和姿态角.设计二阶滑模控制器用于有效地衰减抖振,同时对姿态角指令实现准确和快速跟踪.采用自适应模糊逻辑逼近高超声速飞行器动力学和运动学模型中的不确定部分,以对控制器进行有效补偿,基于Lyapunov稳定性理论,推导模糊规则参数的自适应律,确保整个闭环控制系统的稳定.仿真结果表明,所提出的高超声速飞行器的自适应模糊滑模控制系统能够有效抑制气动参数摄动的影响,对姿态角指令有较好的跟踪性能.     

Robust adaptive trajectory tracking control of underactuated autonomous underwater vehicles with prescribed performance

In this paper, a novel robust adaptive trajectory tracking control scheme with prescribed performance is developed for underactuated autonomous underwater vehicles (AUVs) subject to unknown dynamic parameters and disturbances. A simple error mapping function is proposed in order to guarantee that the trajectory tracking error satisfies the prescribed performance. A novel additional control based on Nussbaum function is proposed to handle the underactuation of AUVs. The compounded uncertain item caused by the unknown dynamic parameters and disturbances is transformed into a linear parametric form with only single unknown parameter called virtual parameter. On the basis of the above, a novel robust adaptive trajectory tracking control law is developed using dynamic surface control technique, where the adaptive law online provides the estimation of the virtual parameter. Strict stability analysis indicates that the designed control law ensures uniform ultimate boundedness of the AUV trajectory tracking closed‐loop control system with prescribed tracking performance. Simulation results on an AUV in two different disturbance cases with dynamic parameter perturbation verify the effectiveness of our adaptive trajectory tracking control scheme.     

基于终端滑模的机械手鲁棒自适应控制

对于不确定的机械手系统,提出一种鲁棒自适应控制方法,用自适应控制来估计系统的未知参数,用终端滑模控制来减少不确定因素的影响,为了避免因干扰的存在使自适应的估计参数发生漂移,引入死区自适应控制．仿真表明,滑模控制不仅抑制了误差,而且消除了死区自适应算法的局限性,该算法在取得较好控制效果的同时,具有很强的鲁棒性．     

Stable nonlinear adaptive controller for an autonomous underwater vehicle using neural networks

In general, the dynamics of autonomous underwater vehicles (AUVs) are highly nonlinear and their hydrodynamic coefficients vary with different operating conditions. For this reason, high performance control system for an AUV usually should have the capacities of learning and adaptation to the time-varying dynamics of the vehicle. In this article, we present a robust adaptive nonlinear control scheme for an AUV, where a linearly parameterized neural network (LPNN) is introduced to approximate the uncertainties of the vehicle's dynamics, and the basis function vector of the network is constructed according to the vehicle's physical properties. The proposed control scheme can guarantee that all of the signals in the closed-loop system are uniformly ultimately bounded (UUB). Numerical simulation studies are performed to illustrate the effectiveness of the proposed control scheme.     

Robust sliding mode control using adaptive switching gain for induction motors

A robust sliding mode approach combined with a field oriented control （FOC） for induction motor （IM） speed control is presented. The proposed sliding mode control （SMC） design uses an adaptive switching gain and an integrator. This approach guarantees the same robustness and dynamic performance of traditional SMC algorithms. And at the same time, it attenuates the chattering phenomenon, which is the main drawback in actual implementation of this technique. This approach is insensitive to uncertainties and permits to decrease the requirement for the bound of these uncertainties. The stability and robustness of the closed- loop system are proven analytically using the Lyapunov synthesis approach. The proposed method attenuates the effect of both uncertainties and external disturbances. Experimental results are presented to validate the effectiveness and the good performance of the developed method.     

Robust discrete adaptive input-output-based sliding mode controller

A robust input-output-based discrete adaptive sliding mode controller is proposed. It combines an integral action, a nonlinear output feedback, an adjustable sliding mode and an adaptive plant parameter estimator. The controller design is carried out via the Lyapunov direct method. A pole assignment procedure is developed for determination of the integral control gain and the coefficients of the sliding mode hyperplane. An on-line update for coefficients of the hyperplane is used to improve control loop behaviour further. Compared with the optimally tuned proportional-integral-derivative (PID) controller, the new controller has increased robustness with regard to the variation in the main process parameters and it has much better set point tracking characteristics. The new controller also exhibits very good disturbance rejection property comparable work or better than to that obtained by the optimally tuned PID controller. Simulation experiments are made to illustrate the quality and robustness of control achieved.