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.     

MIMO adaptive fuzzy terminal sliding-mode controller for robotic manipulators

A new design approach of a multiple-input-multiple-output (MIMO) adaptive fuzzy terminal sliding-mode controller (AFTSMC) for robotic manipulators is described in this article. A terminal sliding-mode controller (TSMC) can drive system tracking error to converge to zero in finite time. The AFTSMC, incorporating the fuzzy logic controller (FLC), the TSMC, and an adaptive scheme, is designed to retain the advantages of the TSMC while reducing the chattering. The adaptive law is designed on the basis of the Lyapunov stability criterion. The self-tuning parameters are adapted online to improve the performance of the fuzzy terminal sliding-mode controller (FTSMC). Thus, it does not require detailed system parameters for the presented AFTSMC. The simulation results demonstrate that the MIMO AFTSMC can provide a reasonable tracking performance.     

机器人操作器的自适应模糊滑模控制器设计

针对机器人动力学系统提出了一种基于模糊逻辑的自适应模糊滑模控制方案.根据滑模控制原理并利用模糊系统的逼近能力设计控制器,基于李雅谱诺夫方法设计自适应律,证明了闭环模糊控制系统的稳定性和跟踪误差的收敛性.控制结构简单,不需要复杂的运算.该设计方案柔化了控制信号,减轻了一般滑模控制的抖振现象.仿真结果表明了所提控制策略的有效性.     

An adaptive fuzzy controller based on sliding mode for robotmanipulators

This paper considers adaptive fuzzy control of robotic manipulators based on sliding mode. It is first shown that an adaptive fuzzy system with the system representative point (RP, or as is often termed, a switching function in variable structure control (VSC) theory) and its derivative as inputs, can approximate the robot nonlinear dynamics in the neighborhood of the switching hyperplane. Then a new method for designing an adaptive fuzzy control system based on sliding mode is proposed for the trajectory tracking control of a robot with unknown nonlinear dynamics. The system stability and tracking error convergence are also proved by Lyapunov techniques.     

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.     

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．     

GA-based modified adaptive fuzzy sliding mode controller for nonlinear systems

In this paper, the stability analysis of the GA-based adaptive fuzzy sliding model controller for a nonlinear system is presented. First, an uncertain and nonlinear plant for the tracking of a reference trajectory is well approximated and described via the reference model and the fuzzy model involving fuzzy logic control rules. Next, the difficulty in designing a fuzzy sliding mode controller (FSMC) capable of rapidly and efficiently controlling complex and nonlinear systems is how to select the most appropriate initial values for the parameter vector. The initial values of the consequent parameter vector are decided via the genetic algorithm. After this, a modified adaptive law can be adopted to find the best high-performance parameters for the fuzzy sliding model controller. The adaptive fuzzy sliding model controller is derived to simultaneously stabilize and control the system. The stability of the nonlinear system is ensured by the derivation of the stability criterion based upon Lyapunov’s direct method. Finally, a numerical simulation is provided as an example to demonstrate the control methodology.     

船舶航向非线性系统的多滑模自适应模糊控制

针对参数未知的船舶航向非线性控制系统数学模型,在考虑舵机伺服机构特性的情况下,船舶航向控制问题就成为一个虚拟控制系数未知的非匹配不确定非线性控制问题.基于多滑模设计方法和模糊逻辑系统的逼近能力,提出了一种多滑模自适应模糊控制算法,通过引入非连续投影算法和积分型Lyapunov函数,提高了系统在抑制参数漂移、控制器奇异等方面的能力.借助Lyapunov函数证明了所设计控制器使最终的闭环非匹配不确定船舶运动非线性系统中的所有信号有界,且跟踪误差收敛到零.仿真研究表明:该算法与传统的PID控制相比,具有较好的跟踪能力和自适应能力.     

Adaptive terminal sliding mode control for rigid robotic manipulators

In order to apply the terminal sliding mode control to robot manipulators, prior knowledge of the exact upper bound of parameter uncertainties, and external disturbances is necessary. However, this bound will not be easily determined because of the complexity and unpredictability of the structure of uncertainties in the dynamics of the robot. To resolve this problem in robot control, we propose a new robust adaptive terminal sliding mode control for tracking problems in robotic manipulators. By applying this adaptive controller, prior knowledge is not required because the controller is able to estimate the upper bound of uncertainties and disturbances. Also, the proposed controller can eliminate the chattering effect without losing the robustness property. The stability of the control algorithm can be easily verified by using Lyapunov theory. The proposed controller is tested in simulation on a two-degree-of-freedom robot to prove its effectiveness.     

A new terminal sliding mode control for robotic manipulators

In this study, a new terminal sliding mode control approach is developed for robotic manipulators based on finite-time stability theory and differential inequality principle. The corresponding stability analysis is presented to lay a foundation for theoretical understanding to the underlying design issue as well as safe operation for real system. An illustrative example of a two-link rigid robotic manipulator is presented to validate effectiveness of the proposed approach.     

An exponentially convergent adaptive sliding mode control of robot manipulators

A new robust adaptive algorithm for control of robot manipulators is proposed to account for a desired transient response with global exponential convergence of tracking errors without any persistent excitating assumption on the regressor. Its novelty lies in a new dynamic sliding surface that allows a systematic combination of adaptive control and variable structure control to yield a sliding mode inside an adaptive control loop. During sliding mode, parameter uncertainty appears in terms of known variables in such a manner that a new robust parameter estimator with enhanced stability properties is established. On the other hand, if the regressor meets the persistent exciting condition, the global uniform exponential stability of the equilibrium concerning the adaptive closed-loop error equation is easily established. The proposed controller from the VSS viewpoint relaxes the longstanding condition on a priori knowledge of the size of the parametric uncertainty to induce a sliding mode. On the other hand, from the adaptive control viewpoint it relaxes the standard assumption of the persistent excitation on the regressor to obtain the exponential convergence of tracking errors. Also, the stability against time-varying parameters is briefly discussed. Concluding remarks concerning its structural behaviour are given, and computer simulation data show a robust performance.     

Stable adaptive controller design of robotic manipulators via neuro‐fuzzy dynamic inversion

In this paper, a stable adaptive control approach is developed for the trajectory tracking of a robotic manipulator via neuro‐fuzzy (NF) dynamic inversion, an inverse model constructed by the dynamic neuro‐fuzzy (DNF) model with desired dynamics. The robot neuro‐fuzzy model is initially built in the Takagi‐Sugeno (TS) fuzzy framework with both structure and parameters identified through input/output (I/O) data from the robot control process, and then employed to dynamically approximate the whole robot dynamics rather than its nonlinear components as is done by static neural networks (NNs) through parameter learning algorithm. Since the NF dynamic inversion comprises a cluster of reference trajectories connecting the initial state to the desired state of the robot, the dynamic performance in the initial control stage of robot trajectory tracking can be guaranteed by choosing the optimum reference trajectory. Furthermore, the assumption that the robot states should be on a compact set can be excluded by NF dynamic inversion design. The system stability and the convergence of tracking errors are guaranteed by Lyapunov stability theory, and the learning algorithm for the DNF system is obtained thereby. Finally, the viability and effectiveness of the proposed control approach are illustrated through comparing with the dynamic NN (DNN) based control approach. © 2005 Wiley Periodicals, Inc.     

Continuous finite-time control for robotic manipulators with terminal sliding mode

A continuous finite-time control scheme for rigid robotic manipulators is proposed using a new form of terminal sliding modes. The robustness of the controller is established using the Lyapunov stability theory. Theoretical analysis and simulation results show that faster and high-precision tracking performance is obtained compared with the conventional continuous sliding mode control method.     

Neural network-based sliding mode adaptive control for robot manipulators

This paper addresses the robust trajectory tracking problem for a robot manipulator in the presence of uncertainties and disturbances. First, a neural network-based sliding mode adaptive control (NNSMAC), which is a combination of sliding mode technique, neural network (NN) approximation and adaptive technique, is designed to ensure trajectory tracking by the robot manipulator. It is shown using the Lyapunov theory that the tracking error asymptotically converge to zero. However, the assumption on the availability of the robot manipulator dynamics is not always practical. So, an NN-based adaptive observer is designed to estimate the velocities of the links. Next, based on the observer, a neural network-based sliding mode adaptive output feedback control (NNSMAOFC) is designed. Then it is shown by the Lyapunov theory that the trajectory tracking errors, the observer estimation errors asymptotically converge to zero. The effectiveness of the designed NNSMAC, the NN-based adaptive observer and the NNSMAOFC is illustrated by simulations.     

Improved adaptive fuzzy sliding mode controller for robust fault tolerant of a Quadrotor

International Journal of Control, Automation and Systems - In this paper, a model for a Quadrotor helicopter has been considered when a fault has been occurred in its actuators. The sliding mode...     

Gain adaptation in sliding mode control of robotic manipulators

In this paper, a novel scheme is proposed to adapt the gains of a sliding mode controller (SMC) so that the problems faced in its practical implementations as a motion controller are overcome. A Lyapunov function is selected for the design of the SMC and an MIT rule is used for gain adaptation. The criterion that is minimized for gain adaptation is selected as the sum of the squares of the control signal and the sliding surface function. This novel approach is tested on a scara-type robot manipulator. The experimental results presented prove its efficacy.     

An adaptive full order sliding mode controller for mismatched uncertain systems

In this paper, an adaptive full order sliding mode (FOSM) controller is proposed for strict feedback nonlinear systems with mismatched uncertainties. The design objective of the controller is to track a specified trajectory in presence of significant mismatched uncertainties. In the first step the dynamic model for the first state is considered by the desired tracking signal. After the first step the desired dynamic model for each state is defined by the previous one. An adaptive tuning law is developed for the FOSM controller to deal with the bounded system uncertainty. The major advantages offered by this adaptive FOSM controller are that advanced knowledge about the upper bound of the system uncertainties is not a necessary requirement and the proposed method is an effective solution for the chattering elimination from the control signal. The controller is designed considering the full-order sliding surface. System robustness and the stability of the controller are proved by using the Lyapunov technique. A systematic adaptive step by step design method using the full order sliding surface for mismatched nonlinear systems is presented. Simulation results validate the effectiveness of the proposed control law.     

用于刚性机械手的无抖振快速终端滑模控制

提出一种用于刚性机械手的无抖振动终端滑模鲁棒控制器。快速终端滑模综合了终端滑模和传统线性滑模的优，能在有限时间内到达平衡点，并降低系统稳态误差。采用优化方法推导出系统的跟踪精度和用于消除抖振的饱和函数和函数宽度之间的数学关系。利用系统的参数化模型，可将参数的不确定部分从回归矩阵中分离出来.根据每个参数不确定范围设计鲁棒控制器。仿真结果证明了该方法的有效性。     

Adaptive neuro fuzzy controller for adaptive compliant robotic gripper

The requirement for new flexible adaptive grippers is the ability to detect and recognize objects in their environments. It is known that robotic manipulators are highly nonlinear systems, and an accurate mathematical model is difficult to obtain, thus making it difficult то control using conventional techniques. Here, a novel design of an adaptive neuro fuzzy inference strategy (ANFIS) for controlling input displacement of a new adaptive compliant gripper is presented. This design of the gripper has embedded sensors as part of its structure. The use of embedded sensors in a robot gripper gives the control system the ability to control input displacement of the gripper and to recognize particular shapes of the grasping objects. Since the conventional control strategy is a very challenging task, fuzzy logic based controllers are considered as potential candidates for such an application. Fuzzy based controllers develop a control signal which yields on the firing of the rule base. The selection of the proper rule base depending on the situation can be achieved by using an ANFIS controller, which becomes an integrated method of approach for the control purposes. In the designed ANFIS scheme, neural network techniques are used to select a proper rule base, which is achieved using the back propagation algorithm. The simulation results presented in this paper show the effectiveness of the developed method.