Wall-following control of a two-wheeled mobile robot

Wall-following control problem for a mobile robot is to move it along a wall at a constant speed and keep a specified distance to the wall.This paper proposes wall-following controllers based on Lyapunov function candidate for a two-wheeled mobile robot (MR) to follow an unknown wall. The mobile robot is considered in terms of kinematic model in Cartesian coordinate system. Two wall-following feedback controllers are designed: full state feedback controller and observer-based controller. To design the former controller, the errors of distance and orientation of the mobile robot to the wall are defined, and the feedback controller based on Lyapunov function candidate is designed to guarantee that the errors converge to zero asymptotically. The latter controller is designed based on Busawon’s observer as only the distance error is measured. Additionally, the simulation and experimental results are included to illustrate the effectiveness of the proposed controllers.     

输入受限的非完整移动机器人的自适应模糊控制

提出了一种非完整移动机器人饱和自适应模糊轨迹跟踪控制方法,该方法基于反演技术分别设计了系统的运动学控制器和动力学控制器。运动学控制器通过引入分流控制技术解决了初始速度跳变引起的控制量突变问题,动力学控制器利用饱和函数和受限控制参数实现了其有界力矩控制。自适应模糊控制器将模糊逻辑系统与自适应方法相结合,有效消除了常规方法难以解决的系统未知不确定性对系统的影响。通过Lyapunov直接法证明了该系统是收敛且渐进稳定的。仿真结果验证了所设计控制器的良好控制性能和强鲁棒性。     

A novel sliding-mode control of induction motor using space vector modulation technique

This paper presents a novel sliding-mode control method for torque control of induction motors. The control principle is based on sliding-mode control combined with space vector modulation technique. The sliding-mode control contributes to the robustness of induction motor drives, and the space vector modulation improves the torque, flux, and current steady-state performance by reducing the ripple. The Lyapunov direct method is used to ensure the reaching and sustaining of sliding mode and stability of the control system. The performance of the proposed system is compared with those of conventional sliding-mode controller and classical PI controller. Finally, computer simulation results show that the proposed control scheme provides robust dynamic characteristics with low torque ripple.     

Stable modeling based control methods using a new RBF network

This paper presents a novel model with radial basis functions (RBFs), which is applied successively for online stable identification and control of nonlinear discrete-time systems. First, the proposed model is utilized for direct inverse modeling of the plant to generate the control input where it is assumed that inverse plant dynamics exist. Second, it is employed for system identification to generate a sliding-mode control input. Finally, the network is employed to tune PID (proportional + integrative + derivative) controller parameters automatically. The adaptive learning rate (ALR), which is employed in the gradient descent (GD) method, provides the global convergence of the modeling errors. Using the Lyapunov stability approach, the boundedness of the tracking errors and the system parameters are shown both theoretically and in real time. To show the superiority of the new model with RBFs, its tracking results are compared with the results of a conventional sigmoidal multi-layer perceptron (MLP) neural network and the new model with sigmoid activation functions. To see the real-time capability of the new model, the proposed network is employed for online identification and control of a cascaded parallel two-tank liquid-level system. Even though there exist large disturbances, the proposed model with RBFs generates a suitable control input to track the reference signal better than other methods in both simulations and real time.     

非完整移动机器人的自适应全局轨迹跟踪控制

针对含有未知参数的移动机器人运动学模型,利用自适应反演控制技术,讨论了两后轮角速度为控制输入的非完整移动机器人轨迹跟踪问题,构造了具有全局渐近稳定性的自适应轨迹跟踪控制器,该方法将系统分解为低阶子系统来处理,利用中间虚拟控制量和部分Lyapunov函数简化了控制器的设计并具有直观的稳定性分析。仿真结果验证了所设计控制器的有效性和正确性。     

New hybrid adaptive neuro-fuzzy algorithms for manipulator control with uncertainties–Comparative study

Control of an industrial robot includes nonlinearities, uncertainties and external perturbations that should be considered in the design of control laws. In this paper, some new hybrid adaptive neuro-fuzzy control algorithms (ANFIS) have been proposed for manipulator control with uncertainties. These hybrid controllers consist of adaptive neuro-fuzzy controllers and conventional controllers. The outputs of these controllers are applied to produce the final actuation signal based on current position and velocity errors. Numerical simulation using the dynamic model of six DOF puma robot arm with uncertainties shows the effectiveness of the approach in trajectory tracking problems. Performance indices of RMS error, maximum error are used for comparison. It is observed that the hybrid adaptive neuro-fuzzy controllers perform better than only conventional/adaptive controllers and in particular hybrid controller structure consisting of adaptive neuro-fuzzy controller and critically damped inverse dynamics controller.     

基于反演设计的机械臂非奇异终端神经滑模控制

针对具有建模误差和不确定干扰的多关节机械臂的轨迹跟踪问题,设计反演非奇异终端神经滑模控制。该方案是采用能有限时间收敛的非奇异终端滑模面,根据滑模控制原理和反演方法设计反演滑模控制器;对于反演滑模控制系统中由于建模误差和不确定干扰造成的不确定因素的上界,设计径向基(Radial basis function, RBF)神经网络自适应律,在线估计不确定因素的上界;利用李亚普诺夫定理证明了系统的稳定性。仿真结果表明,该方法具有良好的轨迹跟踪性能,提高对于建模误差和不确定干扰等因素的鲁棒性,削弱了抖动。     

Adaptive neural network based position tracking control for Dual-master/Single-slave teleoperation system under communication constant time delays

The novel trajectory tracking control strategies for trilateral teleoperation systems with Dual-master/Single-slave robot manipulators under communication constant time delays are proposed in this article. By incorporating this design technique into the neural network (NN) based adaptive control framework, two controllers are designed for the trilateral teleoperation systems in free motion. First, with acceleration measurements, an adaptive controller under the synchronization variables containing the position and velocity error is constructed to guarantee the position and velocity tracking errors between the trilateral teleoperation systems asymptotically converge to zero. Second, without acceleration measurements, an adaptive controller under the new synchronization variables is presented such that the trilateral teleoperation systems can obtain the same trajectory tracking performance as the first controller. Third, in term of establishing suitable Lyapunov–Krasovskii functionals, the asymptotic tracking performances of the trilateral teleoperation systems can be derived independent of the communication constant time delays. Moreover, these two controllers are obtained without the knowledge of upper bounds of the NN approximation errors, respectively. Finally, simulation results are presented to demonstrate the validity of the proposed methods.     

Adaptive nonsingular fast terminal sliding-mode control for the tracking problem of uncertain dynamical systems

In this paper, robust and adaptive nonsingular fast terminal sliding-mode (NFTSM) control schemes for the trajectory tracking problem are proposed with known or unknown upper bound of the system uncertainty and external disturbances. The developed controllers take the advantage of the NFTSM theory to ensure fast convergence rate, singularity avoidance, and robustness against uncertainties and external disturbances. First, a robust NFTSM controller is proposed which guarantees that sliding surface and equilibrium point can be reached in a short finite-time from any initial state. Then, in order to cope with the unknown upper bound of the system uncertainty which may be occurring in practical applications, a new adaptive NFTSM algorithm is developed. One feature of the proposed control law is their adaptation techniques where the prior knowledge of parameters uncertainty and disturbances is not needed. However, the adaptive tuning law can estimate the upper bound of these uncertainties using only position and velocity measurements. Moreover, the proposed controller eliminates the chattering effect without losing the robustness property and the precision. Stability analysis is performed using the Lyapunov stability theory, and simulation studies are conducted to verify the effectiveness of the developed control schemes.     

Adaptive neural controller for visual servoing of robot manipulators with camera-in-hand configuration

In this paper, an adaptive neural controller is proposed for visual servoing of robot manipulators with camera-in-hand configuration. The controller is designed as a combination of a PI kinematic controller and feedforward neural network controller that computes the required torque signals to achieve the tracking. The visual information is provided using the camera mounted on the end-effector and the defined error between the actual image and desired image positions is fed to the PI controller that computes the joint velocity inputs needed to drive errors in the image plane to zero. Then the feedforward neural network controller is designed such that the robot??s joint velocities converges to the given velocity inputs. The stability of combined PI kinematic and feedforward neural network computed torque is proved by Lyapunov theory. It is shown that the neural network can cope with the unknown nonlinearities through the adaptive learning process and requires no preliminary off learning. Simulation results are carried out for a three degrees of freedom microbot robot manipulator to evaluate the controller performance.     

Supervisory hybrid control of piezoelectric actuators utilized in tracking piecewise continuous trajectories

This article presents a supervisory hybrid control design for piezoelectric actuators utilized in tracking trajectories with intermittent jump discontinuities. We use a previously developed robust adaptive controller and a standard PID controller to construct this hybrid control strategy. We show that when the sub-controllers are used for step tracking, while primarily tuned for continuous trajectory tracking, large undesirable oscillations occur. Conversely, when the controllers are retuned for step tracking, their performance degrades in tracking high-frequency continuous trajectories. Thus, a supervisory hybrid controller is developed to track desired trajectories with occasional discontinuities, using both the robust adaptive and the PID controllers. The robust adaptive controller performs as the primary controller for tracking the continuous segments of the desired trajectory, while the PID controller is activated when the steps occur. Results indicate that the proposed supervisory hybrid controller outperforms both sub-controllers in tracking high-frequency trajectories with intermittent discontinuities.     

45°车把转角下前轮驱动自行车机器人的定车运动

针对无配重调节器的自行车机器人在低速下不易平衡的问题,以一种前轮驱动自行车机器人为对象,给出其力学模型及在45°车把转角下定车运动的实现方法。通过车轮转弯半径分析推导出后轮角速度、车架航向角速度与前轮驱动速度、车把转角的关系,采用拉格朗日方程建立系统的力学模型;根据部分反馈线性化原理,将包含车架横滚角的欠驱动子系统线性化,设计出自行车机器人45°车把转角下定车运动的平衡控制器。仿真控制结果表明,合理选择控制参数,控制器可以快速地实现自行车机器人在45°车把转角下的定车运动;样机试验结果进一步证明,控制器可以使自行车机器人在不超过驱动电动机的力矩容限下实现45°车把转角下的定车运动。定车运动的实现从理论和试验两个方面证明,自行车机器人在低速下可以不需要配重调节器,仅依靠车把转动和前轮驱动保持稳定平衡。     

柔性和摩擦力不确定条件下RBF神经网络自适应轨迹跟踪方法

提出一种基于径向基神经网络(Radial basis function, RBF)的力/位置混合自适应控制方法并用于机器人轨迹跟踪控制,解决机器人柔性末端执行器轨迹跟踪过程中柔性和摩擦力模型难以精确描述的问题。RBF神经网络是一种高效的前馈式神经网络,具有其他前向网络所不具有的非线性逼近性能和全局最优特性,并且网络结构简单,训练速度快。设计一种基于RBF神经网络非线性逼近能力来估计模型中的不确定参数的自适应控制器,给出控制器中神经网络权值更新规则,并证明所设计控制器输出力和位置误差的最终一致有界性。将该控制器应用于风管清扫机器人仿真试验,结果表明该自适应控制器能很好地用于柔性和摩擦力不确定条件下轨迹跟踪控制,与传统自适应控制方法相比具有更精确的跟踪特性和更强的鲁棒性。     

非协调移动机器人的变结构跟踪控制

研究非协调移动机器人系统的跟踪问题。根据反演设计的方法 ,针对双后轮驱动移动机器人的运动学模型 ,设计变结构跟踪控制方案 ,使闭环跟踪系统渐近稳定。对双后轮驱动机器人的仿真证实了控制器的鲁棒跟踪能力     

Adaptive fuzzy sliding-mode controller of uncertain nonlinear systems

This paper deals with the design of adaptive fuzzy sliding-mode controllers for the T-S fuzzy model based on the Lyapunov function. It is shown that the Lyapunov function can be used to establish fuzzy sliding surfaces by solving a set of linear matrix inequalities (LMIs). The design of the fuzzy sliding surfaces and the adaptive fuzzy sliding-mode controllers is proposed. The adaptive mechanism is also used to deal with unknown parameter perturbations and external disturbances. Two examples illustrate the feasibility of the proposed methods.     

Adaptive fuzzy sliding-mode control for the Ti6Al4V laser alloying process

It is well known that surface alloying quality may vary significantly with respect to process parameter variation. Thus a feedback control system is required to monitor the operating parameters for yielding a good quality control. Since this multi-input and multi-output (MIMO) system has nonlinear coupling and time-varying dynamic characteristics, it is very difficult to establish an accurate process model for designing a model-based controller. Hence an adaptive fuzzy sliding-mode controller (AFSMC) which combines an adaptive rule with fuzzy and sliding-mode control is employed in this study. It has an on-line learning ability for responding to a system’s nonlinear and time-varying behaviours. Two adaptive fuzzy sliding-mode controllers are designed for tuning the laser power and the traverse velocity simultaneously to tackle the absorptivitiy and geometrical variations of the work pieces. The simulation results show that good surface lapping performance is achieved by using this intelligent control strategy.     

多关节机器人反馈线性化双模糊滑模控制

为了提高多关节机器人轨迹跟踪控制性能,提出了一种反馈线性化双模糊滑模控制方法。该方法在对机器人非线性动力学模型反馈线性化的基础上,设计了一种双模糊滑模控制器。通过设计一个模糊控制器,根据跟踪误差和误差变化率自适应地调整滑模面的斜率,从而加快响应速度。通过设计另一个模糊控制器,根据滑模面自适应地调整滑模控制的切换控制部分,从而减弱抖振。利用李亚普诺夫定理证明了控制系统的稳定性。针对空间三关节机器人进行了仿真实验,结果表明了所提方法的有效性。     

基于Backstepping的轮式移动机器人镇定研究与实验验证

根据轮式移动机器人的运动学模型,基于Back-stepping方法,构造了轮式移动机器人镇定系统的李雅普诺夫函数,并通过使李雅普诺夫函数负定,设计了移动机器人镇定控制律.轮式移动机器人的仿真实验以及实际的控制实验表明,该方法能使轮式移动机器人的伍姿从任意θ(0)≠0的初始状态渐近收敛到原点.     

Output feedback tracking control of uncertain robot manipulators via higher-order sliding-mode observer and fuzzy compensator

This paper develops a new method to control uncertain robot manipulators by using only position measurements. The controller is designed based on a combination of a computed torque controller (CTC) with a higher-order sliding-mode observer and a fuzzy compensator. First, three higher-order sliding-mode (SM) observers (second-order SM, third-order SM and third-order SM linear (TOSML) observers) are designed and compared to verify whether the TOSML observer is the best for observing velocity and identifying uncertainty. A combined CTC-TOSML controller was then designed. Although this controller scheme can overcome the drawbacks of conventional CTCs, its tracking performance can still be improved. To enhance capability of the tracking performance, a CTC-TOSML controller plus fuzzy compensator called a CTC-TOSML-Fuzzy controller is proposed. The proposed controller increases the potential of the CTC for real robot applications. Finally, computer simulation results on a PUMA560 robot are discussed to verify the effectiveness of the proposed strategy.     

Command filter based adaptive control of asymmetric output-constrained switched stochastic nonlinear systems

In this paper, adaptive tracking control problem is investigated for a class of switched stochastic nonlinear systems with an asymmetric output constraint. By introducing a nonlinear mapping (NM), the asymmetric output-constrained switched stochastic system is first transformed into a new system without any constraint, which achieves the equivalent control objective. The command filter technique is employed to handle the “explosion of complexity” in traditional backstepping design, and neural networks (NNs) are directly utilized to cope with the completely unknown nonlinear functions and stochastic disturbances existing in systems. At last, on the basis of stochastic Lyapunov function method, an adaptive neural controller is developed for the considered system. It is shown that the designed adaptive controller can guarantee that all the signals remain semi-globally uniformly ultimately bounded (SGUUB), while the output constraint is satisfied and the desired signal can be tracked with a small domain of the origin. Simulation results are offered to illustrate the feasibility of the newly designed control scheme.