PD with sliding mode control for trajectory tracking of robotic system

Good tracking performance is very important for trajectory tracking control of robotic systems. In this paper, a new model-free control law, called PD with sliding mode control law or PD–SMC in short, is proposed for trajectory tracking control of multi-degree-of-freedom linear translational robotic systems. The new control law takes the advantages of the simplicity and easy design of PD control and the robustness of SMC to model uncertainty and parameter fluctuation, and avoid the requirements for known knowledge of the system dynamics associated with SMC. The proposed control has the features of linear control provided by PD control and nonlinear control contributed by SMC. In the proposed PD–SMC, PD control is used to stabilize the controlled system, while SMC is used to compensate the disturbance and uncertainty and reduce tracking errors dramatically. The stability analysis is conducted for the proposed PD–SMC law, and some guidelines for the selection of control parameters for PD–SMC are provided. Simulation results prove the effectiveness and robustness of the proposed PD–SMC. It is also shown that PD–SMC can achieve very good tracking performances compared to PD control under the uncertainties and varying load conditions.     

Robust adaptive tracking control of robotic systems with uncertainties

To deal with the uncertainty factors of robotic systems, a robust adaptive tracking controller is proposed. The knowledge of the uncertainty factors is assumed to be unidentified; the proposed controller can guarantee robustness to parametric and dynamics uncertainties and can also reject any bounded, immeasurable disturbances entering the system. The stability of the proposed controller is proven by the Lyapunov method. The proposed controller can easily be implemented and the stability of the closed system can be ensured; the tracking error and adaptation parameter error are uniformly ultimately bounded （UUB）. Finally, some simulation examples are utilized to illustrate the control performance.     

Adaptive learning tracking control of robotic manipulators with uncertainties

An adaptive learning tracking control scheme is developed for robotic manipulators by a synthesis of adaptive control and learning control approaches. The proposed controller possesses both adaptive and learning properties and thereby is able to handle robotic systems with both time-varying periodic uncertainties and time invariant parameters. Theoretical proofs are established to show that proposed controllers ensure asymptotical tracking performance. The effectiveness of the proposed approaches is validated through extensive numerical simulation results.     

Experimental implementation of distributed flocking algorithm for multiple robotic fish

This paper presents the experimental implementation of a flocking algorithm for multiple robotic fish governed by extended second-order unicycles. Combing consensus protocols with attraction/repulsion functions, a flocking algorithm is proposed to make the agents asymptotically converge to swim with consistent velocities and approach the equilibrium distances to their neighbors. The LaSalle–Krasovskii invariance principle is applied to verify the stability of the system. Besides numerical simulations, platform simulations involving robotic fish kinematic constraint and control mechanism are shown. An experiment with three robotic fish is implemented to illustrate the effectiveness of the proposed flocking algorithm in the presence of external disturbance and boundary collision.     

Analysis of trajectory deviation during high speed robotic trimming of carbon-fiber reinforced polymers

Although carbon-fiber reinforced polymers (CFRPs) are used extensively in the aerospace industry, trimming of CFRPs in high speed robotic end milling has however not yet received its due attention within the research community. For such an application, the robot should be very stiff for the machining operation to be generated. If the robot is not sufficiently stiff, deviations in shape and position of the workpieces will occur.     

Integrated approach to robotic machining with macro/micro-actuation

A novel integrated approach to high-accuracy machining with industrial robots is presented in this paper. By combining a conventional industrial robot with an external compensation mechanism, a significantly higher bandwidth of the control of the relative position between the tool and the workpiece can be achieved. A model-based feedback controller for the compensation mechanism, as well as a mid-ranging control architecture for the combined system with the robot and the compensation mechanism are developed. The system performance is evaluated in extensive machining experiments, and the workpiece accuracies achieved are quantified and compared to the corresponding results obtained with state-of-the-art approaches to robotic machining. It is shown that the proposed approach to machining offers significantly higher accuracy, up to eight times improvement for milling in steel, where the required process forces, and thus the exhibited position deviations of the robot, are significant.     

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.     

受控旋转弹飞行动力学建模和稳定性分析

选取带有控制系统的旋转弹为研究对象,考虑到控制环节不可避免的时滞及气动非线性效应,从理论上进一步完善了旋转弹动力学模型.从模型的特征方程出发,以时滞、控制增益为分岔参数,对系统的零平衡点稳定性进行了分析,得到平衡点失稳后发生Hopf分岔的临界参数值,并在理论预测的情况下数值模拟了攻角和侧滑角在不同情况下的失稳情况以及Hopf分岔周期解振幅随分岔参数的变化情况.数值结果表明了理论预测的正确性,时滞虽未改变旋转弹锥形运动方式,但是却大幅度的减小了稳定飞行控制增益的取值范围,因此在旋转弹姿态稳定性系统设计过程中时滞的影响不可忽略.     

Adaptive ILC algorithms of nonlinear continuous systems with non-parametric uncertainties for non-repetitive trajectory tracking

In this article, two adaptive iterative learning control (ILC) algorithms are presented for nonlinear continuous systems with non-parametric uncertainties. Unlike general ILC techniques, the proposed adaptive ILC algorithms allow that both the initial error at each iteration and the reference trajectory are iteration-varying in the ILC process, and can achieve non-repetitive trajectory tracking beyond a small initial time interval. Compared to the neural network or fuzzy system-based adaptive ILC schemes and the classical ILC methods, in which the number of iterative variables is generally larger than or equal to the number of control inputs, the first adaptive ILC algorithm proposed in this paper uses just two iterative variables, while the second even uses a single iterative variable provided that some bound information on system dynamics is known. As a result, the memory space in real-time ILC implementations is greatly reduced.     

基于PD控制的机器人轨迹跟踪性能研究与比较

定义同一个Lyapunov函数，分析了基于PD的3种常用机器人轨迹跟踪算法的稳定性和鲁棒性，得到了新的结论，PD加前馈控制按指数收敛到0，PD及修改的PD加前馈控制收敛到一封闭球，增大反馈系数可使球半径任意小，基于PD的轨迹跟踪算法对模型误差及有界不确定性干扰具有鲁棒性，实验研究验证了分析结果，并对3种轨迹跟踪算法的控制性能进行比较。     

An adaptive neural network switching control approach of robotic manipulators for trajectory tracking

In this paper, an adaptive neural network (NN) switching control strategy is proposed for the trajectory tracking problem of robotic manipulators. The proposed system comprises an adaptive switching neural controller and the associated robust compensation control law. Based on the Lyapunov stability theorem and average dwell-time approach, it is shown that the proposed control scheme can guarantee tracking performance of the robotic manipulators system, in the sense that all variables of the closed-loop system are bounded and the effect due to the external disturbance and approximate error of radical basis function (RBF) NNs on the tracking error can be converged to zero in an infinite time. Finally, simulation results on a two-link robotic manipulator show the feasibility and validity of the proposed control scheme.     

Intelligent adaptive model-based control of robotic dynamic systems with a hybrid fuzzy-neural approach

We describe in this paper a new method for adaptive model-based control of robotic dynamic systems using a new hybrid fuzzy-neural approach. Intelligent control of robotic systems is a difficult problem because the dynamics of these systems is highly nonlinear. We describe an intelligent system for controlling robot manipulators to illustrate our fuzzy-neural hybrid approach for adaptive control. We use a new fuzzy inference system for reasoning with multiple differential equations for model selection based on the relevant parameters for the problem. In this case, the fractal dimension of a time series of measured values of the variables is used as a selection parameter. We use neural networks for identification and control of robotic dynamic systems. We also compare our hybrid fuzzy-neural approach with conventional fuzzy control to show the advantages of the proposed method for control.     

Coning motion stability and decoupling methods analysis of spinning missiles with angle-of-attack feedback autopilot

The angle-of-attack (AOA) three-loop feedback autopilot is an improved three-loop autopilot that has been widely employed in missile control systems. For spinning missiles, however, unstable coning motion can be induced by the cross-couple effect. For spinning missiles with an AOA feedback autopilot, this paper analyzes the coning motion stability with the consideration of the augmentation loop and the position of the accelerometer and compares the performance of three decoupling methods. First, a novel double-channel actuator AOA three-loop autopilot is established, and the sufficient and necessary condition of coning motion stability is proposed analytically on account of the complex system equations. The stability condition shows that the stable region of the design parameters for the autopilot shrinks as a result of the spinning condition. Moreover, methods associated with actuator dynamics, control coupling, static stability, and the decoupling method of setting the lead angle to the control system are presented to improve the stability of spinning missiles. Numerical simulations are implemented to demonstrate the accuracy of the proposed methods, whose results illustrate that the stability conditions can guide AOA autopilot design for the flight stabilization of spinning missiles.     

Experimental validation and comparative analysis of optimal time-jerk algorithms for trajectory planning

In this paper, two minimum time-jerk trajectory planning algorithms for robotic manipulators have been considered, evaluated and experimentally validated. These algorithms consider both the execution time and the integral of the squared jerk along the whole trajectory, so as to take into account the need for fast execution and the need for a smooth trajectory, by adjusting the values of two weights. A comparative analysis of these algorithms with two different trajectory planning techniques taken from the literature has been carried out, by means of experimental tests performed on a real robotic manipulator. The results prove the experimental effectiveness of the proposed techniques.     

Neuromorphic vision based control for the precise positioning of robotic drilling systems

The manufacturing industry is currently witnessing a paradigm shift with the unprecedented adoption of industrial robots, and machine vision is a key perception technology that enables these robots to perform precise operations in unstructured environments. However, the sensitivity of conventional vision sensors to lighting conditions and high-speed motion sets a limitation on the reliability and work-rate of production lines. Neuromorphic vision is a recent technology with the potential to address the challenges of conventional vision with its high temporal resolution, low latency, and wide dynamic range. In this paper and for the first time, we propose a novel neuromorphic vision based controller for robotic machining applications to enable faster and more reliable operation, and present a complete robotic system capable of performing drilling tasks with sub-millimeter accuracy. Our proposed system localizes the target workpiece in 3D using two perception stages that we developed specifically for the asynchronous output of neuromorphic cameras. The first stage performs multi-view reconstruction for an initial estimate of the workpiece’s pose, and the second stage refines this estimate for a local region of the workpiece using circular hole detection. The robot then precisely positions the drilling end-effector and drills the target holes on the workpiece using a combined position-based and image-based visual servoing approach. The proposed solution is validated experimentally for drilling nutplate holes on workpieces placed arbitrarily in an unstructured environment with uncontrolled lighting. Experimental results prove the effectiveness of our solution with maximum positional errors of less than 0.2 mm, and demonstrate that the use of neuromorphic vision overcomes the lighting and speed limitations of conventional cameras. The findings of this paper identify neuromorphic vision as a promising technology that can expedite and robustify robotic manufacturing processes in line with the requirements of the fourth industrial revolution.     

Force/position tracking for a robotic manipulator in compliant contact with a surface using neuro-adaptive control

The problem of force/position tracking for a robotic manipulator in compliant contact with a surface under non-parametric uncertainties is considered. In particular, structural uncertainties are assumed to characterize the compliance and surface friction models, as well as the robot dynamic model. A novel neuro-adaptive controller is proposed, that exploits the approximation capabilities of the linear in the weights neural networks, guaranteeing the uniform ultimate boundedness of force and position error with respect to arbitrarily small sets, plus the boundedness of all signals in the closed loop. Simulations highlight the approach.     

Comparison of surface roughness quality obtained by high speed CNC trimming and high speed robotic trimming for CFRP laminate

This paper proposes an experimental approach for evaluating the surface roughness of the CFRP parts produced by high speed CNC trimming and high speed robotic trimming under various cutting conditions. A comparison is made between the surface roughnesses obtained by the two processes. The results obtained show that, the measured profiles obtained from high speed robotic trimming are dominated by a large trajectory deviation, as compared to machine tool trimming results. After the trajectory deviation effect is discounted, the results show that for the +45° ply orientation, the surface quality obtained through high speed robotic trimming is similar to what is obtained with the CNC machine. Furthermore, a significant relationship was observed between the surface quality and the ply orientation, whatever the machining process and the cutting conditions employed. The −45° ply orientation represents the worst case in terms of surface roughness, whatever the machining process. It is 4 times higher compared with that of +45° ply orientations,The results also show that the effect of cutting conditions on surface quality is significant for both machining processes tested.     

Accurate robotic belt grinding of workpieces with complex geometries using relative calibration techniques

Robotic belt grinding operations are performed by mounting a workpiece to the end effector and commanding it to move along a trajectory while maintaining contact with the belt grinding wheel. A constant contact force throughout the grinding process is necessary to provide a smooth finish on the workpiece, but it is difficult to maintain this force due to a multitude of installation, manipulation, and calibration errors. The following describes a novel methodology for robotic belt grinding, which primarily focuses on system calibration and force control to improve grinding performance. The overall theory is described and experimental results of turbine blade grinding for each step of the methodology are shown.     

超声速旋转火箭弹气动特性仿真和分析

采用CFD方法,基于剪切应力输运（Shear Stress Transport,SST）湍流模型,求解大长细比卷弧翼火箭弹在超声速情况下的气动力和气动热问题．对火箭弹流场进行数值计算,与实验数据进行对比．采用薄壁模型模拟结构耦合传热,计算在一定海拔和旋转情况下火箭弹的气动加热,并与不旋转的情况进行对比．计算结果表明该数值方法能较好地计算气动力因数和气动热分布．在特定的低转速和海拔情况下的火箭弹温度分布比不旋转的稍微大一点,在旋转情况下的火箭弹尾部截面压力分布不对称,尾部流线更加紊乱;弹头和尾翼前缘温度较高,应当在火箭弹设计中予以考虑．     

Adaptive synchronised tracking control for multiple robotic manipulators with uncertain kinematics and dynamics

In this study, a new adaptive synchronised tracking control approach is developed for the operation of multiple robotic manipulators in the presence of uncertain kinematics and dynamics. In terms of the system synchronisation and adaptive control, the proposed approach can stabilise position tracking of each robotic manipulator while coordinating its motion with the other robotic manipulators. On the other hand, the developed approach can cope with kinematic and dynamic uncertainties. The corresponding stability analysis is presented to lay a foundation for theoretical understanding of the underlying issues as well as an assurance for safely operating real systems. Illustrative examples are bench tested to validate the effectiveness of the proposed approach. In addition, to face the challenging issues, this study provides an exemplary showcase with effectively to integrate several cross boundary theoretical results to formulate an interdisciplinary solution.