Neural Network Control of Multifingered Robot Hands Using Visual Feedback

It is interesting to observe that humans are able to manipulate an object easily and skillfully without the exact knowledge of the object, contact points, or kinematics of our fingers. However, research so far on multifingered robot control has assumed that the kinematics and contact points of the fingers are known exactly. In many applications of multifingered robot hands, the kinematics and contact points of the fingers are uncertain and structures of the Jacobian matrices are unknown. In this paper, we propose an adaptive neural network (NN) Jacobian controller for multifingered robot hand with uncertainties in kinematics, Jacobian matrices, and dynamics. It is shown that using NNs, the uniform ultimate boundedness of the position error can be achieved in the presence of the uncertainties. Simulation results are presented to illustrate the performance of the proposed controller.     

Neural Network Control of Mobile Robot Formations Using RISE Feedback

In this paper, an asymptotically stable (AS) combined kinematic/torque control law is developed for leader–follower-based formation control using backstepping in order to accommodate the complete dynamics of the robots and the formation, and a neural network (NN) is introduced along with robust integral of the sign of the error feedback to approximate the dynamics of the follower as well as its leader using online weight tuning. It is shown using Lyapunov theory that the errors for the entire formation are AS and that the NN weights are bounded as opposed to uniformly ultimately bounded stability which is typical with most NN controllers. Additionally, the stability of the formation in the presence of obstacles is examined using Lyapunov methods, and by treating other robots in the formation as obstacles, collisions within the formation do not occur. The asymptotic stability of the follower robots as well as the entire formation during an obstacle avoidance maneuver is demonstrated using Lyapunov methods, and numerical results are provided to verify the theoretical conjectures.      

Robust Output Feedback Tracking Control for Time-Delay Nonlinear Systems Using Neural Network

In this paper, the problem of robust output tracking control for a class of time-delay nonlinear systems is considered. The systems are in the form of triangular structure with unmodeled dynamics. First, we construct an observer whose gain matrix is scheduled via linear matrix inequality approach. For the case that the information of uncertainties bounds is not completely available, we design an observer-based neural network (NN) controller by employing the backstepping method. The resulting closed-loop system is ensured to be stable in the sense of semiglobal boundedness with the help of changing supplying function idea. The observer and the controller designed are both independent of the time delays. Finally, numerical simulations are conducted to verify the effectiveness of the main theoretic results obtained     

Output Feedback Control of a Quadrotor UAV Using Neural Networks

In this paper, a new nonlinear controller for a quadrotor unmanned aerial vehicle (UAV) is proposed using neural networks (NNs) and output feedback. The assumption on the availability of UAV dynamics is not always practical, especially in an outdoor environment. Therefore, in this work, an NN is introduced to learn the complete dynamics of the UAV online, including uncertain nonlinear terms like aerodynamic friction and blade flapping. Although a quadrotor UAV is underactuated, a novel NN virtual control input scheme is proposed which allows all six degrees of freedom (DOF) of the UAV to be controlled using only four control inputs. Furthermore, an NN observer is introduced to estimate the translational and angular velocities of the UAV, and an output feedback control law is developed in which only the position and the attitude of the UAV are considered measurable. It is shown using Lyapunov theory that the position, orientation, and velocity tracking errors, the virtual control and observer estimation errors, and the NN weight estimation errors for each NN are all semiglobally uniformly ultimately bounded (SGUUB) in the presence of bounded disturbances and NN functional reconstruction errors while simultaneously relaxing the separation principle. The effectiveness of proposed output feedback control scheme is then demonstrated in the presence of unknown nonlinear dynamics and disturbances, and simulation results are included to demonstrate the theoretical conjecture.      

移动机器人网络控制系统的输出反馈控制

以线性时不变系统为被控对象,建立了四轮移动机器人网络控制系统的离散数学模型。诱导时延是影响系统性能的关键因素,通过在节点中设置缓冲区的方法可以将网络控制系统中的随机诱导时延转化为确定性时延,从而将网络控制系统由随机系统转化为确定性系统。通过被控对象移动机器人控制实验系统,设计了一个能处理网络诱导时延的输出反馈控制器,分析了采样周期和网络诱导时延对网络控制系统稳定性的影响。仿真结果表明了该控制器和控制策略的正确性及有效性。     

State Nullification by Memoryless Output Feedback

We examine linear single-input single-output finite-dimensional systems. It is shown that a continuous time controllable and observable system can be nullified utilizing periodic sampling of the output with time-varying linear feedback. Almost any sampling rate can be used. The result relies on a characterization of linear output feedback nullification of discrete time observable and controllable systems. An algorithm for the nullification and an estimate on the time in which the algorithm is concluded are provided.Research supported by grants from the Israel Science Foundation and from the Information Society Technologies Programme of the European Commission.(Incumbent of the Hettie H. Heineman Professorial Chair in Mathematics).     

Neural Control for Driving a Mobile Robot Integrating Stereo Vision Feedback

This paper proposes a neural control integrating stereo vision feedback for driving a mobile robot. The proposed approach consists in synthesizing a suitable inverse optimal control to avoid solving the Hamilton Jacobi Bellman equation associated to nonlinear system optimal control. The mobile robot dynamics is approximated by an identifier using a discrete-time recurrent high order neural network, trained with an extended Kalman filter algorithm. The desired trajectory of the robot is computed during navigation using a stereo camera sensor. Simulation and experimental result are presented to illustrate the effectiveness of the proposed control scheme.     

Output Feedback Adaptive Neural Control Without Seeking SPR Condition

For output‐feedback adaptive control of affine nonlinear systems based on feedback linearization and function approximation, the observation error dynamics usually should be augmented by a low‐pass filter to satisfy a strictly positive real (SPR) condition so that output feedback can be realized. Yet, this manipulation results in filtering basis functions of approximators, which makes the order of the controller dynamics very large. This paper presents a novel output‐feedback adaptive neural control (ANC) scheme to avoid seeking the SPR condition. A saturated output‐feedback control law is introduced based on a state‐feedback indirect ANC structure. An adaptive neural network (NN) observer is applied to estimate immeasurable system state variables. The output estimation error rather than the basis functions is filtered and the filter output is employed to update NNs. Under given initial conditions and sufficient control parameter constraints, it is proved that the closed‐loop system is uniformly ultimately bounded stable in the sense that both the state estimation errors and the tracking errors converge to small neighborhoods of zero. An illustrative example is provided to demonstrate the effectiveness of this approach.     

Near Optimal Output Feedback Control of Nonlinear Discrete-time Systems Based on Reinforcement Neural Network Learning

In this paper, the output feedback based finitehorizon near optimal regulation of nonlinear affine discretetime systems with unknown system dynamics is considered by using neural networks (NNs) to approximate Hamilton-Jacobi-Bellman (HJB) equation solution. First, a NN-based Luenberger observer is proposed to reconstruct both the system states and the control coefficient matrix. Next, reinforcement learning methodology with actor-critic structure is utilized to approximate the time-varying solution, referred to as the value function, of the HJB equation by using a NN. To properly satisfy the terminal constraint, a new error term is defined and incorporated in the NN update law so that the terminal constraint error is also minimized over time. The NN with constant weights and timedependent activation function is employed to approximate the time-varying value function which is subsequently utilized to generate the finite-horizon near optimal control policy due to NN reconstruction errors. The proposed scheme functions in a forward-in-time manner without offline training phase. Lyapunov analysis is used to investigate the stability of the overall closedloop system. Simulation results are given to show the effectiveness and feasibility of the proposed method.      

Adaptive Impedance Control for Upper-Limb Rehabilitation Robot Using Evolutionary Dynamic Recurrent Fuzzy Neural Network

Control system implementation is one of the major difficulties in rehabilitation robot design. A newly developed adaptive impedance controller based on evolutionary dynamic fuzzy neural network (EDRFNN) is presented, where the desired impedance between robot and impaired limb can be regulated in real time according to the impaired limb??s physical recovery condition. Firstly, the impaired limb??s damping and stiffness parameters for evaluating its physical recovery condition are online estimated by using a slide average least squares (SALS)identification algorithm. Then, hybrid learning algorithms for EDRFNN impedance controller are proposed, which comprise genetic algorithm (GA), hybrid evolutionary programming (HEP) and dynamic back-propagation (BP) learning algorithm. GA and HEP are used to off-line optimize DRFNN parameters so as to get suboptimal impedance control parameters. Dynamic BP learning algorithm is further online fine-tuned based on the error gradient descent method. Moreover, the convergence of a closed loop system is proven using the discrete-type Lyapunov function to guarantee the global convergence of tracking error. Finally, simulation results show that the proposed controller provides good dynamic control performance and robustness with regard to the change of the impaired limb??s physical condition.     

柔性关节机操手的神经网络控制

本文在关节柔性较弱的情况下,对柔性关节机器人操作手的轨迹跟踪问题,提出了一种基于奇异摄动理论的机器人神经网络控制设计方法,在一般框架下证明了系统跟踪误差最终一致有界,并且可以通过选取增益矩阵使该误差界任意地小. 该方法克服了对模型参数线性化条件的要求,无需求解回归矩阵,因而具有很强的鲁棒性和模型推广能力. 数值试验表明,所提出的控制方法是可行且有效的.     

Contour Tracking of a Redundant Robot Using Integral Variable Structure Control with Output Feedback

This work examines the contour tracking problem of redundant robot on a path with singularity. Using an optimal quadratic programming method is to solve the singularity problem and the computing load of motion planning is reduced by a novel hybrid motion planning method. To achieve contour tracking with output feedback, an integral sliding mode control with a high-gain observer is employed to eliminate the chattering due to discontinuous switching control of the sliding-mode control and maintain robustness of the ideal sliding mode.     

Robust Recurrent Neural Network Control of Biped Robot

In this paper, a recurrent neural network (RNN) control scheme is proposed for a biped robot trajectory tracking system. An adaptive online training algorithm is optimized to improve the transient response of the network via so-called conic sector theorem. Furthermore, L ₂-stability of weight estimation error of RNN is guaranteed such that the robustness of the controller is ensured in the presence of uncertainties. In consideration of practical applications, the algorithm is developed in the discrete-time domain. Simulations for a seven-link robot model are presented to justify the advantage of the proposed approach. We give comparisons between the standard PD control and the proposed RNN compensation method.     

基于混合GA-BP算法优化的神经网络在机器人地面控制中的应用

在机器人地面控制中,针对BP算法易陷入局部极小、收敛速度慢的缺点,根据遗传算法具有全局寻优的特点,将二者结合起来形成一种训练神经网络的混合GA-BP算法;通过算法比较和实例结果分析,表明该算法可以有效、可靠地运用于机器人地面控制,并可方便地应用于其它方面.     

A Stable Output Feedback Position Control With Integral Action For Robot Manipulators

In this paper, the problem of robust regulation of robot manipulators using only position measurements is addressed. The main idea of the control design methodology is to use an observer to estimate simultaneously the velocity and the modeling error signal induced by model/system mismatches. The controller is obtained by replacing the velocity and the modeling error in an inverse dynamics feedback by their estimates, which leads to a certainty equivalence controller. The resulting controller has a PID‐type structure which, under least prior knowledge, reduces to the PI₂D regulator studied in [20]. Moreover, the controller is endowed with a natural antireset windup (ARW) scheme to cope with control torque saturations. Regarding the closed‐loop behavior, it is proven that the region of attraction can be arbitrarily enlarged with high observer gains only, thus we prove semiglobal asymptotic stability. Our result supersedes previous works in the direction of performance estimates; specifically, it is also proven that the performance induced by a saturated inverse dynamics controller can be recovered by our PID‐type controller. In this sense, our work reveals some connections between PID‐type and inverse dynamics controllers.     

Adaptive Neural Tracking Control for Unknown Output Feedback Nonlinear Time-delay Systems

An adaptive output feedback neural network tracking controller is designed for a class of unknown output feedback nonlinear time-delay systems by using backstepping technique.Neural networks are used to approximate unknown time-delay functions.Delay-dependent filters are intro- duced for state estimation.The domination method is used to deal with the smooth time-delay basis functions.The adaptive bounding technique is employed to estimate the upper bound of the neural network reconstruction error.Based on Lyapunov-Krasoviskii functional,the semi-global uniform ultimate boundedness(SGUUB)of all the signals in the closed-loop system is proved.The arbitrary output tracking accuracy is achieved by tuning the design parameters and the neural node number. The feasibility is investigated by an illustrative simulation example.     

未知输出反馈非线性时滞系统自适应神经网络跟踪控制

An adaptive output feedback neural network tracking controller is designed for a class of unknown output feedback nonlinear time-delay systems by using backstepping technique. Neural networks are used to approximate unknown time-delay functions. Delay-dependent filters are introduced for state estimation. The domination method is used to deal with the smooth time-delay basis functions. The adaptive bounding technique is employed to estimate the upper bound of the neural network reconstruction error. Based on Lyapunov-Krasoviskii functional, the semi-global uniform ultimate boundedness (SGUUB) of all the signals in the closed-loop system is proved. The arbitrary output tracking accuracy is achieved by tuning the design parameters and the neural node number. The feasibility is investigated by an illustrative simulation example.     

一种基于模糊神经网络的机器人控制

采用高斯函数作为模糊隶属函数,将模糊控制与神经网络相结合。利用神经网络实现模糊推理,运用了一种模糊高斯基函数神经网络,并用于两关节机器人的轨迹跟踪控制。仿真结果表明,该网络对机器人轨迹跟踪控制具有很好的效果,是一种行之有效的控制方法。     

时滞系统的状态反馈和基于观测器的输出反馈设计

考虑了同时具有状态和输入时滞线性定常系统的H∞镇定问题.基于动态耗散理论和微分对策原理,通过采用带积分项的储存函数,对系统的状态反馈控制器和基于观测器的输出反馈设计问题进行了处理.它们的可解充分条件可以化为与时滞相关的矩阵不等式和Riccati方程的形式.得到的与时滞相关的状态反馈控制律和基于观测器的输出反馈控制律都能使闭环系统内稳且具有H∞干扰衰减.     

状态反馈块解耦的分块方法

给出了求取最大限度能解耦分块的方法。用单模态变换抽取各分块的反馈不变结构，利用其行相关性态逐渐合并不能解耦的各块，可得到最大限度的能解耦分块。