Sliding-Mode Tracking Control of Surface Vessels

A sliding-mode control law is presented and experimentally implemented for trajectory tracking of underactuated autonomous surface vessels. The control law is developed by introducing a first-order sliding surface in terms of surge tracking errors and a second-order surface in terms of lateral motion tracking errors. The resulting sliding-mode control law guarantees position tracking while the rotational motion remains bounded. The experimental vessel is a small boat with two propellers in an indoor pool. The position and orientation of the boat are measured using a camera that detects two infrared diodes attached near the front and back ends of the boat. A computer with a capture card processes the camera image to determine the position, calculates the control forces and their corresponding input voltages, and sends the control signals to wireless receivers on the vessel using a wireless transmitter. Several experiments are performed where the vessel accurately follows straight-line and circular trajectories.      

手指书写:一种虚拟文字识别人机交互新方法

本文提出了一种全新的手指书写虚拟文字识别系统,该系统利用摄像头捕捉人手指的运动轨迹,通过手指跟踪及检测算法,恢复出手指虚拟"书写"文字的二维图像数据(称之为虚拟文字),再进行识别输出.文中对单层函数连接神经网络进行改进并用来进行高准确度的运动手指尖的预测及跟踪,给出了一种基于数学形态学及模板匹配的简单有效的手指检测方法,提出了基于运动手指跟踪的"虚拟文字"恢复方法.实验结果表明:系统方案可行,手指跟踪及检测准确率高,对手指"书写"的数字、英文等虚拟文字识别率能达到95％以上.     

FPGA-Based Adaptive Backstepping Sliding-Mode Control for Linear Induction Motor Drive

A field-programmable gate array (FPGA)-based adaptive backstepping sliding-mode controller is proposed to control the mover position of a linear induction motor (LIM) drive to compensate for the uncertainties including the friction force. First, the dynamic model of an indirect field-oriented LIM drive is derived. Next, a backstepping sliding-mode approach is designed to compensate the uncertainties occurring in the motion control system. Moreover, the uncertainties are lumped and the upper bound of the lumped uncertainty is necessary in the design of the backstepping sliding-mode controller. However, the upper bound of the lumped uncertainty is difficult to obtain in advance of practical applications. Therefore, an adaptive law is derived to adapt the value of the lumped uncertainty in real time, and an adaptive backstepping sliding-mode control law is the result. Then, an FPGA chip is adopted to implement the indirect field-oriented mechanism and the developed control algorithms for possible low-cost and high-performance industrial applications. The effectiveness of the proposed control scheme is verified by some experimental results. With the adaptive backstepping sliding-mode controller, the mover position of the FPGA-based LIM drive possesses the advantages of good transient control performance and robustness to uncertainties in the tracking of periodic reference trajectories.     

一种基于李亚普诺夫方法的非线性自整定PID控制系统的研究

应用李亚普诺夫方法与自适应控制原理针对一类非线性PID控制系统提出了一种自整定方法,运用有监督控制和在线调整自适应控制律对PID控制系统的3个控制参数进行在线调整及更新,保证了非线性PID控制系统的闭环稳定性.最后利用该方法控制倒摆系统的运动轨迹问题,仿真结果表明该方法可以获得令人满意的控制性能.     

Antisway Tracking Control of Overhead Cranes With System Uncertainty and Actuator Nonlinearity Using an Adaptive Fuzzy Sliding-Mode Control

An adaptive fuzzy sliding-mode control (AFSMC) is presented for the robust antisway trajectory tracking of overhead cranes subject to both system uncertainty and actuator nonlinearity. First, a fuzzy sliding-mode control (FSMC) law is designed for the antisway trajectory tracking of the nominal plant. In association with a conventional trajectory tracking control law, this FSMC law guarantees asymptotic stability as well as improved transient response of the load sway dynamics while the trolley tracking error dynamics is rendered uniformly asymptotically stable. Second, a fuzzy uncertainty observer is designed to cope with system uncertainty as well as actuator nonlinearity present in an actual plant, and it is incorporated with the FSMC law for the development of the AFSMC law. In addition to stability analysis, the robust performance of the proposed AFSMC law is verified via numerical simulations and experiments.      

Position domain contour control for multi-DOF robotic system

Contour tracking control is one of the fundamental operations for robotic systems. In this paper, a position domain PD control is developed to control a multi-DOF nonlinear robotic system for improving contour tracking performance. In this new position domain control system, a robotic system is viewed as a master–slave system where the master motion is used as an independent reference through equidistantly sampling, while slave motions are described as functions of the master motion according to contour tracking requirements. A position domain dynamic model of the robotic system based on the master motion is developed through one-to-one mapping of the original dynamic model from time domain to position domain. Stability analysis is conducted for the proposed position domain PD control, the global boundedness of the tracking errors is guaranteed through the Lyapunov method, and the effectiveness is successfully verified through simulation study for linear and nonlinear contour tracking problems. Compared results demonstrate that the position domain PD control is better than its time domain counterpart for contour tracking of multi-DOF robotic systems.     

An adaptive switching learning control method for trajectory tracking of robot manipulators

In this paper, a new adaptive switching learning control approach, called adaptive switching learning PD control (ASL-PD), is proposed for trajectory tracking of robot manipulators in an iterative operation mode. The ASL-PD control method is a combination of the feedback PD control law with a gain switching technique and the feedforward learning control law with the input torque profile. The torque profile is updated by the previous torque profile (which makes sense for learning). Furthermore, in this new control method, the switching control scheme is integrated into the iterative learning procedure; as such, the trajectory tracking converges very fast. The ASL-PD method achieves the asymptotical convergence based on the Lyapunov’s method. The ASL-PD method possesses both adaptive and learning capabilities with a simple control structure. The simulation study validates this new method. In particular, both position and velocity tracking errors monotonically decrease with the increase of the number of iterations. The convergence rate with the ASL-PD method is faster than that of the adaptive iterative learning control method proposed by others in literature.     

Robust feedback linearization using an adaptive PD regulator for a sensorless control of a throttle valve

With classic gasoline injection systems, engine efficiency and emissions are affected by the control of the throttle plate, in particular its angular position. Depending on the current engine load, the angular position must track a trajectory as determined by the accelerator. This paper considers two problems. The first one is the design of a state observer. A velocity estimator is proposed based on measurements of current. If the effect of the noise is minimized, the angular position can be achieved through a cascade structure between a particular velocity estimator and an inversion of the electrical system. This approach allows us to avoid a more complex structure for the observer, and yields an acceptable performance and the elimination of bulky position sensor systems. The elimination of the position sensor system simplifies the production system of the valve. The second problem, the robustness of the tracking, is addressed using a minimum variance control approach. This paper presents feasible real-time self-tuning of an approximated proportional derivative (PD) regulator, which compensates for the tracking error caused by inexact feedback linearization. It is interesting to note that the structure of the approximated PD regulator is similar to the velocity estimator. Robustness in the proposed loop control is achieved. Measured results on a real experimental setup with hardware-in-the-loop are shown.     

Precise position control of shape memory alloy actuator using inverse hysteresis model and model reference adaptive control system

Position control of Shape Memory Alloy (SMA) actuators has been a challenging topic during the last years due to their nonlinearities in the governing physical equations as well as their hysteresis behaviors. Using the inverse of phenomenological hysteresis model in order to compensate the input–output hysteresis behavior of these actuators shows the effectiveness of this approach. In this paper, in order to control the tip deflection of a large deformation flexible beam actuated by an SMA actuator wire, a feedforward–feedback controller is proposed. The feedforward part of the proposed control system, maps the beam deflection into SMA temperature, is based on the inverse of the generalized Prandtl–Ishlinskii model. An adaptive model reference temperature control system is cascaded to the inverse hysteresis model in order to estimate the SMA electrical current for tracking the reference signal. In addition, a closed-loop proportional–integral controller with position feedback is added to the feedforward controller to increase the accuracy as well as eliminate the steady state error in position control process. Experimental results indicate that the proposed controller has great accuracy in tracking some square wave signals. It is also experimentally shown that the suggested controller has precise tracking performance in presence of environmental disturbances.     

An optimal variable structure control with integral compensationfor electrohydraulic position servo control systems

An approach using variable-structure control with integral compensation is presented for an electrohydraulic position servo control system to achieve accurate servo tracking in the presence of load disturbance and plant parameter variation. Simulations show that the proposed approach may give a rather accurate servo-tracking result and is fairly robust to plant parameter variation and load disturbance     

无人机自主低空突防航迹控制系统设计

设计了一种无人机自主低空突防航迹控制系统。提出了一种满足无人机低空突防要求的航迹预规划方法和航迹控制指令生成算法。针对动态逆方法需要被控对象精确数学模型的难点,在分析无人机误差动力学特性的基础上,采用神经网络在线动态补偿模型逆误差,并运用李亚普诺夫稳定性理论推导网络权值更新律,确保指令跟踪误差和网络权值的有界性。数字仿真结果表明,该系统满足无人机自主低空突防时航迹精确跟踪要求。     

车式移动机器人轨迹跟踪控制方法

针对车式移动机器人轨迹跟踪这一典型控制任务,本文提出一种滑模轨迹跟踪控制方法.该方法采用PI型滑模面设计等效控制律,利用变速函数代替符号函数获得切换控制率,并运用Lyapunov理论证明系统的稳定性.仿真结果表明该方法不但能使机器人有效跟踪任意参考轨迹,而且能减小在控制中的抖振现象,即使在外界干扰影响的情况下,也具有良好的控制品质.     

Adaptive decentralized control of robot manipulators driven by current-fed induction motors

In this paper, an adaptive decentralized control scheme with a rotor-flux observer is proposed for the tracking control of robot manipulators actuated by current-fed induction motors. To cope with all parametric uncertainties in the electromechanical systems, an adaptive law is designed so that all the signals of closed-loop systems are bounded, and the tracking errors in position, velocity and rotor fluxes converge to a residual set.     

H ∞ Output Tracking Control for Discrete-Time Switched Systems Based on Switching Method

This paper addresses the H _∞ output tracking control problem for a class of discrete-time switched linear systems. We do not require that the H _∞ output tracking control problem for each individual subsystem to be solvable. Based on the multiple Lyapunov functions approach, a switching law depending on the system state is designed, which, together with the designed controllers, can guarantee that the output tracking error dynamics converges H _∞ asymptotically to zero. In sufficient conditions, we introduce an additional scalar matrix variable to realize the decoupling between the system matrices and Lyapunov matrices. The controller gains can be obtained by solving linear matrix inequalities (LMIs). A numerical example is provided to demonstrate the feasibility of the proposed design method.     

基于复合控制的光电搜索跟踪转台控制系统设计与实现

高精度的控制系统是保证光电搜索跟踪系统实现目标稳定跟踪的关键因素之一.为提高光电搜索跟踪转台的响应速度及控制精度,在介绍系统总体功能的基础上,对控制系统进行建模,并设计了基于位置前馈与专家PID算法的复合控制策略,实际应用表明,该系统能对目标实现高精度的稳定跟踪,鲁棒性好,达到了预期的设计要求.     

Control of articulated robot arm with sensory feedback: laser beamtracking system

A method for the trajectory tracking control of an articulated robot arm using sensory feedback is presented. First, a general control algorithm for such a problem is presented. To implement sensory feedback effectively, the dynamics of a robot arm is described in the task coordinate system. Then the dynamics of the robot arm in the task coordinate system are linearized using nonlinear feedback. Because the linearization cannot be done completely because of variations and identification errors of the physical parameters of a robot arm, a robust controller is designed so that the effect of parameter variations and errors can be lessened. The control law is shown to be simplified by the use of high-gain feedback. The simplification can make the implementation of the control law very easy. The proposed algorithm is applied to the trajectory-tracking control of an articulated robot arm using a laser beam. The experiments show that the proposed algorithm works well for such a sensory feedback system     

Development of new training algorithms for neuro-wavelet systems on the robust control of induction servo motor drive

A robust wavelet neural network control (RWNNC) system is proposed to control the rotor position of an induction servo motor drive in this paper. In the proposed RWNNC system, a wavelet neural network controller is the main tracking controller that is used to mimic a computed torque control law, and a robust controller is designed to recover the residual approximation for ensuring the stable control performance. Moreover, to relax the requirement for a known bound on lumped uncertainty, which comprises a minimum approximation error, optimal network parameters and higher order terms in a Taylor series expansion of the wavelet functions, an RWNNC system with adaptive bound estimation was investigated for the control of an induction servo motor drive. In this control system, a simple adaptive algorithm was utilized to estimate the bound on lumped uncertainty. In addition, numerical simulation and experimental results due to periodic commands show that the dynamic behaviors of the proposed control systems are robust with regard to parameter variations and external load disturbance.     

Computationally Efficient Predictive Robot Control

Conventional linear controllers (PID) are not really suitable for the control of robot manipulators due to the highly nonlinear behavior of the latter. Over the last decades, several control methods have been proposed to circumvent this limitation. This paper presents an approach to the control of manipulators using a computationally-efficient-model-based predictive control scheme. First, a general predictive control law is derived for position tracking and velocity control, taking into account the dynamic model of the robot, the prediction and control horizons, and also the constraints. However, the main contribution of this paper is the derivation of an analytical expression for the optimal control to be applied that does not involve a numerical procedure, as opposed to most predictive control schemes. In the last part of the paper, the effectiveness of the approach for the control of a nonlinear plant is illustrated using a direct-drive pendulum, and then, the approach is validated and compared to a PID controller using an experimental implementation on a 6-DOF cable-driven parallel manipulator.     

Computed force and velocity control for spatial multi-DOF electro-hydraulic parallel manipulator

A novel dynamic trajectory tracking controller for spatial 6-DOF electro-hydraulic parallel manipulator considering system nonlinearity-computed force and velocity controller is proposed, with a view of improving the control performance with high computational efficiency of control algorithm. The dynamic model of electro-hydraulic parallel manipulator, both mechanical and hydraulic system, is described by using Kane and hydromechanics method. The requisite system states are estimated via forward kinematics based upon global Newton–Raphson with monotonic descent algorithms under the measured actuator position. The desired leg position and velocity required for the proposed controller are calculated by an analytical method corresponding to the desired generalized pose, and the desired driven force is computed with an effectively simplified inverse dynamics. Under feed-forward of the desired driven force and velocity, the computed force and velocity controller is developed with actual leg position as its feedback only, and the desired leg position, velocity and driven force as its input. The control performance of the proposed controller for multi-DOF parallel manipulator is evaluated in theory and experiment, especially for dynamic tracking performance. Experimental results show that the presented controller can greatly improve the dynamic trajectory tracking performance for high real time electro-hydraulic parallel manipulator.     

An adaptive controller for a direct-drive SCARA robot

A direct adaptive controller for trajectory tracking of high-speed robots such as a direct-drive SCARA robot is presented. In this robot, nonlinear effects due to centrifugal, Coriolis, and inertial forces are more important than friction and gravity forces, unlike most industrial robots. The control law of the adaptive scheme consists of a PD regulator plus feedforward compensation of full dynamics. The feedforward terms are adjusted by an adaptation law so that the steady-state position errors are zero. With this adaptive controller, the joint acceleration measurement is not required and no inversion of the estimated mass matrix is involved. The tracking performances of the controller applied to a two-degree-of-freedom SCARA is illustrated by a real-time implementation based on a single-chip digital signal processor (DSP)