On-line frequency domain information for control of a flexible-linkrobot with varying payload

Experimental results are given for the endpoint position control of a single-link, very flexible robot arm carrying an unknown, varying payload. The control objective is to maintain endpoint position accuracy in the presence of flexure effects after rapid movement due to a rigid body slew-angle commanded position. Fast, simple, and efficient frequency domain schemes are used for online controller gain adjustment within an effective scheduling framework. Only endpoint acceleration and motor shaft angle measurements are utilized in relatively simple control laws where the appropriate gains have been scheduled as correlated to modal frequency information corresponding to a varying, unknown payload     

Robustness Comparative Study of Two Control Schemes for 3-DOF Flexible Manipulators

This article describes a comparative study of two control schemes designed for a new three-degree-of-freedom flexible arm. This arm has been built with light links, has most of its mass concentrated on the tip, and its special mechanical configuration uncouples tip motions in spherical coordinates. This special configuration simplifies the dynamic modeling and control of the arm. A compliance matrix is used to model the oscillations of the structure. A consequence of this simple dynamics is that minimum sensing effort is required (only direct motor and tip measurements), and the use of complex observers is avoided because the state of the system can be very easily obtained from these measurements; then its control becomes very simple. Two two-nested control loop schemes are used to control the tip position, by using a joint position and tip acceleration feedback, measured with accelerometers placed at the tip (first control scheme), or tip deflexions feedback, measured with strain gauges placed at the bars of the mechanism near the joints (second control scheme). Both control systems can be considered as equivalents when nominal payload is used for designing them. It can be proved that the use of strain gauges is more robust than the use of accelerometers as tip sensors if the tip mass differs from the nominal one. Simulated results are presented for both control schemes and different payload conditions. Comparative results between the controlled and non-controlled tip responses are also shown.     

Adaptive output feedback tracking control of robot manipulators using position measurements only

In this paper, a new adaptive neuro controller for trajectory tracking is developed for robot manipulators without velocity measurements, taking into account the actuator constraints. The controller is based on structural knowledge of the dynamics of the robot and measurements of joint positions only. The system uncertainty, which may include payload variation, unknown nonlinearities and torque disturbances is estimated by a Chebyshev neural network (CNN). The adaptive controller represents an amalgamation of a filtering technique to generate pseudo filtered tracking error signals (for the elimination of velocity measurements) and the theory of function approximation using CNN. The proposed controller ensures the local asymptotic stability and the convergence of the position error to zero. The proposed controller is robust not only to structured uncertainty such as payload variation but also to unstructured one such as disturbances. Moreover the computational complexity of the proposed controller is reduced as compared to the multilayered neural network controller. The validity of the control scheme is shown by simulation results of a two-link robot manipulator. Simulation results are also provided to compare the proposed controller with a controller where velocity is estimated by finite difference methods using position measurements only.     

无人机吊挂飞行系统的减摆控制设计

主要考虑了四旋翼无人机（Unmanned aerial vehicle,UAV）吊挂飞行系统的位置控制及负载摆动抑制的设计问题.在存在欠驱动特性以及未知系统参数的约束下,本文基于能量法设计了一种非线性控制策略,实现了对无人机位置的精确控制和飞行过程中负载摆动的快速抑制.基于Lyapunov方法的稳定性分析证明了闭环系统的稳定性,位置误差的收敛及摆动的抑制.实验结果表明本文提出的控制策略取得了较好的控制效果.     

Robust control of multi-robot systems: The generalized energy accumulation approach

This paper is concerned with the control problem of a multi-robot system handling a common payload with unknown mass properties. Force constraints at the grasp points are considered. Robust control schemes are proposed to cope with the model uncertainty and achieve asymptotic path tracking. To deal with the force constraints, a strategy for optimally sharing the task is suggested. This strategy basically consists of two steps. The first is to detect the roots that need help and the second to arrange for help. It is shown that the overall system is robust to uncertain payload parameters, and satisfies the force constraints.     

Adaptive boundary control of a flexible marine installation system

In this paper, boundary control of a marine installation system is developed to position the subsea payload to the desired set-point and suppress the cable’s vibration. Using Hamilton’s principle, the flexible cable coupled with vessel and payload dynamics is described as a distributed parameter system with one partial differential equation (PDE) and two ordinary differential equations (ODEs). Adaptive boundary control is proposed at the top and bottom boundaries of the cable, based on Lyapunov’s direct method. Considering the system parametric uncertainty, the boundary control schemes developed achieve uniform boundedness of the steady state error between the boundary payload and the desired position. The control performance of the closed-loop system is guaranteed by suitably choosing the design parameters. Simulations are provided to illustrate the applicability and effectiveness of the proposed control.     

Acceleration feedback for control of a flexible manipulator arm

Many studies to date have dealt with control and modeling strategies for endpoint positioning of flexible manipulator arm systems, and some actual experiments have been documented in verification of developed methods. This article describes laboratory results for a single-link flexible manipulator arm in which three separate control strategies are compared and contrasted. Namely, the control schemes compared are: compensation using classical root locus techniques with endpoint position feedback, a full state feedback, observer-based design, and compensation using endpoint acceleration feedback. The last technique, designs using accelerometer feedback, has received very little attention to date, and results here indicate great promise for use in flexible manipulator control.     

Adaptive neural network hierarchical sliding mode control for six degrees of freedom overhead crane

This paper presents an effective control method for three-dimensional (3D) overhead cranes with six degrees of freedom (DOF). Two payload swings and an axial payload oscillation should be minimized besides driving the bridge, trolley, and hoisting drum to bring the payload to the desired position in space. First, a novel 3D-6DOF crane model is developed, where the sixth degree of freedom is axial cargo oscillation that has never been considered in previous studies. A controller is then designed using the hierarchical sliding mode control method. Moreover, a radial basis function neural network (RBFNN) is used to approximate the system's unknown dynamic model accurately. According to the Lyapunov principle, a control law and an updated law for the neural network's weight matrices are designed to ensure the stability of the closed-loop system. Simulation results on Matlab software show the proposed approach's effectiveness, such as smaller swing, minor axial oscillation, and precise position as desired.     

Two adaptive control structures of robot manipulators

This paper proposes two simple adaptive control schemes of robot manipulators. The first one is the state feedback control which consists of feedforward from the desired position trajectory, PD feedback from the actual trajectory, and an auxiliary input. The second one is the feedforward/feedback control which consists of a feedforward term from the desired position, velocity, and acceleration trajectory based on the inverse of robot dynamics. The feedforward, feedback, and auxiliary gains are adapted using simple equations derived from the decentralized adaptive control theory based on Lyapunov's direct method, and using only the local information of the corresponding joint. The proposed control schemes are computationally fast and do not require a priori knowledge of the detail parameters of the manipulator or the payload. Simulation results are presented in support of the proposed schemes. The results demonstrate that both controllers perform well with bounded adaptive gains.     

A switched optimal control approach to reduce transferring time,energy consumption,and residual vibration of payload’s skew rotation in crane systems

In this paper, nonlinear optimal control schemes are proposed to reduce transferring time, energy consumption, and residual vibration for the payload’s skew rotation process of crane systems. The main contribution of this paper is to reduce the energy consumption without trading-off the sub-optimal transferring time. The novel idea is to use an electro-mechanical clutch to intelligently disengage the connection between the motor and the payload during the motion such that the payload can continue rotating only by its own momentum. As a result, a switched optimal control problem must be realized. Two solutions, namely particular and general schemes are proposed in the paper, where physical constraints of the actuator including bounded velocity and bounded acceleration are explicitly taken into account. Both simulation and experimental results are provided to prove the effectiveness of the proposed optimal control systems. The established schemes can be directly applied to transfer the payload to a desirable skew orientation without any residual oscillation, or can be utilized as a sub-optimal-time reference trajectory planner of the skewing control module in either overhead or rotary crane systems. Furthermore, the hybrid rotation process presented in this paper, which is driven by the engaging/disengaging event of the clutch, can be served as a theoretical benchmark for any newly established switched optimal control method.     

Adaptive/robust time‐varying stabilization of second‐order non‐holonomic chained form with input uncertainties

Adaptive and robust time‐varying control schemes are constructed to stabilize second‐order non‐holonomic chained form in the presence of input uncertainties. The proposed control schemes guarantee that all the state variables converge to zero asymptotically in spite of input uncertainties, and are applied to the stabilization of a planar rigid body driven by active force and torque with unknown inertia and geometric parameters. The basic idea of the proposed stabilization schemes is to first convert the non‐holonomic system into a linear time‐varying form by time‐varying co‐ordinate transformation, and then design control laws to stabilize the converted linear time‐varying system. Copyright © 2002 John Wiley & Sons, Ltd.     

A Dynamic Analysis of a Spatial Manipulator to Determine Payload Weight

This paper presents a methodology whereby the payload weight of a serial manipulator can be determined from a minimum set of sensor data, i.e., joint angle and joint torque measurements. The particular manipulator geometry that is analyzed is a four degree‐of‐freedom serial chain that is commonly used in excavator systems. It was quite remarkable that a relatively simple solution was obtained for the payload weight considering that there are a total of nine unknown moments and cross moments of inertia of the payload together with the unknown location of the center of mass. Example calculations are presented. © 2003 Wiley Periodicals, Inc.     

Adaptive speed/position control of induction motor based on SPR approach

A sensorless speed/position tracking control scheme for induction motors is proposed subject to unknown load torque via adaptive strictly positive real (SPR) approach design. A special nonlinear coordinate transform is first provided to reform the dynamical model of the induction motor. The information on rotor fluxes can thus be derived from the dynamical model to decide on the proportion of input voltage in the d-q frame under the constraint of the maximum power transfer property of induction motors. Based on the SPR approach, the speed and position control objectives can be achieved. The proposed control scheme is to provide the speed/position control of induction motors while lacking the knowledge of some mechanical system parameters, such as the motor inertia, motor damping coefficient, and the unknown payload. The adaptive control technique is thus involved in the field oriented control scheme to deal with the unknown parameters. The thorough proof is derived to guarantee the stability of the speed and position of control systems of induction motors. Besides, numerical simulation and experimental results are also provided to validate the effectiveness of the proposed control scheme.     

Output feedback tracking control of robot manipulators with model uncertainty via adaptive fuzzy logic

Many robot controllers require not only joint position measurements but also joint velocity measurements; however, most robotic systems are only equipped with joint position measurement devices. In this paper, a new output feedback tracking control approach is developed for the robot manipulators with model uncertainty. The approach suggested herein does not require velocity measurements and employs the adaptive fuzzy logic. The adaptive fuzzy logic allows us to approximate uncertain and nonlinear robot dynamics. Only one fuzzy system is used to implement the observer-controller structure of the output feedback robot system. It is shown in a rigorous manner that all the signals in a closed loop composed of a robot, an observer, and a controller are uniformly ultimately bounded. Finally, computer simulation results on three-link robot manipulators are presented to show the results which indicate good position tracking performance and robustness against payload uncertainty and external disturbances.     

Razumikhin–Nussbaum‐lemma‐based adaptive neural control for uncertain stochastic pure‐feedback nonlinear systems with time‐varying delays

This paper addresses the problem of adaptive neural control for a class of uncertain stochastic pure‐feedback nonlinear systems with time‐varying delays. Major technical difficulties for this class of systems lie in: (1) the unknown control direction embedded in the unknown control gain function; and (2) the unknown system functions with unknown time‐varying delays. Based on a novel combination of the Razumikhin–Nussbaum lemma, the backstepping technique and the NN parameterization, an adaptive neural control scheme, which contains only one adaptive parameter is presented for this class of systems. All closed‐loop signals are shown to be 4‐Moment semi‐globally uniformly ultimately bounded in a compact set, and the tracking error converges to a small neighborhood of the origin. Finally, two simulation examples are given to demonstrate the effectiveness of the proposed control schemes. Copyright © 2012 John Wiley & Sons, Ltd.     

Nonlinear adaptive control of robotic manipulators - hyperstability approach

A nonlinear model reference adaptive controller based on hyperstability approach, is presented for the control of robot manipulators. Use of hyperstability approach simplifies the stability proof of the adaptive system. The unknown parameters of the system, as well as its variable payload, are estimated on line and are adaptive to their actual values; tending to reduce the system error. In addition, any sudden change in the system parameters or payload is detected by the proposed intelligent controller. Robot path tracking, with unknown parameter values and variable payload, is simulated to show the effectiveness of the proposed adaptive control algorithm. Both system output error and parameter estimation error vanish under the proposed parameter adaptation algorithm.     

Adaptive tracking control of leader–follower systems with unknown dynamics and partial measurements

In this paper, a decentralized adaptive tracking control is developed for a second-order leader–follower system with unknown dynamics and relative position measurements. Linearly parameterized models are used to describe the unknown dynamics of a self-active leader and all followers. A new distributed system is obtained by using the relative position and velocity measurements as the state variables. By only using the relative position measurements, a dynamic output–feedback tracking control together with decentralized adaptive laws is designed for each follower. At the same time, the stability of the tracking error system and the parameter convergence are analyzed with the help of a common Lyapunov function method. Some simulation results are presented to validate the proposed adaptive tracking control.     

An E‐HOIM Based Data‐Driven Adaptive TILC of Nonlinear Discrete‐Time Systems for Non‐Repetitive Terminal Point Tracking

This work proposes a new adaptive terminal iterative learning control approach based on the extended concept of high‐order internal model, or E‐HOIM‐ATILC, for a nonlinear non‐affine discrete‐time system. The objective is to make the system state or output at the endpoint of each operation track a desired target value. The target value varies from one iteration to another. Before proceeding to the data‐driven design of the proposed approach, an iterative dynamical linearization is performed for the unknown nonlinear systems by using the gradient of the nonlinear system with regard to the control input as the iteration‐and‐time‐varying parameter vector of the equivalent linear I/O data model. By virtue of the basic idea of the internal model, the inverse of the parameter vector is approximated by a high‐order internal model. The proposed E‐HOIM‐ATILC does not use measurements of any intermediate points except for the control input and terminal output at the endpoint. Moreover, it is data‐driven and needs merely the terminal I/O measurements. By incorporating additional control knowledge from the known portion of the high order internal model into the learning control law, the control performance of the proposed E‐HOIM‐ATILC is improved. The convergence is shown by rigorous mathematical proof. Simulations through both a batch reactor and a coupled tank system demonstrate the effectiveness of the proposed method.     

Nonlinear Anti-swing Control of Underactuated Tower Crane Based on Improved Energy Function

The control system of tower crane exhibits strong nonlinearity in the process of control execution, which is prone to the problems of inaccurate positioning control of the payload and difficult anti-swing control. Aiming at the problems, this paper proposes a control law based on improved energy coupling analysis for suppressing the payload swing in the tower cranes. A three-dimensional dynamic model of tower crane system with considering friction is established, and an improved energy coupling signal is designed. The coupling relationship of trolley movement and payload swing, jib rotation and payload swing are considered, then a nonlinear anti-swing controller is established in order to reduce the swing. The closed-loop stability of the system with the controller is verified by the Lyapunov method and LaSalle invariance principle, simulations and experimental analyses are performed to verify the controller performance. The control performance of the controller is compared with other classic and typical current control methods, and the proposed controller outperformed other controllers. The anti-swing controller proposed in this paper has accurate positioning, and can achieve precise control when the payload is transported, reaching the set target position in a little time and eliminating residual swing angle. Meanwhile the proposed controller has a good control robustness, which can restore stability in around a very short time when the rope length and payload mass of the system’s inherent property are changed and external interference is added. In addition, when different target position parameters are uncertain, the proposed control law has good robust performance.

     

A semiglobally stable output feedback PI2D regulator forrobot manipulators

Provides an answer to the long-standing question of designing asymptotically stable proportional plus integral regulators with only position feedback for robots with uncertain payload. It has previously been shown in Kelly (1993) and Ailon and Ortega (1993) that globally asymptotically stable set-point regulators for robot manipulators without velocity measurement can be obtained replacing the velocity feedback of a proportional plus derivative controller by a filtered position feedback. In these schemes, the only robot prior information required is the evaluation of the gravity forces at the reference (constant) position. This prior knowledge is used to shape the robot potential energy to have a unique minimum at the desired position. A mismatch in the estimation of the gravity forces leads to a position steady-state error. The authors' main contribution in this paper is to obviate the need of this prior information via the inclusion of two integral terms, around the position error and the filtered position, respectively. Semiglobal stability of the resulting control law is established