Neural network impedance force control of robot manipulator

The performance of an impedance controller for robot force tracking is affected by the uncertainties in both the robot dynamic model and environment stiffness. The purpose of this paper is to improve the controller robustness by applying the neural network (NN) technique to compensate for the uncertainties in the robot model. NN control techniques are applied to two impedance control methods: torque-based and position-based impedance control, which are distinguished by the way of the impedance functions being implemented. A novel error signal is proposed for the NN training. In addition, a trajectory modification algorithm is developed to determine the reference trajectory when the environment stiffness is unknown. The robustness analysis of this algorithm to force sensor noise and inaccurate environment position measurement is also presented. The performances of the two NN impedance control schemes are compared by computer simulations. Simulation results based on a three-degrees-of-freedom robot show that highly robust position/force tracking can be achieved in the presence of large uncertainties and force sensor noise     

Multi-hierarchy interaction control of a redundant robot using impedance learning

The control of robots with a compliant joint motion is important for reducing collision forces and improving safety during human robot interactions. In this paper, a multi-hierarchy control framework is proposed for the redundant robot to enable the robot end-effector to physically interact with the unknown environment, while providing compliance to the joint space motion. To this end, an impedance learning method is designed to iteratively update the stiffness and damping parameters of the end-effector with desired performance. In addition, based on a null space projection technique, an extra low stiffness impedance controller is included to improve compliant joint motion behaviour when interaction forces are acted on the robot body. With an adaptive disturbance observer, the proposed controller can achieve satisfactory performance of the end-effector control even with the external disturbances in the joint space. Experimental studies on a 7 DOF Sawyer robot show that the learning framework can not only update the target impedance model according to a given cost function, but also enhance the task performance when interaction forces are applied on the robot body.     

A Robust Position and Force Control Strategy for 7-DOF Redundant Manipulators

This paper is concerned with robust position and contact force control for 7-DOF redundant robot arms. An outer-inner loop controller, called the augmented hybrid impedance control scheme is developed. A 6-DOF force/torque sensor is used to measure the interaction forces. These are fed back to the outer-loop controller that implements either a force or an impedance controller in each of the 6 DOF of the tool frame. The force controller is provided with a force set point, and desired inertia and damping are introduced in the force control loop to improve transient performance. The inner loop consists of a Cartesian-level potential difference controller, a redundancy resolution scheme at the acceleration level, and a joint-space inverse dynamics controller. Experimental results for two 7-DOF robot arms (redundant, dextrous, isotropically enhanced, seven-turning pair robot (REDIESTRO) and Mitsubishi PA10-7C) are given to illustrate the performance of the force control strategy. A successful application of the proposed scheme to a surface cleaning task is described using the REDIESTRO, while position and force tracking experiments are described for the Mitsubishi PA10-7C robot.     

Finite-time tracking controller design for nonholonomic systems with extended chained form

A design scheme of the finite-time tracking controller is given for the nonholonomic systems with extended chained form. The relay switching technique and the terminal sliding mode control scheme with finite-time convergence are used to the design of the controller. The global stability is guaranteed and the system states accurately track the states of the reference model in finite time. The simulation results for two physical models of a knife-edge and a wheeled mobile robot have demonstrated the effectiveness of the proposed algorithm.     

Hybrid fault adaptive control of a wheeled mobile robot

A fault adaptive control methodology for mobile robots is presented. The robot is modeled as a continuous system with a supervisory controller. The physical processes of the robot are modeled using bond graphs, and this forms the basis of a combined qualitative reasoning and quantitative model-based estimation scheme for online fault detection and isolation during robot operation. A hierarchical-control accommodation framework is developed for the supervisory controller that determines a suitable control strategy to accommodate the isolated fault. It is shown that for small degradations in actuation effort, a robust controller achieves fault accommodation without significant loss of performance. However, for larger faults, the supervisor needs to switch among several controllers to maintain acceptable performance. The switching stability among a set of trajectory tracking controllers is presented. Simulation results verify the proposed fault adaptive control technique for a mobile robot.     

Designing a robust adaptive dynamic controller for nonholonomic mobile robots under modeling uncertainty and disturbances

The main stream of researches on the mobile robot is planning motions of the mobile robot under nonholonomic constraints. Much has been written about the problem of motion planning under nonholonomic constraints using only a kinematic model of a mobile robot. Those methods, however, assume that there is some kind of a dynamic controller that can produce perfectly the same velocity that is necessary for the kinematic controller. Also there is little literature on the robustness of the controller when there are uncertainties or external disturbances in the dynamical model of a mobile robot. In this paper, we proposed a robust adaptive controller that can achieve perfect velocity tracking while considering not only a kinematic model but also a dynamic model of the mobile robot. The proposed controller can overcome uncertainties and external disturbances by robust adaptive technique. The stability of the dynamic system will be shown through the Lyapunov method.     

Gain scheduler middleware: a methodology to enable existing controllers for networked control and teleoperation-part II: teleoperation

This paper is the second of two companion papers. The foundation for the external gain scheduling approach to enable an existing controller via middleware for networked control with a case study on a proportional-integral (PI) controller for dc motor speed control over IP networks was given in Part I. Part II extends the concepts and methods of the middleware called gain scheduler middleware (GSM) in Part I to enable an existing controller for mobile robot path-tracking teleoperation. By identifying network traffic conditions in real-time, the GSM will predict the future tracking performance. If the predicted tracking performance tends to be degraded over a certain tolerance due to network delays, the GSM will modify the path-tracking controller output with respect to the current traffic conditions. The path-tracking controller output is modified so that the robot will move with the fastest possible speed, while the tracking performance is maintained in a certain tolerance. Simulation and experimental results on a mobile robot path-tracking platform show that the GSM approach can significantly maintain the robot path-tracking performance with the existence of IP network delays.     

An adaptive neurocontroller using RBFN for robot manipulators

In recent years, neural networks have fulfilled the promise of providing model-free learning controllers for nonlinear systems; however, it is very difficult to guarantee the stability and robustness of neural network control systems. This paper proposes an adaptive neurocontroller for robot manipulators based on the radial basis function network (RBFN). The RBFN is a branch of neural networks and is mathematically tractable. Therefore, we adopt the RBFN to approximate nonlinear robot dynamics. The RBFN generates control input signals based on the Lyapunov stability that is often used in the conventional control schemes. A saturation function is also chosen as an auxiliary controller to guarantee the stability and robustness of the control system under the external disturbances and modeling uncertainties.     

Design and Real-Time Implementation of a nonlinear regulation controller for the RMP-100 Segway TWIP

The RMP-100 is an underactuated robot known in the literature as two wheeled inverted pendulum (TWIP). This mechanism has two independent wheels that allow to perform two tasks simultaneously: keep the inverted pendulum in its upright position (balancing) and move the robot to a specific location on the workspace terrain. This paper proposes (as an extension of the work of Gutiérrez Frías, 2013) a nonlinear Lyapunov-based controller with the purpose to stabilize the pose (position and orientation) of the robot while balancing the pendulum. Moreover, asymptotic stability is proven using LaSalle’s invariance principle. Furthermore, the development of a new software for implementing real-time controllers on the RMP-100 is briefly explained; and finally, in order to validate the performance of the proposed controller, experimental results are included.     

Double-level ball-riding robot balancing: From system design,modeling, controller synthesis,to performance evaluation

In this research, a new robot, double-level ball-riding robot, is introduced. The robot consists of an upper ball-riding subsystem and a lower ball-riding subsystem. The robot’s dynamics model can be considered separately in two identical planes. Euler–Lagrange equation of motion is applied in order to obtain the dynamics model. Motors are included in the robot’s model. The model is then linearized. The robot’s parameters are identified. The robot’s prototype is manufactured and assembled. Linear Quadratic Regulator with Integral (LQR + I) controller is proposed and applied in order to balance both levels of the robot. The complementary and orientation transformation are used to fuse sensors in order to obtain robot leaning angles. Balancing performance of the developed double-level ball-riding robot is evaluated by simulations and experiments. The results show efficient control performance of LQR + I controller.     

基于ARM9嵌入式系统智能灭火机器人控制器设计

控制器是机器人的＂大脑＂。在详细分析了比赛用智能灭火机器人系统对控制器要求的基础上,选择比较合理的系统架构。内容具体分为软件设计和硬件设计两方面。在硬件方面采用ARM9作为灭火机器人的控制器核心,ARM9的采用保证了机器人在功能强大的同时拥有良好的扩展性,并且成本较低,易于普及。在软件方面给出了整体设计和沿墙走的流程图。实验证明,该系统设计合理,稳定可靠,达到了最初的设计目标,可以出色地完成灭火任务。     

Intelligent optimal control of single-link flexible robot arm

This paper addresses the design and properties of an intelligent optimal control for a nonlinear flexible robot arm that is driven by a permanent-magnet synchronous servo motor. First, the dynamic model of a flexible robot arm system with a tip mass is introduced. When the tip mass of the flexible robot arm is a rigid body, not only bending vibration but also torsional vibration are occurred. In this paper, the vibration states of the nonlinear system are assumed to he unmeasurable, i.e., only the actuator position can be acquired to feed into a suitable control system for stabilizing the vibration states indirectly. Then, an intelligent optimal control system is proposed to control the motor-mechanism coupling system for periodic motion. In the intelligent optimal control system a fuzzy neural network controller is used to learn a nonlinear function in the optimal control law, and a robust controller is designed to compensate the approximation error. Moreover, a simple adaptive algorithm is proposed to adjust the uncertain bound in the robust controller avoiding the chattering phenomena. The control laws of the intelligent optimal control system are derived in the sense of optimal control technique and Lyapunov stability analysis, so that system-tracking stability can be guaranteed in the closed-loop system. In addition, numerical simulation and experimental results are given to verify the effectiveness of the proposed control scheme.     

Motion and Internal Force Control for Omnidirectional Wheeled Mobile Robots

This paper presents an integrated scheme for motion control and internal force control for a redundantly actuated omnidirectional wheeled mobile robot. The interactive forces between a robot body and its wheels can be reduced into two orthogonal parts: motion-induced forces and internal forces. First, it is shown that the internal forces reside in the null space of the coefficient matrix of the interactive forces and do not affect robotic motion. However, these forces caused by motor torques should be minimized as much as possible to increase the energy efficiency and life span of joint components. With different goals, the control for motion and the control for internal forces can be designed separately. Here, both kinematic and dynamic models of the forces are proposed. A proportional differential plus controller regulates the motion and an inverse dynamic controller tracks it. Then, to minimize the internal forces, an integral feedback internal force controller is used. The motion controller guarantees the robotic motion while the internal force controller minimizes the internal force occurring during robot motion. Simulation results verify the effectiveness of the proposed schemes.     

移动机器人全局轨迹跟踪的自适应滑模控制

讨论了两驱动后轮角速度为控制输入的移动机器人轨迹跟踪问题，针对含有未知参数的非完整移动机器人运动学模型．基于反演（backstepping）控制算法的思想设计了变结构控制的切换函数，并由此构造了具有全局渐近稳定的白适应滑模轨迹跟踪控制器。该方法设计过程简单并具有直观的稳定性分析，适用于移动机器人的全局轨迹跟踪控制。仿真结果表明了该方法的有效性和正确性。     

An output-based controller with two operational modes for a robotwith uncertain parameters in set-point regulation tasks

This paper presents a control approach for the set-point regulation task of a rigid robot with uncertain parameters. The controller strategy is based on two operational modes. During the first mode, the controller drives the robot toward a small neighborhood of the equilibrium point, while in the second mode, the robot converges exponentially to the final target. The proposed control scheme is associated with simple linear controllers, it applies position measurements only, and accounts for the system uncertainties and the unknown payload. Friction is included in the model. Simulation and experimental results are demonstrated     

Sensor-based fuzzy reactive navigation of a mobile robot throughlocal target switching

Fuzzy reactive control, incorporating a local target switching scheme, is applied to the automatic navigation of an intelligent mobile robot in an unknown and changing environment. Sensed-ranging signals and relative target position signals are input to the fuzzy controller. The steering angle and the velocity change are inferred to drive the mobile robot. A reactive rule base governing the robot behavior is synthesized from the human heuristics with respect to various situations of environment. A local target switching scheme is proposed to serve as a front-end processor of the fuzzy active controller and to deal with the local trapping and wandering cycle problem in the navigation of a behavior based mobile robot. The algorithm is described, together with some particular considerations about implementation. Efficiency and effectiveness of the proposed approach are verified through simulation and experiments conducted on a Nomad 200 mobile robot     

Control and breakthrough detection of a three-axis robotic bone drilling system

This paper describes a robotic bone drilling system for applications in orthopedic surgery. The goal is to realize a three-axis robotic drilling system which can automatically stop drilling at the moment a drill breaks through bone. The proposed robotic bone drilling system consists of an inner loop fuzzy controller for robot position control, and an outer loop PD controller for feed unit force control. Moreover, breakthrough detection is a function of thrust force threshold information and trends in drill torque and feed rate. The proposed technique has been verified by drilling pig bones, the results for both the bone drilling and bone breakthrough processes are in accord with theoretical model.     

Sensorintegration für Industrie-Robotersteuerungen

Robot applications applying external sensors can only be implemented with limitations using an industrial robot controller, or have to make use of a special controller. The presented activities of EMST aim at the development of an open and flexible robot control system suiting well for sensor applications. The main aspect of research is a sensor integration in an industrial control system being able to perform fast and complex interactions with robot movement.     

Adaptive control of time-varying mechanical systems: analysis andexperiments

A new adaptive controller for time-varying mechanical systems is proposed based on two assumptions: 1) the dynamics of time-varying mechanical systems is derived under the assumption that the generalized constraints on the system do not depend on time but the system parameters, such as masses and payloads are time-varying; and 2) the time-varying parameters are given by a group of known bounded time functions and unknown constants. It is shown that the proposed adaptive controller results in a stable closed-loop system. Further, if the desired trajectory of the system is periodic, a time-scaling technique of mapping one cycle period of the desired trajectory into a unit interval is proposed to provide robustness to the parameter adaptation algorithm. An experimental platform consisting of a two-link robot with a time-varying payload is developed to test the proposed adaptive controller. Comparative experimental results demonstrate the effectiveness of the proposed design     

Balancing control of a unicycle robot with double gyroscopes using adaptive fuzzy controller

This paper presents a unicycle robot which utilizes the precession effect of a double-gyroscope for lateral balancing and designs an adaptive fuzzy controller to realize the balance control according to its dynamic model. The double gyroscope structure of the unicycle robot can eliminate the pitch angle interference caused by the precession effect and improve the robot's lateral anti-interference ability. An adaptive fuzzy controller is designed based on the dynamic equations of the unicycle robot to improve its robustness. The adaptive controller part improves the anti-interference ability of the unicycle robot, and the fuzzy controller part is used as decoupling controller to reduce the interference of coupling. Simulation and experimental results to verify the anti-interference ability and decoupling effect of the designed controller.