High-speed positioning of an industrial robot based on Preview-Learning control

In this study, a numerical procedure for designing kinematic parameters of SCARA-type manipulators is proposed to yield such a design that the resulting manipulator has the fastest cycle time for a given task. To achieve this goal, an optimization problem is formulated to minimize the cycle time by determining geometric parameters such as the link lengths and the locations of manipulators as well as the trajectory. The representative task to get the cycle time is defined as CP (continuous path) motion along the path crisscrossing the standard working area. A gradient projection algorithm is used to obtain the optimal design with the assumption that each actuator should exert a torque and angular velocity within the capacity of specific commercially available direct-drive motors. SCARA-type manipulators of both absolute coordinate and relative coordinate types are designed to reduce the cycle times. The results show that the absolute coordinate manipulator produces a shorter cycle time than the relative coordinate manipulator in optimal designs.     

Position-based impedance control of a biped humanoid robot

This paper describes position-based impedance control for biped humanoid robot locomotion. The impedance parameters of the biped leg are adjusted in real-time according to the gait phase. In order to reduce the impact/contact forces generated between the contacting foot and the ground, the damping coefficient of the impedance of the landing foot is increased largely during the first half double support phase. In the last half double support phase, the walking pattern of the leg changed by the impedance control is returned to the desired walking pattern by using a polynomial. Also, the large stiffness of the landing leg is given to increase the momentum reduced by the viscosity of the landing leg in the first half single support phase. For the stability of the biped humanoid robot, a balance control that compensates for moments generated by the biped locomotion is employed during a whole walking cycle. For the confirmation of the impedance and balance control, we have developed a life-sized humanoid robot, WABIAN-RIII, which has 43 mechanical d.o.f. Through dynamic walking experiments, the validity of the proposed controls is verified.     

Achieving high-precision laparoscopic manipulation through adaptive force control

In this paper, we present a new solution to laparoscopic manipulation based on forcefeedback control. This method allows us to both explicitely control the forces applied to the patient through the trocar and to precisely control the position of the surgical instrument. It does not require any geometrical model of the operative environment nor any fine robot base placement prior to the instrument insertion. Different adaptive control strategies involving different kinds of sensory equipments are proposed. These strategies are experimentally validated on a laboratory apparatus. An experiment is also presented where a laparoscope held by the robot's arm tracks a target through visual servoing.     

The position control of induction motors using a binary disturbance observer

A control approach for the robust position control of an induction motor based on the binary disturbance observer is described. The binary controller with the binary disturbance observer is implemented for the position control of the induction machine subjected to load disturbances and realizes continuous control. The binary disturbance observer is used to eliminate the chattering problem of a sliding mode disturbance observer. In order to eliminate the steady-state error, the binary disturbance observer with an integral augmented switching hyperplane is proposed. The robustness is achieved, and continuous control is realized by employing the proposed observer without the chattering problem and the steady-state error. The effectiveness of the proposed observer is confirmed by the comparative experimental results.     

Robust motion/force control of nonholonomic mobile manipulators using hybrid joints

In this paper, robust force/motion control strategies are presented for mobile manipulators under both holonomic and non-holonomic constraints in the presence of uncertainties and disturbances. The proposed control strategies guarantee that the system motion converges to the desired manifold with prescribed performance and constraint force control is developed using the passivity of hybrid joints rather than force feedback control. Experiment results validate that not only the states of the system asymptotically converge to the desired trajectory, but also the constraint force asymptotically converges to the desired force.     

Stable adaptive control using fuzzy systems and neural networks

Stable direct and indirect adaptive controllers are presented, which use Takagi-Sugeno fuzzy systems, conventional fuzzy systems, or a class of neural networks to provide asymptotic tracking of a reference signal for a class of continuous-time nonlinear plants with poorly understood dynamics. The indirect adaptive scheme allows for the inclusion of a priori knowledge about the plant dynamics in terms of exact mathematical equations or linguistics while the direct adaptive scheme allows for the incorporation of such a priori knowledge in specifying the controller. We prove that with or without such knowledge both adaptive schemes can “learn” how to control the plant, provide for bounded internal signals, and achieve asymptotically stable tracking of a reference input. In addition, for the direct adaptive scheme a technique is presented in which linguistic knowledge of the inverse dynamics of the plant may be used to accelerate adaptation. The performance of the indirect and direct adaptive schemes is demonstrated through the longitudinal control of an automobile within an automated lane     

Adaptive-backstepping force/motion control for mobile-manipulator robot based on fuzzy CMAC neural networks

In this paper, an adaptive backstepping fuzzy cerebellar-model-articulation-control neural-networks control （ABFCNC） system for motion/force control of the mobile-manipulator robot （MMR） is proposed. By applying the ABFCNC in the tracking-position controller, the unknown dynamics and parameter variation problems of the MMR control system are relaxed. In addition, an adaptive robust compensator is proposed to eliminate uncertainties that consist of approximation errors, uncertain disturbances. Based on the tracking position-ABFCNC design, an adaptive robust control strategy is also developed for the nonholonomicconstraint force of the MMR. The design of adaptive-online learning algorithms is obtained by using the Lyapunov stability theorem. Therefore, the proposed method proves that it not only can guarantee the stability and robustness but also the tracking performances of the MMR control system. The effectiveness and robustness of the proposed control system are verified by comparative simulation results.     

Combined direct and indirect adaptive control of robot manipulators using multiple models

A novel methodology is proposed for the adaptive control of rigid robotic manipulators. The proposed method utilizes multiple adaptive models for the identification and control of the manipulator. The present study is an extension of our previous work which utilized an indirect adaptive control approach with multiple models for better transient performance. The proposed scheme uses a composite approach where both prediction and tracking errors are used in a combined direct and indirect adaptive control framework. Simulation results are given to demonstrate the efficient use of the methodology.     

The development and control of a flexible-spine for a human-form robot

In this paper, the development of a robot which has a flexible spine is presented. By embedding a multi-d.o.f. soft structure into a robot body as a spine, the robot can increase its ability to absorb shock and to work in various environment such as narrow places. As a result of these abilities, the robot can expand its opportunity to work in the human environment. Moreover, its motion could be more natural. The developed full-body human-form robot has a five-jointed flexible spine. Each joint (vertebra) has 3 d.o.f. Between each vertebrae is a 'disk' made of silicone rubber. The spine is controlled by eight tendons, whose tensions can be controlled using tension sensors and locally distributed microcontrollers. This paper describes the development of the flexible spine and the control of the posture of the spine and body.     

Parkinson's disease: a motor control study using a wrist robot

Deep brain stimulation (DBS) is the most common surgical procedure for patients with Parkinson's disease (PD). DBS has been shown to have a positive effect on PD symptoms; however, its specific effects on motor control are not yet understood. We introduce the novel use of a wrist robot in studying the effects of stimulation on motor performance and learning. We present results from patients performing reaching movements in a null field and in a force field with and without stimulation. We discuss special cases where robotic testing reveals otherwise undiagnosed impairments, and where clinical scores and robot-based scores display opposing trends.     

A one linear actuator hopping robot: modeling and control

This paper addresses the modeling and control design of a one linear actuator hopping robot. The robot consists of a body and a leg, which are in contact with a sufficiently wide horizontal ground surface; both are fixed rigidly. Force actuation affects the angle of the body by a force couple that arises due to the mass of the body, as well as the length of the leg; hence both the angular velocity of the body and height of a jump can be controlled by only one actuator. Since the aim of this study is to achieve continuous hopping motion while keeping the system as simple as possible, an ON-OFF actuator is employed. Hence, we consider utilizing the thrust timing of the actuator—when robot is in the stance phase—to control the gait. For better stability of the hopping motion, optimization of mechanical parameters was made possible by evaluating the numerically obtained transition map, which contains a transition from one standard position to the next. The system is considered as a discrete system, in which one cycle of motion is regarded as one sampling interval. Finally, a control system was designed in which, by simulation, the continuous hopping gait was realized.     

Stability analysis of robust adaptive hybrid position/force controller for robot manipulators using neural network with uncertainties

The aim of this paper is to design a robust adaptive neural network-based hybrid position/force control scheme for robot manipulators in the presence of model uncertainties and external disturbance. The feedforward neural network employed to learn a highly nonlinear function requires no preliminary learning. The control purposes are to achieve the stability in the sense of Lyapunov for desired interaction force between the end-effector and the environment and to regulate robot tip position in cartesian space. An adaptive compensator is also developed to eliminate the effect of disturbance term of neural network approximation error and external disturbance or unmodeled dynamics etc. A key feature of this compensator is that the prior information of the disturbance bound is not required. Finally, a comparative simulation study with a model-based robust control scheme for a two-link robot manipulator is presented.     

Adaptive hybrid force/position control of robot manipulators using an adaptive force estimator in the presence of parametric uncertainty

This study is devoted to sensorless adaptive force/position control of robot manipulators using a position-based adaptive force estimator (AFE) and a force-based adaptive environment compliance estimator. Unlike the other sensorless method in force control that uses disturbance observer and needs an accurate model of the manipulator, in this method, the unknown parameters of the robot can be estimated along with the force control. Even more, the environment compliance can be estimated simultaneously to achieve tracking force control. In fact, this study deals with three challenging problems: No force sensor is used, environment stiffness is unknown, and some parametric uncertainties exist in the robot model. A theorem offers control laws and updating laws for two control loops. In the inner loop, AFE estimates the exerted force, and then, the force control law in the outer loop modifies the desired trajectory of the manipulator for the adaptive tracking loop. Besides, an updating law updates the estimated compliance to provide an accurate tracking force control. Some experimental results of a PHANToM Premium robot are provided to validate the proposed scheme. In addition, some simulations are presented that verify the performance of the controller for different situations in interaction.     

Adaptive neuro fuzzy based hybrid force/position control for an industrial robot manipulator

In this paper an ANFIS-PD+I (AFSPD+I) based hybrid force/position controller has been proposed which works effectively with unspecified robot dynamics in the presence of external disturbances. A constraint is put to limit the movement of manipulator in XY Cartesian coordinates. The validity of the proposed controller has been tested using a 6-degree of freedom PUMA robot manipulator. The performance comparison have been done with the fuzzy proportional derivative plus integral, fuzzy proportional integral derivative and conventional proportional integral derivative controllers subjected to the same data set with proposed controller. The projected AFSPD+I controller adhered to the desired path closer and smoother than the other mentioned controllers.     

Pivoting motion control for a laparoscopic assistant robot and human clinical trials

This paper presents a compliant motion control method for the robotic assistant ERM (Endoscopic Robotic Manipulator), designed and developed by the authors for handling the camera in laparoscopic surgery. Since the robot has a passive wrist and it is not fixed to the operating table, the relative position between the robot camera holder and the insertion point is unknown. In this way, the proposed approach keeps the camera orientation according to the motion references in spite of this uncertainty and compensates for other unexpected disturbances about the relative robot–patient position. This system has been tested with live animals as well as in clinical trials on humans.     

Two-stage structural damage detection using fuzzy neural networks and data fusion techniques

It is proposed in this paper a novel two-stage structural damage detection approach using fuzzy neural networks (FNNs) and data fusion techniques. The method is used for structural health monitoring and damage detection, particularly for cases where the measurement data is enormous and with uncertainties. In the first stage of structural damage detection, structural modal parameters derived from structural vibration responses are fed into an FNN as the input. The output values from the FNN are defuzzified to produce a rough structural damage assessment. Later, in the second stage, the values output from three different FNN models are input directly to the data fusion center where fusion computation is performed. The final fusion decision is made by filtering the result with a threshold function, hence a refined structural damage assessment of superior reliability. The proposed approach has been applied to a 7-degree of freedom building model for structural damage detection, and proves to be feasible, efficient and satisfactory. Furthermore, the simulation result also shows that the identification accuracy can be boosted with the proposed approach instead of FNN models alone.     

Global adaptive tracking control of robot manipulators using neural networks with finite-time learning convergence

In this paper, the global adaptive neural control with finite-time (FT) convergence learning performance for a general class of nonlinear robot manipulators has been investigated. The scheme proposed in this paper offers a subtle blend of neural controller with robust controller, which palliates the limitation of neural approximation region to ensure globally uniformly ultimately bounded (GUUB) stability by integrating a switching mechanism. Morever, the proposed scheme guarantees the estimated neural weights converging to optimal values in finite time by embedding an adaptive learning algorithm driven by the estimated weights error. The optimal weights obtained through the learning process of the neural networks (NNs) will be reused next time for repeated tasks, and can thus reduce computational load, improve transient performance and enhance robustness. The simulation studies have been carried out to demonstrate the superior performance of the controller in comparison to the conventional methods.

     

一种通过模糊自调整应用于机械手广义力/位置控制的稳定系统研究

提出一种基于模糊自调整的机械手控制结构,并针对机械手与外界环境接触时产生的作用力,定义了一种广义力,它是机械手执行机构输出力与机械手末端受到外界力的合力。那么,就可以用类似于机械手位置控制的方法达到力/位置控制目的,通过模糊自调整方法实现。在机械手受到的外力是有界限的前提下,考虑机械手非线性、耦合和多变量的动态特征,证明了整个闭环系统是全局稳定的。