Design Of Force Controller Research Articles

Feedback force and velocity control of an arm exoskeleton to assist user motion

The paper proposes a feedback force and velocity control of an arm exoskeleton to assist user motion. The original published control so-called feedback hybrid force and position control was based on the force and position control and was designed to assist user motion. This original control was successful at providing assist for the user’s arm. This article presents an improved control scheme called the feedback force and velocity control. The proposed control is designed to regulate the velocities of joints of the exoskeleton and the feedback forces on links to assist user motion. The design and optimization of the feedback force and velocity control are realized by the Balancing Composite Motion Optimization (BCMO). The numerical method is realized in the paper to show that the proposed control is better than the original control in terms of less oscillation and fast response.

Analysis and Design of Direct Force Control for Robots in Contact with Uneven Surfaces

Robots executing contact tasks are essential in a wide range of industrial processes such as polishing, welding, debugging, drilling, etc. Force control is indispensable in these type of tasks since it is required to keep the interaction force (between the robot and the environment/surface) within acceptable values. In this paper, we present a methodology to analyze and to design the force control system needed to regulate the force as close as possible to the desired value. The proposed methods are presented using a widely used generic contact task consisting of exerting a desired force on the normal direction to the surface while a desired velocity/position is tracked on the tangent direction to the surface. The analysis considers environments/surfaces with certain uneven characteristics, i.e., not perfectly flat. The uneven characteristic is studied using ramp or sinusoidal signals disturbing the position on the normal direction to the surface, and we present how the velocity on the tangent direction is related with the slope of the ramp or the frequency of the sinusoidal disturbance. Then, we provide a method to design the force controller that keeps the force error within desired limits and preserves stability, despite the uneven surface. Furthermore, considering the relation between the disturbance (ramp or sinusoidal) and the tangent velocity, we present a method to compute the maximum velocity for which the task can be executed. Simulations exemplifying and verifying the proposed methods are presented.

Forced Servoing of a Series Elastic Actuator Based on Link-Side Acceleration Measurement

Joint stiffness of an elastic-joint robot can be changed according to joint stiffness requirements. A series elastic actuator (SEA) can reduce the contact stiffness between the body and the environment or human, which can further ensure interactive operation in a human–machine-compatible environment. However, the introduction of the SEA improves the complexity of the robot dynamics model. In this paper, we propose a control schema based on link-side acceleration measurement to eliminate the overshoot and vibration in the transient process of force control. An extended Kalman filter (EKF) algorithm that fuses photoelectric encoders and accelerometers is first presented based on the link-side acceleration measurement. Following this, based on the external torque estimation, the vibration reduction control algorithm is designed. The simulation model is built, and the algorithm design and simulation of position control and force control are carried out and finally tested on the real robot platform. The effectiveness of the control algorithm is proved. The experimental results show that the dynamic response of the external force estimation is about 2 ms faster than that of the force sensor, and the error between the estimated external torque and the real external torque is within ±0.16 N·m.

Design of Active Force Controller for Off-Road Seat Suspension equipped to MR Damper

ارتعاشات انتقال‏یافته به بدن راننده در خودروهای غیر‏جاده‏ای سنگین که سیستم تعلیق اولیه ندارند باعث ایجاد مشکلات سلامتی در بلند مدت می‏گردد. معمولا در این‏گونه از وسایل‏نقلیه سیستم تعلیق ثانویه در صندلی راننده تعبیه می‏شود تا بتواند جداسازی ارتعاشی و جذب آن را انجام دهد و باعث جلوگیری از مشکلات سلامتی بخصوص در ستون فقرات شود. سیستم تعلیق غیرفعال صندلی به طور گسترده برای این منظور استفاده می‏شود، اما کارایی آنها پایین است. سیستم تعلیق صندلی فعال و سیستم تعلیق نیمه‏فعال عملکرد بهتری در جلوگیری از نوسانات ناخواسته دارند. یکی از میراگرهایی که اخیرا مورد توجه محققان قرار گرفته است میراگر نیمه‏فعال مجهز به سیال هوشمند مگنتورئولوژیک است که با ایجاد میدان مغناطیس گرانروی سیال تغییر می‏کند. بدین‏ترتیب سیستم تعلیق با استفاده کمی از جریان الکتریسیته می‏تواند با تغییر ضریب میرایی به‏صورت موثری انرژی ارتعاشی را مستهلک کند. در مقاله حاضر یک کنترل‏گر جدید هوشمند فعال نیرو مجهز به تخمین‏گر یادگیری تدریجی طراحی و شبیه‏سازی شده است. سیستم تعلیق به مدل ارتعاش بدن انسان کوپل شد و نتایج ارتعاش بدن راننده بدست آمد. نکته قابل توجه این است که کنترل فعال نیرو می‏تواند برای حذف اغتشاشات سرعت بالا نیز بکار رود. از آنجایی‏که ارتعاشات رخ داده در خودرو دارای سرعت تغییرات بالا هستند نتایج نشان داد که کنترل فعال نیرو مجهز به سامانه تخمین‏گر یادگیری تدریجی می‏تواند بطور موثری در کاهش ارتعاش انتقالی به راننده عمل کند بطوری‏که پیک‏های اول و دوم ارتعاش در حدود 60 درصد کاهش داشتند.

Study of Characteristics of Off-road Driving and Driving Force Control Design for Better Operability and Traveling Performance on Off-road Drive

Study of Characteristics of Off-road Driving and Driving Force Control Design for Better Operability and Traveling Performance on Off-road Drive

Precision force control of an underactuated stance leg exoskeleton for human performance augmentation

Lower limb exoskeleton which augments the human performance is a wearable human–machine integrated system used to assist people carrying heavy loads. Recently, underactuated lower limb exoskeleton systems with some passive joints become more and more attractive due to the advantages of smaller weight, lower system energy consumption and lower cost. However, because of the less of control inputs, the existed control methods of fully actuated exoskeletons cannot be extended to underactuated systems, which makes the robust controller design of underactuated lower limb exoskeletons becomes more challenged. This article focuses on the high-performance human–machine interaction force control design of underactuated lower limb exoskeletons with passive ankle joint. In order to solve the reduction of control inputs, the holonomic constraint from the wearer is considered, which help transform the dynamics of 3-degree-of-freedom underactuated exoskeleton in joint space into a 2-degree-of-freedom fully actuated system in Cartesian space. A two-level interaction force controller using adaptive robust control algorithm is proposed to effectively address the negative effect of various model uncertainties and external disturbances. In order to facilitate the control parameter selection, a gain tuning method is also presented. Comparative simulations are carried out, which indicate that the proposed two-level interaction force controller achieves smaller interaction force and better robust performance to various modeling errors and disturbances.

Design, evaluation, and implementation of a model-predictive control approach for a force control in friction stir welding processes

Friction press joining is an innovative joining process for bonding plastics and metals without additives in an overlap configuration. A model-based approach for the design of an axial force controller for friction press joining is presented in this paper. A closed-loop control was set up on the machining center, in which the plunge depth was used as the controlling variable. In order to support the controller development, a nonparametric dynamic process model was developed via a data-based system identification. Subsequently, various control concepts were designed off-line and verified on the actual system. The most promising ones, a proportional controller, a controller created with the pole placement method, and a model predictive controller, were selected for further investigations. The three controllers were re-evaluated and compared by means of a defined input of disturbance variables and reference variables. The model predictive control (MPC) approach as well as the proportional controller were also tested for model uncertainties. For this purpose, different material combinations were joined using the different controllers. Thereby, it was shown that the MPC controller resulted in smaller standard deviations when encountering large model uncertainties. The investigations demonstrated the high potential of friction press joining of plastic components with metals. The results form the basis for future research, whereby the force can be specified as an additional input parameter instead of the plunge depth.

Design of model predictive force control for hydraulic servo system based on cuckoo search and genetic algorithms

This article discusses a system identification based on a black-box state-space model for an experimental electro-hydraulic servo system. Furthermore, it presents force-tracking control for the electro-hydraulic servo system based on model predictive control. The parameters of model predictive controls have been tuned by cuckoo search algorithm as well as genetic algorithm. The realization of model predictive controls depends on using a data acquisition card (NI-6014) and Simulink/MATLAB as the core of the electro-hydraulic servo system control system. In this research, the combination of model predictive control tuned by cuckoo search algorithm and genetic algorithm has been introduced in the form of switching model predictive controls. This combination collects the advantages of two model predictive controls in one model predictive control by switching model predictive controls. The simulation and experimental results display that the suggested switching of model predictive controls introduces a good tracking performance in terms of settling time, rise time, and system overshoots as compared to the two separated model predictive controls. In addition, the experimental evaluation has shown that the proposed switching model predictive controls achieved a stable and robust control system even facing to a different reference command signals (step, multistep, and sinusoidal signals). Moreover, its behavior is more robust for system parameters perturbation and small or large perturbation of disturbances in the working environment. It also achieves the necessitated physical limits of the actuator. As a general conclusion and a deep study of electro-hydraulic servo system, one can conclude that the switching strategy between model predictive control tuned by cuckoo search algorithm and by genetic algorithm has the priority of applying it on the field of electro-hydraulic servo system. The proposed new strategy (switching of model predictive control) is promising in experimental applications.

Regressor-free adaptive fuzzy force tracking control of redundant robot manipulator for task space

In this study, an adaptive fuzzy force control of a redundant robot manipulator experiencing system uncertainties and operating in an unknown environment is proposed. This is important not only to provide additional control flexibility for complicated tasks but also to avoid the joint limit of a robot in implementing better dynamics and kinematics. The relation between the task and joint spaces is discussed to derive a dynamic model for force tracking controller design. To treat the system uncertainties, an adaptive fuzzy system approach is established to achieve the adaptive position and force controller design based on a regressor-free approach. Considering that the stiffness coefficient of the environment is assumed to be unknown, the gradient descent method is used to estimate this coefficient to achieve adaptive force tracking. A stability analysis of the closed-loop system and the corresponding update laws are given by the Lyapunov stability theorem. Finally, several apposite simulations using the KUKA lightweight robot are performed to validate our approach and demonstrate the performance and effectiveness of the proposed regressor-free adaptive fuzzy force controller.

Force control design for robots based on correlation in human demonstration data

Force control design for robots based on correlation in human demonstration data

Impedance control of collaborative robots based on joint torque servo with active disturbance rejection

PurposeThe purpose of this paper is to present a simple yet effective force control scheme for collaborative robots by addressing the problem of disturbance rejection in joint torque: inherent actuator flexibility and nonlinear friction.Design/methodology/approachIn this paper, a joint torque controller with an extended state observer is used to decouple the joint actuators from the multi-rigid-body system of a constrained robot and compensate the motor friction. Moreover, to realize robot force control, the authors embed this controller into the impedance control framework.FindingsResults have been given in simulations and experiments in which the proposed joint torque controller with an extended state observer can effectively estimate and compensate the total disturbance. The overall control framework is analytically proved to be stable, and further it is validated in experiments with a robot testbed.Practical implicationsWith the proposed robot force controller, the robot is able to change its stiffness in real time and therefore take variable tasks without any accessories, such as the RCC or 6-DOF F/T sensor. In addition, programing by demonstration can be realized easily within the proposed framework, which makes the robot accessible to unprofessional users.Originality/valueThe main contribution of the presented work is the design of a model-free robot force controller with the ability to reject torque disturbances from robot-actuator coupling effect and motor friction, applicable for both constrained and unconstrained environments. Simulation and experiment results from a 7-DOF robot are given to show the effectiveness and robustness of the proposed controller.

Design of Intelligent Hybrid Force and Position Control of Robot Manipulator

This work considers the Hybrid Force/Position control of robot manipulator in the presence of uncertainties and external disturbances. The proposed controller contains the model based term, Radial Basis Function neural network term plus an adaptive bound part. The Radial basis function neural network is functioning to learn a non linear function with no requirement of off line training. An adaptive bound part is developed to guess the unknown bound on the unmodeled disturbance, neural network reconstruction error and friction term. The Lyapunov function approach is used to the stability of the system. In the end simulations results are presented for two link robot manipulators.

Force control design for a robot manipulator attached to a UAV

Abstract This article focuses on the modeling and control of an Unmanned Aerial Vehicle (UAV) while applying forces to its surrounding environment using a manipulator. The manipulator is attached beneath the UAV. The derived mathematical model of the UAV carrying the manipulator, relies on the application of the Newton-Euler equations. Model-based controllers are designed, in order to stabilize the UAV while maintaining the contact with its surrounding environment and applying forces to it. The objective is to keep the end effector of the manipulator as close as possible to a desired point while at the same time exerting forces to it. Simulation studies using a quadrotor and a two Degree of Freedom (DoF) planar manipulator are provided to demonstrate the effectiveness of the proposed control scheme.

Adaptive Impedance Force Controller Design for Robot Manipulator including Actuator Dynamics

This study proposes an adaptive impedance force control using fuzzy neural networks (FNNs) for robot manipulator including actuator dynamics with uncertainties and operated in unknown environment. The system uncertainties are estimated by FNNs, and the corresponding adaptive impedance control is derived based on Lyapunov stability approach. Besides, the stiffness coefficient of contact environment is estimated by gradient method with convergent theory. The proposed approach does not calculate the regressor matrix which is a significant simplification in implementation. Simulation results are introduced to illustrate the effectiveness and performance of our approach.

A Robust Force Control Approach for Underwater Vehicle Manipulator Systems

In various interaction tasks using Underwater Vehicle Manipulator Systems (UVMSs) (e.g. sampling of the sea organisms, underwater welding), important factors such as: i) uncertainties and complexity of UVMS dynamic model ii) external disturbances (e.g. sea currents and waves) iii) imperfection and noises of measuring sensors iv) steady state performance as well as v) inferior overshoot of interaction force error, should be addressed during the force control design. Motivated by the above factors, this paper presents a model-free control protocol for force controlling of an Underwater Vehicle Manipulator System which is in contact with an unknown compliant environment, without incorporating any knowledge of the UVMS’s dynamic model, exogenous disturbances and sensor’s noise model. Moreover, the transient and steady state response as well as reduction of overshooting force error are solely determined by certain designer-specified performance functions and are fully decoupled by the UVMS’s dynamic model, the control gain selection, as well as the initial conditions. Finally, a simulation study clarifies the proposed method and verifies its efficiency.

Hunt–Crossley model based force control for minimally invasive robotic surgery

In Minimally Invasive Surgery (MIS) the continuously increasing use of robotic devices allows surgical operations to be conducted more precisely and more efficiently. Safe and accurate interaction between robot instruments and living tissue is an important issue for both successful operation and patient safety. Human tissue, which is generally viscoelastic, nonlinear and anisotropic, is often described as purely elastic for its simplicity in contact force control design and online computation. However, the elastic model cannot reproduce the complex properties of a real tissue. Based on in vitro animal tissue relaxation tests, we identify the Hunt-Crossley viscoelastic model as the most realistic one to describe the soft tissue's mechanical behavior among several candidate models. A force control method based on Hunt-Crossley model is developed following the state feedback design technique with a Kalman filter based active observer (AOB). Both simulation and experimental studies were carried out to verify the performance of developed force controller, comparing with other linear viscoelastic and elastic model based force controllers. The studies and comparisons show that the Hunt-Crossley model based force controller ensures comparable rise time in transient response as the controller based on Kelvin-Boltzmann model which is reported as the most accurate description for robot-tissue interaction in recent literature, but it causes much less overshoot and remains stable for tasks with faster response time requirements.

Application of cuckoo search algorithm to constrained control problem of a parallel robot platform

This paper presents a cascade load force control design for a parallel robot platform. A parameter search for a proposed cascade controller is difficult because there is no methodology to set the parameters and the search space is broad. A parameter search based on cuckoo search (CS) is suggested to effectively search parameters of the cascade controllers. The control design problem is formulated as an optimization problem under constraints. Typical constraints, such as mechanical limits on positions and maximal velocities of hydraulic actuators as well as on servo-valve positions, are included in the proposed algorithm. The optimal results are compared to the state-of-the-art algorithms for these problem instances (NP-hard and constrained optimization problems). Simulation results also show that applied optimal tuned cascade control algorithm exhibits a significant performance improvement over classical tuning methods.

Robust adaptive position and force controller design of robot manipulator using fuzzy neural networks

This paper presents a robust adaptive position and force control scheme for an n-link robot manipulator under unknown environment. The robot manipulator’s model and the stiffness coefficient of contact environment are assumed to be not exactly known. Therefore, the traditional impedance force controller cannot be applied. We herein adopt the fuzzy neural networks (FNNs) to estimate the unknown model matrices of robot manipulator and the adaptive tracking position and force control is developed by the proposed adaptive scheme. Based on the Lyapunov stability theory, the stability of the closed-loop system and convergence of adjustable parameters are guaranteed. The corresponding update laws of FNNs’ parameters and estimated stiffness coefficient of contacting environment can be derived. Finally, simulation results of a two-link robot manipulator with environment constraint are introduced to illustrate the performance and effectiveness of our approach.

Optimal control of hydraulically driven parallel robot platform based on firefly algorithm

A new cascade load force control design for a parallel robot platform is proposed. A parameter search for a proposed cascade controller is difficult because there is no methodology to set the parameters and the search space is broad. A parameter search based on firefly algorithm (FA) is suggested to effectively search the parameters of the cascade controller. We used unified mathematical model of hydraulic actuator of parallel robot platform. These equations are readily applicable to various types of proportional valves, and they unify the cases of critical center, overlapped and underlapped valves. These unified model equations are useful for nonlinear controller design. The optimal results are compared to those obtained from other metaheuristic algorithms: GA, PSO and CS. A comparative study is also made between proposed optimal tuned cascade control using FA and well-tuned PID controller. Simulation results show the advantages of the proposed optimal tuned cascade controller using FA to solve a formulated tracking problem.

Optimal cascade hydraulic control for a parallel robot platform by PSO

A new cascade load force control design for a parallel robot platform is proposed. A parameter search for a proposed cascade controller is difficult because there is no methodology to set the parameters and the search space is broad. A parameter search based on particle swarm optimization (PSO) is suggested to effectively search the parameters of the cascade controller. We used a unified mathematical model of a hydraulic actuator of parallel robot platform. These equations are readily applicable to various types of proportional valves, and they unify the cases of critical center, overlapped, and underlapped valves. These unified model equations are useful for nonlinear controller design. Simulation results show the advantages of the proposed optimal tuned cascade controller to solve the formulated tracking problem in relation to the classical proportional–integral (PI) controller.