Spatial impedance control of two cooperative industrial manipulators

Nowadays robotic manipulators are considered to perform a wide range of tasks. Since robotic systems consisting of multiple manipulators are capable of handling a variety of tasks that cannot be executed by single manipulators, cooperation of multiple manipulators is becoming increasingly interesting in particular for industrial applications. The interaction with other manipulators, respectively the environment, requires an extension of the conventional position control in order to achieve a desired compliance, and thus to limit the interaction wrench so to avoid damaging the involved objects. In this contribution the cooperation of two industrial manipulators, a Stäubli RX130L (6-DOF) and a Stäubli TX90L mounted on a linear axis (constituting a redundant 7-DOF manipulator), is addressed applying an impedance control scheme. The manipulation task is to grasp an object with both manipulators and to follow a prescribed trajectory by simultaneously limiting the contact forces between the manipulators and the object and ensuring a compliant behavior towards the environment. (© 2016 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Design an interactive virtual physics environment with uncertainties control to support motion tracking

The development of robot or character motion tracking algorithms is inherently a challenging task. This is more than ever true when the latest trends in motion tracking are considered. Some researchers can deal with kinematic and dynamic constraints induced by the mechanical structure. Another class of researchers fulfills various types of optimality conditions, yet others include means of dealing with uncertainties about the robot or character and its environment. In order to deal with the complexity of developing motion tracking algorithms, it is proposed in this paper to design an interactive virtual physics environment with uncertainties for motion tracking based on sliding mode control. The advantages of doing so are outlined and a virtual environment presented which is well suited to support motion tracking development. The environment makes full use of multi-body system dynamics and a robust sliding mode controller independent of model as simulation kernel. So the environment is capable of simulating setups which fulfill the requirements posed by state-of-the-art motion tracking algorithm development. The demonstration results verified the validity of the environment.     

力觉临场感遥操作机器人系统的鲁棒控制

力觉临场感遥操作机器人系统的通信通道中存在通信时延,而且在机器人和环境建模中,系统参数存在不确定性,以致可能造成系统不稳定和操作性能降低.针对通信时延和系统不确定性,建立系统的状态方程,利用鲁棒控制理论,提出用力、位置和速度反馈的控制方法.分析与实验表明,用该方法设计的控制器能使系统鲁棒渐近稳定,而且能使系统完全透明.     

Modelling a hormone-inspired controller for individual- and multi-modular robotic systems

For all living organisms, the ability to regulate internal homeostasis is a crucial feature. This ability to control variables around a set point is found frequently in the physiological networks of single cells and of higher organisms. Also, nutrient allocation and task selection in social insect colonies can be interpreted as homeostatic processes of a super-organism. And finally, behaviour can also represent such a control scheme. We show how a simple model of hormone regulation, inspired by simple biological organisms, can be used as a novel method to control the behaviour of autonomous robots. We demonstrate the formulation of such an artificial homeostatic hormone system (AHHS) by a set of linked difference equations and explain how the homeostatic control of behaviour is achieved by homeostatic control of the internal ‘hormonal’ state of the robot. The first task that we used to check the quality of our AHHS controllers was a very simple one, which is often a core functionality in controller programmes that are used in autonomous robots: obstacle avoidance. We demonstrate two implementations of such an AHHS controller that performs this task in differing levels of quality. Both controllers use the concept of homeostatic control of internal variables (hormones) and they extend this concept to also include the outside world of the robots into the controlling feedback loops: As they try to regulate internal hormone levels, they are forced to keep a homeostatic control of sensor values in a way that the desired goal ‘obstacle avoidance’ is achieved. Thus, the created behaviour is also a manifestation of the acts of homeostatic control. The controllers were evaluated using a stock-and-flow model that allowed sensitivity analysis and stability tests. Afterwards, we have also tested both controllers in a multi-agent simulation tool, which allowed us to predict the robots' behaviours in various habitats and group sizes. Finally, we demonstrate how this novel AHHS controller is suitable to control a multi-cellular robotic organism in an evolutionary robotics approach, which is used for self-programming in a gait-learning task. These examples shown in this article represent the first step in our research towards autonomous aggregation and coordination of robots to higher-level modular robotic organisms that consist of several joined autonomous robotic units. Finally, we plan to achieve such aggregation patterns and to control complex-shaped robotic organisms using AHHS controllers, as they are described here.     

Dynamic Modeling and Force Control of a Redundant Robot for Polishing Applications

For many robotic applications with tasks such as cutting, assembly or polishing, it is necessary to get in contact with the surrounding. In this paper a redundant robot with seven degrees of freedom in a metal polishing task is considered. For simulation as well as for the controller design a dynamic model of the robot and a contact model are required. The equations of motion of the robot are calculated with the Projection Equation in subsystem representation and the contact model contains linear tool elasticities and work piece elasticities. In the case of a polishing task, a constant contact force during the process is required even if the robot moves along a trajectory. Thus some degrees of freedom of the robot tool center point have to be position controlled while the other ones have to be force controlled. The redundant robot offers the possibility to avoid singular positions or to maximize the available end-effector forces within the inverse kinematics and is therefore best suited for polishing large objects. The actual process forces are measured with a six axis force-torque-sensor mounted at the tool center point. These forces are used in a parallel force/position control law to achieve the desired behavior. Results from measurements of a test arrangement are presented. (© 2014 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Robust digital controllers for uncertain chaotic systems: A digital redesign approach

In this paper, a new and systematic method for designing robust digital controllers for uncertain nonlinear systems with structured uncertainties is presented. In the proposed method, a controller is designed in terms of the optimal linear model representation of the nominal system around each operating point of the trajectory, while the uncertainties are decomposed such that the uncertain nonlinear system can be rewritten as a set of local linear models with disturbed inputs. Applying conventional robust control techniques, continuous-time robust controllers are first designed to eliminate the effects of the uncertainties on the underlying system. Then, a robust digital controller is obtained as the result of a digital redesign of the designed continuous-time robust controller using the state-matching technique. The effectiveness of the proposed controller design method is illustrated through some numerical examples on complex nonlinear systems––chaotic systems.     

Adaptive fuzzy output feedback control for a single-link flexible robot manipulator driven DC motor via backstepping

In this paper, an adaptive fuzzy output feedback approach is proposed for a single-link robotic manipulator coupled to a brushed direct current (DC) motor with a nonrigid joint. The controller is designed to compensate for the nonlinear dynamics associated with the mechanical subsystem and the electrical subsystems while only requiring the measurements of link position. Using fuzzy logic systems to approximate the unknown nonlinearities, an adaptive fuzzy filter observer is designed to estimate the immeasurable states. By combining the adaptive backstepping and dynamic surface control (DSC) techniques, an adaptive fuzzy output feedback control approach is developed. Stability proof of the overall closed-loop system is given via the Lyapunov direct method. Three key advantages of our scheme are as follows: (i) the proposed adaptive fuzzy control approach does not require that all the states of the system be measured directly, (ii) the proposed control approach can solve the control problem of robotic manipulators with unknown nonlinear uncertainties, and (iii) the problem of “explosion of complexity” existing in the conventional backstepping control methods is avoided. The detailed simulation results are provided to demonstrate the effectiveness of the proposed controller.     

Recursive Control of a 7 dof Robotic Arm

This paper provides a short insight into tracking control of uncertain nonlinear plants with the focus on ease of application and simple parameter tuning. The advanced control scheme is based on recursive control laws which have been shown in the last decades to be efficient in control of robotic motion. The algorithm is recursive rapidly convergent and robust. The development of humanoid robotic arms within the collaborative research center (CRC588) requires a class of easy implementable and robust nonlinear controllers to accomplish manipulation tasks in permanently alternating scenarios. The ability of the controller is tested by simulation for several robotic arms with increasing dof and variable end–effector mass for given human arm movements provided from a motion capture system. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Intelligent active force control of a 3-RRR parallel manipulator incorporating fuzzy resolved acceleration control

This paper introduces a novel intelligent control scheme for robust and precise positioning and orientation of a class of highly non-linear 3-RRR (revolute-revolute-revolute) planar parallel manipulator. The primary objective is to force the manipulator to track accurately a prescribed Cartesian trajectory when the system is subjected to different types of disturbances in the forms of forced harmonic excitations. A two level fuzzy tuning resolved acceleration control (FLRAC) is first designed and implemented to the system to demonstrate the stable response of the manipulator in performing trajectory tracking tasks in the absence of the disturbances. In this scheme, the first level of fuzzy tuning is used to acquire the proportional-derivative (PD) gains linearly while the second level considers non-linear tuning for determining the other parameters of the fuzzy controller to increase its performance. Then, the controller is added in series with an active force controller (AFC) to create a novel two degree-of-freedom (DOF) controller known as FLRAC-AFC which is subsequently and rigorously tested for system robustness and accuracy in tracking the prescribed trajectory. The simulation study provides further insight into the potentials of the proposed robotic system in rejecting the disturbances for the given operating conditions. The results clearly show that the FLRAC-AFC scheme provides a much superior trajectory tracking capability compared to the conventional linear RAC alone.     

Accurate and robust body position trajectory tracking of six-legged walking robots with nonsingular terminal sliding mode control method

Many tasks, such as walking in narrow environments, detecting land mines, coordinating with manipulators, and avoiding obstacles, demand multi-legged walking robots to accurately and robustly track predefined body trajectories. Tracking body position trajectory must be accurate and robust in these situations, but research on this topic is rarely carried out. In this study, we propose a nonsingular terminal sliding mode (NTSM) control algorithm to implement accurate and robust body position trajectory tracking of six-legged walking robots. The NTSM control algorithm is constructed on the basis of the body position trajectory tracking model with a new NTSM reaching law. The performance of the NTSM control method is evaluated through several verifications. Results demonstrate that the proposed algorithm is effective for accurate and robust body position trajectory tracking. The findings of this study can provide insights into improving multi-legged walking robots’ walking and operation abilities in special environments and expanding the application fields of these robots.     

A novel adaptive controller for two-degree of freedom polar robot with unknown perturbations

In industrial applications, the performance of robot manipulators is always affected due to the presence of uncertainties and disturbances. This paper proposes a novel adaptive control scheme for robust control of robotic manipulators perturbed by unknown uncertainties and disturbances. First, an active sliding mode controller is designed and a sufficient condition is obtained guarantying reachability of the states to hit the sliding surface in finite time. Then, based on a Lyapunov function candidate an adaptive switching gain is derived which make the controller capable to bring the tracking error to zero without any disturbance exerted upon the stability. By virtue of this controller it can be shown that the controller can track the desired trajectories even in the presence of unknown perturbations. For the problem of determining the control parameters Particle Swarm Optimization (PSO) algorithm has been employed. Our theoretic achievements are verified by numerical simulations.     

“货到人”拣选系统机器人任务分配的鲁棒双层规划模型

在电商“货到人”拣选系统中,如何调度系统中的机器人并对任务进行合理地分配决定着整个系统的运行效率与成本。分析“货到人”拣选系统作业流程,建立机器人数量配置、机器人调度与机器人任务分配的双层规划模型。上层模型以批量订单完成总成本最小为目标函数,以机器人调度为决策变量,构建整数规划模型;下层模型以机器人完成所有任务的平均空闲率最小为目标函数,以任务分配为决策变量,考虑机器人在完成任务过程中由于调度、避障、路径规划等导致的行走距离不确定因素,构建鲁棒优化模型。上层的调度结果制约了下层的最小平均空闲率,下层的任务分配结果影响上层的最小成本,上下层结果共同决定机器人配置决策。利用遗传算法求解模型,通过实例仿真验证了模型的有效性。     

The Lightweight Robot ElRob: Interesting Aspects in Modeling and Control

The articulated robot ElRob, consisting of flexible links and joints, is considered in several publications. Recent developments are presented in this work. The overall goal of the research is to decrease the effects of structural elasticities in lightweight robots. For this purpose model-based control concepts are investigated and very accurate and efficient kinematic and dynamic models are necessary. The robot is split into groups of bodies, the so called subsystems, with separated describing velocities and coordinate systems. To obtain structured equations of motion the Projection Equation is used. The beams are modelled using the floating frame of reference formulation and a Ritz-approach. Because of its flexibility, the examined robot is an underactuated system leading to special difficulties. As an example is it not possible to compute the desired joint angles with respect to a reference path in task space for the flexible system (inverse kinematic problem). Different methods to solve this drawback and other problems resulting from flexibility are discussed with special focus on feed forward control and different feedback control concepts. The resulting end point error, the necessary control input and other interesting results for the laboratory experiment are presented and compared. (© 2011 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Robust reliable stabilization of stochastic switched nonlinear systems under asynchronous switching

This paper is concerned with the problem of robust reliable control for a class of uncertain stochastic switched nonlinear systems under asynchronous switching, where the switching instants of the controller experience delays with respect to those of the system. A design scheme for the reliable controller is proposed to guarantee almost surely exponential stability for stochastic switched systems with actuator failures, and the dwell time approach is utilized for the stability analysis. Then the approach is extended to take into account stochastic switched system with Lipschitz nonlinearities and structured uncertainties. Finally, a numerical example is employed to verify the proposed method.     

Nonlinear adaptive output feedback robust control of hydraulic actuators with largely unknown modeling uncertainties

In this paper, a nonlinear adaptive output feedback robust controller is proposed for motion control of hydraulic servo systems in the presence of largely unknown matched and mismatched modeling uncertainties. Different from the existing control technologies, the presented hydraulic closed-loop controller which can deal with strong matched and mismatched parametric uncertainties is synthesized via the backstepping technique. Specially, a nonlinear disturbance observer which can estimate the largely mismatched disturbance is integrated into the design of the linear extended state observer to obtain estimation of unmeasurable system states, uncertain parameters and strong disturbances simultaneously. In addition, the projection-type adaptive law is synthesized into the design of the resulting controller. More importantly, the global stability of the whole closed-loop system is strictly guaranteed by the Lyapunov analysis. Furthermore, the effectiveness and practicability of the presented control strategy have been demonstrated by comparative experiments under different working conditions.     

Robust control of multiple robots handling a constrained load

Multiple-robot systems are usually confronted with uncertainties,such as uncertainties in the manipulators and load parameters,and unmodelled dynamics. In this paper, the problem of controllingmultiple manipulators handling a constrained load is addressed.A reduced-order dynamic model of the system is first derived,and several properties of this model are established. Usingthe reduced-order model, a robust control law is proposed. Thiscontroller guarantees the uniform ultimate boundedness of theposition error, the internal-force error, and the constraint-forceerror. The proposed control law requires only the bounds onthe uncertainties of the parameters. Simulation results of twoplanar robots moving a load along a horizontal plane are givento illustrate the theoretical developments.     

Robust D-stability analysis for linear discrete singular time-delay systems with structured and unstructured parameter uncertainties

In this paper, the robust D-stability problem (i.e. the robusteigenvalue-clustering in a specified circular region problem)of linear discrete singular time-delay systems with structured(elemental) and unstructured (norm-bounded) parameter uncertaintiesis investigated. Under the assumptions that the linear nominaldiscrete singular time-delay system is regular and impulse-free,and has all its finite eigenvalues lying inside a specifiedcircular region, a new sufficient condition is proposed to preservethe assumed properties when structured and unstructured parameteruncertainties are added into the linear nominal discrete singulartime-delay system. When all the finite eigenvalues are justrequired to locate inside the unit circle of the z-plane, theproposed criterion will become the stability robustness criterion.For the case that the linear discrete singular time-delay systemis only subject to structured parameter uncertainties, by anillustrative example, the presented sufficient condition isshown to be less conservative than the existing one reportedrecently in the literature.     

Algoritmo compensador neuronal discreto de dinámica en robots móviles usando filtro de Kalman extendido

This paper presents the design of an algorithm based on neural networks in discrete time for its application in mobile robots. In addition, the system stability is analyzed and an evaluation of the experimental results is shown.The mobile robot has two controllers, one addressed for the kinematics and the other one designed for the dynamics. Both controllers are based on the feedback linearization. The controller of the dynamics only has information of the nominal dynamics (parameters). The neural algorithm of compensation adapts its behaviour to reduce the perturbations caused by the variations in the dynamics and the model uncertainties. Thus, the differences in the dynamics between the nominal model and the real one are learned by a neural network RBF (radial basis functions) where the output weights are set using the extended Kalman filter. The neural compensation algorithm is efficient, since the consumed processing time is lower than the one required to learning the totality of the dynamics. In addition, the proposed algorithm is robust with respect to failures of the dynamic controller. In this work, a stability analysis of the adaptable neural algorithm is shown and it is demonstrated that the control errors are bounded depending on the error of approximation of the neural network RBF. Finally, the results of experiments performed by using a mobile robot are shown to test the viability in practice and the performance for the control of robots.     

A computationally efficient robust tube based MPC for linear switched systems

This article considers the robust regulation problem for a class of constrained linear switched systems with bounded additive disturbances. The proposed solution extends the existing robust tube based model predictive control (RTBMPC) strategy for non-switched linear systems to switched systems. RTBMPC utilizes nominal model predictions, together with tightened sets constraints, to obtain a control policy that guarantees robust stabilization of the dynamic systems in presence of bounded uncertainties. In this work, similar to RTBMPC for non-switched systems, a disturbance rejection proportional controller is used to ensure that the closed loop trajectories of the switched linear system are bounded in a tube centered on the nominal system trajectories. To account for the uncertainty related to all sub-systems, the gain of this controller is chosen to simultaneously stabilize all switching dynamics. The switched system RTBMPC requires an on-line solution of a Mixed Integer Program (MIP), which is computationally expensive. To reduce the complexity of the MIP, a sub-optimal design with respect to the previous formulation is also proposed that uses the notion of a pre-terminal set in addition to the usual terminal set to ensure stability. The RTBMPC design with the pre-terminal set aids in determining the trade-off between the complexity of the control algorithm with the performance of the closed-loop system while ensuring robust stability. Simulation examples, including a Three-tank benchmark case study, are presented to illustrate features of the proposed MPC.     

Adaptive cooperative control of networked uncalibrated robotic systems with time‐varying communicating delays

This paper investigates the trajectory tracking control of the networked multimanipulator with the existence of time‐varying delays and uncertainties in both kinematics and dynamics. To address time‐varying delays in the communication links, a novel control scheme is established by the design of delay–rate‐dependent networking mutual coupling strengths. Besides, to handle the kinematic and dynamic uncertainties, an adaptive controller is designed. The proposed control scheme guarantees that the networked robotic system can track a commonly desired trajectory cooperatively with the strongly connected communication graph, uncertainties, and time‐varying communicating delays. A Lyapunov–Krasovskii functional is employed to rigorously prove the asymptotic convergence of both tracking errors and synchronization errors. The simulation results are provided to verify the effectiveness of the control method proposed by this paper.