Phylogenetic and Ontogenetic Learning in a Colony of Interacting Robots

The objective of this paper is to describe the development of a specific theory of interactions and learning among multiple robots performing certain tasks. One of the primary objectives of the research was to study the feasibility of a robot colony in achieving global objectives, when each individual robot is provided only with local goals and local information. In order to achieve this objective the paper introduces a novel cognitive architecture for the individual behavior of robots in a colony. Experimental investigation of the properties of the colony demonstrates its ability to achieve global goals, such as the gathering of objects, and to improve its performance as a result of learning, without explicit instructions for cooperation. Since this architecture is based on representation of the likes and dislikes of the robots, it is called the Tropism System Cognitive Architecture. This paper addresses learning in the framework of the cognitive architecture, specifically, phylogenetic and ontogenetic learning by the robots. The results show that learning is indeed possible with the Tropism Architecture, that the ability of a simulated robot colony to perform a gathering task improves with practice and that it can further improve with evolution over successive generations. Experimental results also show that the variability of the results decreases over successive generations.     

A novel soft computing architecture for the control of autonomous walking robots

An integration of concepts from neurobiology, applied psychology, insect physiology and behaviour based robotics has led us to propose a novel generic systems architecture for the intelligent control of mobile robots and in particular, autonomous walking machines. (We define what we mean by “autonomy”.) The control architecture is hierarchical and will be described from a top-down perspective. Level one consists of interpreting a motivation and translating this into high-level commands. Once a high-level command is generated, a range of internal representations or “cognitive maps” may be employed at level two to help provide body-centred motion. At level three of the hierarchy kinematic planning is performed. The fourth level – dynamic compensation – requires feedback from the actuators and compensates for errors in the target vectors provided by the kinematic level and caused by systematic dynamic uncertainties or environmental disturbances. This is implemented using adaptive neural controllers. The interfaces will be described and results from simulation and implementation of levels 2–4 on a hexapod robot will be presented. The hierarchy employs the following soft computing techniques: evolution strategies, cognitive maps, adaptive heuristic critics, temporal difference learning and adaptive neural control using linear-equivalent neural networks.     

基于人工情感的拟人机器人控制体系结构

简要概括了当前人工情感的应用，提出一种基于人工情感的拟人机器人控制体系结构，并给出了仿真示例．基于情感的控制结构具有混合分层的特点，情感状态影响到机器人的整个信息处理过程．这种结构不仅体现了机器人的个性化，同时增强了机器人在动态环境中的学习和自适应能力．     

An uncertainty compensator for robust control of wheeled mobile robots

This paper proposes an uncertainty compensator to design a novel robust control for mobile robots with dynamic and kinematic uncertainties. A novel gradient-based adaptive fuzzy estimator is developed to compensate uncertainties with minimum required feedback signals. As a novelty, the proposed approach uses the tracking error and its first time derivative to form the estimation error of uncertainty, and guarantees that both the estimation error and tracking error converge asymmetrically to ignorable value. Advantages of the proposed robust control are simplicity in design, robustness against uncertainties, guaranteed stability, and good control performance. The control approach is verified by stability analysis. Simulation results and experimental results illustrate the effectiveness of the proposed control. Experimental evaluation of the proposed controller is expressed for two different low-cost nonholonomic wheeled mobile robots. The proposed control design is compared with an adaptive control approach to confirm the superiority of the proposed approach in terms of precision, simplicity of design, and computations.     

Robust Adaptive Control of Robots Using Neural Network: Global Stability

A desired compensation adaptive law‐based neural network (DCAL‐NN) controller is proposed for the robust position control of rigid‐link robots. The NN is used to approximate a highly nonlinear function. The controller can guarantee the global asymptotic stability of tracking errors and boundedness of NN weights. In addition, the NN weights here are tuned on‐line, with no offline learning phase required. When compared with standard adaptive robot controllers, we do not require linearity in the parameters, or lengthy and tedious preliminary analysis to determine a regression matrix. The controller can be regarded as a universal reusable controller because the same controller can be applied to any type of rigid robots without any modifications. A comparative simulation study with different robust and adaptive controllers is included.     

含模型不确定性移动机器人路径跟踪的分层模糊控制

对受非完整约束且含模型不确定性的移动机器人基于分层模糊系统设计了跟踪期望几何路径的鲁棒间接自适应控制方案.此方法除实现路径跟踪外,还可避免控制器的奇异性并保证跟踪方向.由于控制结构中使用了分层模糊系统,大大减少了模糊规则数目;并用鲁棒控制项对模糊系统逼近误差进行补偿,减少了其对跟踪精度的影响.证明了闭环系统跟踪误差收敛到原点的小邻域内,且可通过适当增大鲁棒控制项的设计参数使跟踪误差进一步减小.最后用实验结果验证了方法的有效性.     

Robust adaptive neural tracking control for a class of electrically driven robots with time delays

This article addresses the problem of designing the robust tracking control for a class of uncertain electrically driven robots with time delays. The unknown time-delay uncertainty is assumed to be bounded by a function of all the state variables. By suitably choosing the Lyapunov–Krasovskii functionals, a novel adaptive/robust neural tracking control scheme is developed for the first time such that all the states and signals of the closed-loop time-delay robot system are bounded and the tracking error is shown to be uniformly ultimately bounded. By suitably designing the embedded current signal, the effect of time-delay uncertainty in the mechanical dynamics does not require to be incorporated into the current tracking error dynamics, and so the Lyapunov–Krasovskii functionals can be easily constructed in the stability analysis. Compared with the previous investigations of controlling robots the control scheme developed here can be extended to handle a broader class of electrically driven robots perturbed simultaneously by plant uncertainties, time-varying perturbations, and time-delay uncertainties. Finally, simulation examples are made to demonstrate the effectiveness of the proposed control algorithm.     

A multi-level control architecture for the bionic handling assistant

The bionic handling assistant is one of the largest soft continuum robots and very special in being a pneumatically operated platform that is able to bend, stretch, and grasp in all directions. It nevertheless shares many challenges with smaller continuum and other soft robots such as parallel actuation, complex movement dynamics, slow pneumatic actuation, non-stationary behavior, and a lack of analytic models. To master the control of this challenging robot, we argue for a tight integration of standard analytic tools, simulation, control, and state-of-the-art machine learning into an overall architecture that can serve as blueprint for control design also beyond the BHA. To this aim, we show how to integrate specific modes of operation and different levels of control in a synergistic manner, which is enabled by using modern paradigms of software architecture and middleware. We thereby achieve an architecture with unique overall control abilities for a soft continuum robot that allow for flexible experimentation toward compliant user-interaction, grasping, and online learning of internal models.     

基于行为的机器人编队控制研究

建立了一种基于行为的机器人编队控制结构模型,该结构采用分层控制策略,全局控制器根据当前所有机器人的状态从一个有限状态机中选择下一步机器人的行为,将协调控制量发送给各机器人,各机器人再通过局部控制器对自身进行控制。该结构模型简单、易行,并且适用于机器人不同任务的需要,具有很高的灵活性,而且易于仿真实现。在此基础上,将一类机器人模型进行反馈线性化,再根据编队控制的要求,利用后推方法设计控制律。仿真结果表明这种结构模型和控制算法是有效的。     

基于神经动力学的非完整移动机器人跟踪控制

主要研究了非完整移动机器人轨迹跟踪问题。基于后退控制和神经动力学生物激励模型,采用自适应参数调节的方法提出了一种新的跟踪控制器。该控制器能够生成平稳合理的速度,解决了以往大部分跟踪控制器所产生的速度突变问题,并且具有很好的鲁棒性。运用李雅普诺夫稳定性理论证明控制系统的稳定性。对连续、离散轨迹的仿真及与传统后退方法的比较分析验证了该方法的有效性。     

ABC2 an Agenda Based Multi-Agent Model for Robots Control and Cooperation

This paper presents a model for the control of autonomous robots that allows cooperation among them. The control structure is based on a general purpose multi-agent architecture using a hybrid approach made up by two levels. One level is composed of reactive skills capable of achieving simple actions by their own. The other one uses an agenda used as an opportunistic planning mechanism to compound, activate and coordinate the basic skills. This agenda handles actions both from the internal goals of the robot or from other robots. This two level approach allows the integration of real-time response of reactive systems needed for robot low-level behavior, with a classical high level planning component that permits a goal oriented behavior. The paper describes the architecture itself, and its use in three different domains, including real robots, as well as the issues arising from its adaptation to the RoboCup simulator domain.     

基于二阶运动学模型的移动机器人主从跟踪系统的鲁棒控制

In this paper, we study the problem of modeling and controlling leader-follower formation of mobile robots. First, a novel kinematics model for leader-follower robot formation is formulated based on the relative motion states between the robots and the local motion of the follower robot. Using this model, the relative centripetal and Coriolis accelerations between robots are computed directly by measuring the relative and local motion sensors, and utilized to linearize the nonlinear system equations. A formation controller, consisting of a feedback linearization part and a sliding mode compensator, is designed to stabilize the overall system including the internal dynamics. The control gains are determined by solving a robustness inequality and assumed to satisfy a cooperative protocol that guarantees the stability of the zero dynamics of the formation system. The proposed controller generates the commanded acceleration for the follower robot and makes the formation control system robust to the effect of unmeasured acceleration of the leader robot. Furthermore, a robust adaptive controller is developed to deal with parametric uncertainty in the system. Simulation and experimental results have demonstrated the effectiveness of the proposed control method.     

基于协进化机制的Multi-agent分布式智能控制体系

提出了一种基于协进化机制的多Agent分布式智能控制体系结构，采用划分基本行为模块的方法，将复杂、分布的控制系统构造成基于行为的、行为活性状态可调控的多Agent系统，并设计了基于协进化机制的分布式并行协进化学习机构及其协进化算法，使系统能分布并行地协进化各基本行为规则库和全局行为协调规则库。该体系结构能简单而有效地使系统的局部控制及全局行为协调协作具有较好的在线学习、自适应特性，系统可扩展性强，有较好的实用性，能应用于资源受限的嵌入式控制系统。     

轮式移动机器人瞬态模型鲁棒自适应同步终端滑模编队控制

在轮式移动机器人协同编队问题中,如何保证移动机器人在追踪自身期望轨迹的同时,又能实现与其他机器人运动同步的问题对控制算法的设计提出了更高的要求.本文提出一种基于图论的鲁棒自适应同步终端滑模控制算法来解决这一问题.首先介绍了轮式移动机器人非线性运动学瞬态模型,该模型避免了一般运动学模型多输入耦合互相干扰的问题.然后根据交叉耦合误差设计同步控制算法实现运动同步,通过鲁棒控制对系统外部干扰进行抑制,自适应律保证切换增益实时调节.运用Lyapunov方法进行了稳定性分析,证明了系统追踪误差的收敛性.最后通过MATLAB仿真验证了所设计算法的有效性.     

Output feedback adaptive RISE control for uncertain nonlinear systems

In this article, committed to extending the robust integral of the sign of the error (RISE) feedback control to the working condition of output feedback, a novel output feedback controller with a continuously bounded control input which combines the adaptive control and integral robust feedback will be proposed for trajectory tracking of a family of nonlinear systems subject to modeling uncertainties. A novel adaptive state observer (ASO) with disturbance rejection performance is creatively constructed to derive real-time estimation of the unmeasured state signals. Moreover, a projection-type adaption law is integrated to handle parameter uncertainties and an integral robust term is employed to deal with external disturbances. It is shown that asymptotic estimation performance and meanwhile asymptotic tracking result can eventually be derived. Simulation validations are implemented to demonstrate the high tracking performance of the presented controller. Notably, the synthesized control algorithm can be readily extended to the Euler–Lagrange systems. Typically, it can be extended to practical electromechanical equipment such as three-dimensional vector forming robots to improve the real-time forming accuracy.     

An SRWNN-based approach on developing a self-learning and self-evolving adaptive control system for motion platforms

In this paper, a self-recurrent wavelet neural network (SRWNN)-based indirect adaptive control architecture is modified for performing speed control of a motion platform. The transient behaviour of the original learning algorithm has been improved by modifying the learning rate updates. The contribution of the proposed modification has been verified via both simulations and experiments. Moreover, the performance of the proposed architecture is compared with robust RST designs performed on a similar benchmark system, to show that via adaptive nonlinear control, it is possible to obtain a fast step response without degrading the robustness of a multi-body mechanical system. Finally, the architecture is further improved so as to possess structural learning for populating the SRWNNs automatically, rather than employing static network structures, and simulation results are provided to show the performance of the proposed structural learning algorithm.     

Synchronization of bilateral teleoperators with time delay

Bilateral teleoperators, designed within the passivity framework using concepts of scattering and two-port network theory, provide robust stability against constant delay in the network and velocity tracking, but cannot guarantee position tracking in general. In this paper we fundamentally extend the passivity-based architecture to guarantee state synchronization of master/slave robots in free motion independent of the constant delay and without using the scattering transformation. We propose a novel adaptive coordination architecture which uses state feedback to define a new passive output for the master and slave robots containing both position and velocity information. A passive coordination control is then developed which uses the new outputs to state synchronize the master and slave robots in free motion. The proposed algorithm also guarantees ultimate boundedness of the master/slave trajectories on contact with a passive environment. Experimental results are also presented to verify the efficacy of the proposed algorithms.     

Repetitive control of electrically driven robot manipulators

This article presents a novel robust discrete repetitive control of electrically driven robot manipulators for tracking of a periodic trajectory. We propose a novel model, which presents the highly non-linear dynamics of robot manipulator in the form of linear discrete-time time-varying system. Based on the proposed model, we develop a two-term control law. The first term is an ordinary time-optimal and minimum-norm (TOMN) control by employing parametric controllers to guarantee stability. The second term is a novel robust control to improve the control performance in the face of uncertainties. The robust control estimates and compensates uncertainties including the parametric uncertainty, unmodelled dynamics and external disturbances. Performance of the proposed method is compared with two discrete methods, namely the TOMN control and an adaptive iterative learning (AIL) control. Simulation results confirm superiority of the proposed method in terms of the convergence speed and precision.     

Distributed control of simulated autonomous mobile robot collectives in payload transportation

A behavior-based control paradigm that allows a distributed collection of autonomous mobile robots to control the lifting and lowering processes of payload transportation is proposed and then tested with computer simulations. This control paradigm, which represents an approach to solving the cooperative load-bearing problem inherent in multi-agent payload transportation, is based upon a control structure we term thebehavior pathway controller. The behavior pathway controller emphasizes simple, feasible methodologies over complex, optimal methodologies, although we show that with some global self-organization of the collective, the feasible solutions approach and become optimal solutions. Using this controller in simulated environments, our robots demonstrate an ability to function with inaccurate sensor data, which is an important consideration for real world implementations of an autonomous mobile robot control paradigm. The simulated robots also demonstrate an ability to learn their place, or role, within the collective. They must learn their relative roles because they possess no predetermined knowledge about pallet mass, pallet inertia, collective size, or their positions relative to the pallet's center of gravity.