Dynamic Analysis and Distributed Control of the Tetrobot Modular Reconfigurable Robotic System

Reconfigurable robotic systems can be adapted to different tasks or environments by reorganizing their mechanical configurations. Such systems have many redundant degrees of freedom in order to meet the combined demands of strength, rigidity, workspace kinematics, reconfigurability, and fault tolerance. In order to implement these new generations of robotic system, new approaches must be considered for design, analysis, and control. This paper presents an efficient distributed computational scheme which computes the kinematics, dynamics, redundancy resolution, and control inputs for real-time application to the control of the Tetrobot modular reconfigurable robots. The entire system is decomposed into subsystems based on a modular approach and Newton's equations of motion are derived and implemented using a recursive propagation algorithm. Two different dynamic resolution of redundancy schemes, the centralized Jacobian method and the distributed virtual force method, are proposed to optimize the actuating forces. Finally, distributed dynamic control algorithms provide an efficient modular implementation of the control architecture for a large family of configurations.     

Adaptive containment control of heterogeneous high-order fully actuated multi-agent systems

In this article, the distributed containment control schemes are presented for a class of nonlinear heterogeneous high-order fully actuated multi-agent systems. First, for each follower, the reference trajectory generator is constructed by using the output information of the neighbor agents. Then, the distributed containment controllers are designed for the system with known nonlinear system function matrices and control gain matrices. Further, since the existence of modeling errors and other uncertain factors, the obtained system model is often not accurate enough, that is, the system function matrix and control gain matrix are actually unknown nonlinear matrices. To tackle these obstacles, we employ the radial basis function neural network method to approximate the unknown nonlinear system function matrices and skillfully construct the controllers combined with the adaptive technology to eliminate the effects of the unknown control gain matrices. The developed control strategies can guarantee that the containment control can be realized and the containment errors can be adjusted to arbitrary precision. In the end, the simulation results on the electromechanical system and robotic system demonstrate the availability and effectiveness of the provided schemes.     

Model continuity in the design of dynamic distributed real-time systems

Model continuity refers to the ability to transition as much as possible a model specification through the stages of a development process. In this paper, the authors show how a modeling and simulation environment, based on the discrete event system specification formalism, can support model continuity in the design of dynamic distributed real-time systems. In designing such systems, the authors restrict such continuity to the models that implement the system's real-time control and dynamic reconfiguration. The proposed methodology supports systematic modeling of dynamic systems and adopts simulation-based tests for distributed real-time software. Model continuity is emphasized during the entire process of software development $the control models of a dynamic distributed real-time system can be designed, analyzed, and tested by simulation methods, and then smoothly transitioned from simulation to distributed execution. A dynamic team formation distributed robotic system is presented as an example to show how model continuity methodology effectively manages the complexity of developing and testing the control software for this system.     

Adaptive control for robot manipulators using multiple parameter models

In this paper, we propose multiple parameter models based adaptive switching control system for robot manipulators. We first uniformly distribute the parameter set into a finite number of smaller compact subsets. Then, distributed candidate controllers are designed for each of these smaller compact subsets. Using Lyapunov inequality, a candidate controller is identified from the finite set of distributed candidate controllers that best estimates the plant at each instant of time. The design reduced the observer-controller gains by reducing modeling errors and uncertainties via identifying an appropriate control/model via choosing largest guaranteed decrease in the value of the Lyapunov function energy function. Compared with CE based CAC design, the proposed design requires smaller observer-controller gains to ensure stability and tracking performance in the presence of large-scale modeling errors and disturbance uncertainties. In contrast with existing adaptive method, multiple model based distributed hybrid design can be used to reduce the energy consumption of the industrial robotic manipulator for large scale industrial automation by reducing actuator input energy. Finally, the proposed hybrid adaptive control design is experimentally tested on a 3-DOF Phantom^TM robot manipulator to demonstrate the theoretical development for real-time applications.

     

Agent virtual machine for automation controllers: Example application: Shipboard automation

We are interested in providing an agent infrastructure for truly distributed control. Requirements include multiple language implementations, so that this agent host environment can exist in both real-time controllers and contain the ability to operate with external agent systems. Our first infrastructure, the Autonomous Cooperative System (ACS), accomplished these goals, but presented other issues. Although ACS has been successfully deployed on commercial controllers, new releases of controllers will cause complications in the evolution of the software and adoption of future functionality. We need to establish a formal integration procedure of the agent functionality with the controllers and to transform the underlying system into a fully integrated and open heterogeneous system. We use a shipboard automation example to demonstrate the use of the agent virtual machine. With this system, the shipboard automation system is designed as a distributed and survivable environment, thereby spreading intelligence throughout the controllers and not in central locations.     

Agent virtual machine for automation controllers: Example application: Shipboard automation

We are interested in providing an agent infrastructure for truly distributed control. Requirements include multiple language implementations, so that this agent host environment can exist in both real-time controllers and contain the ability to operate with external agent systems. Our first infrastructure, the Autonomous Cooperative System (ACS), accomplished these goals, but presented other issues. Although ACS has been successfully deployed on commercial controllers, new releases of controllers will cause complications in the evolution of the software and adoption of future functionality. We need to establish a formal integration procedure of the agent functionality with the controllers and to transform the underlying system into a fully integrated and open heterogeneous system. We use a shipboard automation example to demonstrate the use of the agent virtual machine. With this system, the shipboard automation system is designed as a distributed and survivable environment, thereby spreading intelligence throughout the controllers and not in central locations.     

Hardware-software codesign of embedded systems

Designers generally implement embedded controllers for reactive real-time applications as mixed software-hardware systems. In our formal methodology for specifying, modeling, automatically synthesizing, and verifying such systems, design takes place within a unified framework that prejudices neither hardware nor software implementation. After interactive partitioning, this approach automatically synthesizes the entire design, including hardware-software interfaces. Maintaining a finite-state machine model throughout, it preserves the formal properties of the design. It also allows verification of both specification and implementation, as well as the use of specification refinement through formal verification     

A Hybrid Control Approach for Non-invasive Medical Robotic Systems

In this paper, a hybrid supervisory control approach adopted for a non-invasive medical robot called Focused Ultrasound Surgical Robot—Breast Surgery (FUSBOT-BS) is elaborated. The system was built for the use in the breast surgery with high intensity focused ultrasound (HIFU) as the means of the treatment. A number of different control strategies such as PID and model-based control were incorporated into a family of controllers to create the hybrid control. Depending on the objective, the supervisory control determines the type of controller used for the specified task so as to maximize the advantages of each of the controllers. Before it was implemented into the actual robotic system the then proposed control approach was modeled and simulated using Matlab®. This control approach was developed based on a review of popular control approaches used in medical robotic systems, in order to look at the feasibility of having a uniform control strategy for a spectrum of medical robotic system. With unified control strategy it is possible to have a safety standard regulation for the medical robotic systems which is currently difficult to be done because of various control strategies adopted by each of the medical robotic systems.     

Distributed Reinforcement Learning Control for Batch Sequencing and Sizing in Just-In-Time Manufacturing Systems

This paper presents an approach that is suitable for Just-In-Time (JIT) production for multi-objective scheduling problem in dynamically changing shop floor environment. The proposed distributed learning and control (DLC) approach integrates part-driven distributed arrival time control (DATC) and machine-driven distributed reinforcement learning based control. With DATC, part controllers adjust their associated parts' arrival time to minimize due-date deviation. Within the restricted pattern of arrivals, machine controllers are concurrently searching for optimal dispatching policies. The machine control problem is modeled as Semi Markov Decision Process (SMDP) and solved using Q-learning. The DLC algorithms are evaluated using simulation for two types of manufacturing systems: family scheduling and dynamic batch sizing. Results show that DLC algorithms achieve significant performance improvement over usual dispatching rules in complex real-time shop floor control problems for JIT production.     

Integrated Environment for Modelling, Simulation and Control Design for Robotic Manipulators

In this paper an integrated environment for the design of robotic controllers implemented on a PC is described. It is based on the Planar Manipulators Toolbox for dynamic simulation of redundant planar manipulators. The tools are fully integrated in the MATLAB/SIMULINK and hence, a lot of standard tools are available for the analysis and control design. Using the real-time simulation it is possible to apply the developed controllers to a real robot manipulator, which can be included in the system via corresponding interfaces, without any additional coding. The main advantage is the flexibility in fast prototyping of different algorithms in the field of control of robotic systems, especially for redundant manipulators.     

The application of transputers to distributed control

This paper describes the development of a flexible control system based on multiple T800 floating point transputers and is presented in two parts. In part 1 of the paper an overview is presented of distributed command and control systems (DCCS) and the suitability of the transputer for implementation within such systems discussed. It is concluded in this initial investigation that the transputer is ideally suited for use in such systems as it is not only a very powerful processing device but the addition of serial communication links allows individual control modules to easily communicate with other controllers, hosts and external devices. In part 2 of the article, entitled 'A parallel architecture for distributed adaptive control', a transputer-based adaptive control module which has been developed is described. The integration of this flexible controller into a distributed control system is also discussed.     

Dynamics modeling of robotic manipulators using an artificial neural network

Dynamics modeling is important for the design, analysis, simulation, and control of robotic and other computer-controlled mechanical systems. The complete dynamic modeling of such systems involves the computationally intensive solution of a set of non-linear, coupled differential equations. Artificial neural networks are well suited for this application due to their ability to represent complex functions and, potentially, to operate in real time. The application of an artificial neural network to dynamics modeling of robotic systems is investigated. The Cerebellar Model Arithmetic Computer (CMAC) is employed. A hybrid implementation of CMAC is proposed to allow use of the model for either simulation or control of robotic manipulators. The success of the simulated results and the accuracy of the generated outputs after a few training cycles demonstrate great promise for further development of the method and its implementation in control systems. © 1994 John Wiley & Sons, Inc.     

A Constraint-Based Robotic Soccer Team

It is a challenging task for a team of multiple fast-moving robots to cooperate with each other and to compete with another team in a dynamic, real-time environment. For a robot team to play soccer successfully, various technologies have to be incorporated including robotic architecture, multi-agent collaboration and real-time reasoning. A robot is an integrated system, with a controller embedded in its plant. A robotic system is the coupling of a robot to its environment. Robotic systems are, in general, hybrid dynamic systems, consisting of continuous, discrete and event-driven components. Constraint Nets (CN) provide a semantic model for modeling hybrid dynamic systems. Controllers are embedded constraint solvers that solve constraints in real-time. A controller for our robot soccer team, UBC Dynamo98, has been modeled in CN, and implemented in Java, using the Java Beans architecture. A coach program using an evolutionary algorithm has also been designed and implemented to adjust the weights of the constraints and other parameters in the controller. The results demonstrate that the formal CN approach is a practical tool for designing and implementing controllers for robots in multi-agent real-time environments. They also demonstrate the effectiveness of applying the evolutionary algorithm to the CN-modeled controllers.     

空间关联系统的分布式输出反馈控制器设计

讨论了一类由若干个连续时间线性系统组成的强耦合的空间关联系统的分布式控制器设计问题. 该类系统在实际中有广泛应用, 如高速路车辆控制系统, 电网系统及计算机网络系统. 提出了混合Lyapunov判据和混合有界实引理对该类大系统的稳定性和H无穷性能进行了分析. 给出了基于LMI的分布式动态输出反馈控制器设计方法, 从而保证了闭环大系统的稳定性和H无穷性能. 在控制器的求解过程中, 引入了更为高效和计算可靠性更高的变量替换法进行求解, 试验结果表明, 一些通过消元法无解的问题借助变量替换法可以得到很好的解决. 最后, 通过具体实例说明了分布式控制系统在动态性能指标上优于分散控制系统.     

基于ARM Linux平台的CAN设备驱动程序设计与实现

针对移动机器人控制系统在实时性、可靠性方面的要求,提出了基于CAN总线的分布式控制系统方案,介绍了在核心数据处理单元S3C2410上扩展CAN总线设备的硬件接口设计,详细讨论了嵌入式ARM Linux上驱动程序开发的一般方法,以及CAN设备驱动程序的设计方法和具体实现。经实验证明,此系统结构简单,稳定性和可靠性强。     

基于自控网系统的混杂控制

混杂控制系统通常是复杂的非线性控制系统,难以用统一的模型进行建模. David和Alla提出的混杂Petri网,虽然可以解决一般混杂系统的建模问题,并得到了广泛的应用,但对于传统的如PID这类控制器,缺乏统一建模的能力.探讨了基于广义自控网系统的混杂控制方法,实现了对混杂控制器中监控器和数字控制器进行统一的Petri网建模.仿真实例设计了基于广义同步自控网系统的电加热炉控制系统,给出了可根据不同温度状态实施多种控制策略的变结构数字控制器模型,并详细分析了控制器的性能,证明了广义自控网系统具有十分强大的建模能力和广泛的应用前景.     

Implementing spiking neural networks for real-time signal-processing and control applications: a model-validated FPGA approach.

In this paper, we present two versions of a hardware processing architecture for modeling large networks of leaky-integrate-and-fire (LIF) neurons; the second version provides performance enhancing features relative to the first. Both versions of the architecture use fixed-point arithmetic and have been implemented using a single field-programmable gate array (FPGA). They have successfully simulated networks of over 1000 neurons configured using biologically plausible models of mammalian neural systems. The neuroprocessor has been designed to be employed primarily for use on mobile robotic vehicles, allowing bio-inspired neural processing models to be integrated directly into real-world control environments. When a neuroprocessor has been designed to act as part of the closed-loop system of a feedback controller, it is imperative to maintain strict real-time performance at all times, in order to maintain integrity of the control system. This resulted in the reevaluation of some of the architectural features of existing hardware for biologically plausible neural networks (NNs). In addition, we describe a development system for rapidly porting an underlying model (based on floating-point arithmetic) to the fixed-point representation of the FPGA-based neuroprocessor, thereby allowing validation of the hardware architecture. The developmental system environment facilitates the cooperation of computational neuroscientists and engineers working on embodied (robotic) systems with neural controllers, as demonstrated by our own experience on the Whiskerbot project, in which we developed models of the rodent whisker sensory system.     

Implementation of a human-friendly systems methodology for intelligent control system modelling and simulation

We have been developing an interactive computer software for the systematic support to modeling and simulation of intelligent control systems, based on a human-friendy systems methodology. The support system has a universal application in data analysis, system structuring, statistical and fuzzy modeling, and simulation, with the aid of human-computer interfaces to acquire knowledge or judgment of the domain experts. This paper presents our soft systems methodology and its implementation into the computer to develop intelligent process control systems. New technical proposals include a modeling method of fuzzy implication inference models and a design method of model predictive controllers.     

: A distributed real-time logic language

This paper presents a new language that integrates the real-time and distributed paradigms within the framework of a concurrent logic language. Concurrent logic languages (CLLs) are capable of expressing concurrence, communication and nondeterminism in a natural way. That is, the intrinsic parallel semantics of the concurrent logic languages makes them well-suited for distributed programming. The proposed language is particularly suitable for loosely coupled systems and it contains mechanisms for distributed and real-time process control. A new execution model for concurrent logic languages is presented, which enables efficient distributed execution and real-time control. The model is introduced by giving an operational semantics for the language and the new model's implementation is discussed, including the definition of a new abstract machine and its implementation on a network of Unix workstations. Although the sequential core is not optimized, some previous results are discussed, showing the feasibility of the language's execution model for distributed real-time systems. The language is currently being used as the kernel language for a distributed simulation and validation tool for communication protocols.     

: A distributed real-time logic language

This paper presents a new language that integrates the real-time and distributed paradigms within the framework of a concurrent logic language. Concurrent logic languages (CLLs) are capable of expressing concurrence, communication and nondeterminism in a natural way. That is, the intrinsic parallel semantics of the concurrent logic languages makes them well-suited for distributed programming. The proposed language is particularly suitable for loosely coupled systems and it contains mechanisms for distributed and real-time process control. A new execution model for concurrent logic languages is presented, which enables efficient distributed execution and real-time control. The model is introduced by giving an operational semantics for the language and the new model's implementation is discussed, including the definition of a new abstract machine and its implementation on a network of Unix workstations. Although the sequential core is not optimized, some previous results are discussed, showing the feasibility of the language's execution model for distributed real-time systems. The language is currently being used as the kernel language for a distributed simulation and validation tool for communication protocols.