Reduced-order models and controllers for continuous-time stochasticsystems: an information theory approach

Reduced-order models and controllers for continuous-time stochastic systems are described. The reduced-order models are chosen to minimize the Kullback-Leibler information distance (KLID) between the outputs of the actual and reduced systems. An LQG controller based on a reduced-order system model is described. A second reduced-order controller is found to minimize the KLID between the closed-loop system outputs with the full and reduced-order controllers     

Robust control techniques for general dynamic systems

Lyapunov techniques are used to design robust controllers for nonlinear systems. The objective is to use the system structure to simplify the controller as far as possible. A general robust control scheme is developed that applies to systems described by a class of second-order nonlinear equations. Applications to a mobile robot and a chemical stirred tank reactor are given.     

Input–output identification of controlled discrete manufacturing systems

The automated construction of discrete event models from observations of external system's behaviour is addressed. This problem, often referred to as system identification, allows obtaining models of ill-known (or even unknown) systems. In this article, an identification method for discrete event systems (DESs) controlled by a programmable logic controller is presented. The method allows processing a large quantity of observed long sequences of input/output signals generated by the controller and yields an interpreted Petri net model describing the closed-loop behaviour of the automated DESs. The proposed technique allows the identification of actual complex systems because it is sufficiently efficient and well adapted to cope with both the technological characteristics of industrial controllers and data collection requirements. Based on polynomial-time algorithms, the method is implemented as an efficient software tool which constructs and draws the model automatically; an overview of this tool is given through a case study dealing with an automated manufacturing system.     

A methodology for designing controllers for industrial systems based on nonlinear separation model and control

A feasible method of controller design for industrial nonlinear dynamic systems is proposed, by separating the nonlinear dynamic system into nonlinear static parts and a linear dynamic part. The proposed method of industrial controller design reduces the existing gaps between the control theory and the actual field. In the controller construction procedure of a system, the nonlinearities are eliminated by using the inversion functions of the nonlinear static parts. Any conventional control theory is ideally applicable to a linear dynamic part of the system. Based on the proposed method of nonlinear separation, controllers were constructed for three different systems: a chemical reactor, a temperature level-controlled tanked water system, and the contour-following control of an articulated robot arm. Some encouraging control performances, superior to those of other existing controllers, showed its significant potential for application to industrial systems with nonlinearities.     

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.     

Adaptive controller designs for robot manipulator systems using Lyapunov direct method

Two different controllers for robot manipulator systems based on the general adaptive control theory using Lyapunov direct method are designed. A way of improving the transient response and convergence speed of a multivariable system in conjunction with this controller is established.     

Evolving reliable and robust controllers for real robots by genetic programming

 Using Genetic Programming (GP)-based approaches to evolve robot controllers has the advantage of operating variable-size genotype. This is an important feature for evolving robot control systems as it allows complete freedom for the control architecture in respect to the task complexity which is difficult to predict. However, GP-based work in evolving controllers has been questioned in the verification of the performance on real robots, the generalisation of defining primitives, and the computational cost needed. In this paper, we present our GP framework in which a special representation of the robot controller is designed; this representation can capture well the characteristic of a behaviour controller so that our system can efficiently evolve desired robot behaviours by a relatively low computational cost. This system has been successfully used to evolve reliable and robust controllers working on a real robot, for a variety of tasks.     

Stand-alone programmable controller for time-critical robotic systems

This paper proposes and describes a new technique in the design and implementation of stand-alone programmable controllers which implement complex control algorithms for complex systems in general and time-critical systems in particular. Unlike other existing commercially available controllers, the proposed controller is capable of implementing all kinds of simple and complex control algorithm techniques such as PIDs, adaptive, optimal, etc. The controller is designed to handle timed events accurately due to the utilization of a new software approach based on Petri net technique as well as the use of an optimized simulation language designed for control and data acquisition applications. The most significant aspects of the proposed controller are its low cost, high speed, and easy implementation. Using the new approach, it is possible to effortlessly and efficiently simulate any controller algorithm and controlled plant, verify results if they meet pre-defined system specifications and then immediately generate and save codes on EPROMs to be placed on the controller board. The proposed controller system consists of a hardware portion and a software package. The software package is written in C and assembly languages and consists of four different programs. A prototype of the proposed controller was designed, constructed and successfully tested to implement various control algorithms. The data obtained suggest that the proposed technique will significantly aid engineers to simplify the task of implementing complex algorithms industry such as robotics.     

Developing control and integration software for flexible manufacturing systems

The slow growth of computer-integrated manufacturing is attributed to the complexity of designing and implementing their control and integration software. This article expands on a methodology for designing and implementing this software that was introduced in [16]. The goal of this methodology is to build flexible and resuable control and integration software for computer-integrated manufacturing systems. It hinges upon the concepts of software/hardware components, their assemblages, a distributed common language environment, formal models, and generic controllers. Major sources of flexibility are obtained by decoupling process plan models from the model of the factory floor and by using a generic controller. Reusability is achieved by building selfcontained software/hardware components with general, possibly parametrized, interfaces. The interplay between simulated and actual hardware internals of software/hardware components is used as the basis of a testing strategy that performs off-line simulation followed by on-line testing.The methodology has been applied in designing and implementing the control and integration software of an actual Prismatic Machining Cell. The article also reports on the details of this implementation.The names of the authors appear in alphabetical order.     

Development of an Intelligent Wheelchair Using Computer Graphics Animation and Simulation

A new robot simulator JC-1 is used as a control software development tool in a project in progress where an intelligent wheelchair for a blind user is being developed. The intelligent wheelchair is planned to be able to fulfill simple symbolic commands like "follow wall" or "follow object" and using the JC-1 simulator an evaluation team which includes e.g. the user, a rehabilitation engineer and a software engineer, can check control algorithms and user interface routines before constructing a real wheelchair prototype. The JC-1 simulator models the environment using simplified boundary- representation where objects, robot sensors and actuators are presented as symbolic objects in the graphics data-base of the simulator. In the JC-1 simulator a robot controller under development controls the motion of the graphical model of the robot while simulator commands or other robot controllers can be used to control the movement of disturbing obstacles. Computer graphics animation and simulation help to find fundamental design errors at an early design stage and as this paper suggests, enable the user of the final product to take part in to the designing process of the robot controller. Benefits and difficulties of using computer graphics simulation in the wheelchair development process are discussed.     

Design and construction of a variable-aperture gripper for flexible automated assembly

The growing market demand for a wide variety of product models and small batch production makes flexible robotized production systems an emerging need in industry. Today, in manufacturing applications, general purpose grippers are not very considered, and robot end effectors are properly designed for the specific task with a strongly limited versatility. Flexibility is thus usually obtained by using a different tool for each family of parts: a tool changing system allows the robot to rapidly replace the tool on the end effector; tools are stored in a tool magazine allocated in the workcell. However, such systems are expensive and their use can affect the working cycle-time. This paper presents the design and testing of a variable-aperture, cost-effective gripper, capable of adapting its aperture (grasp width) to different handling demands, without affecting the working-cycle time of the production system. The solution proposed consists of (1) an electrically-actuated mechanism, which allows it to satisfy flexibility requirements, by regulating the aperture in hidden time; (2) a pneumatically-actuated mechanism to achieve high performance in open/close operations. Simulations and preliminary tests showed that this type of design can be a suitable solution to increase flexibility in robotized workcells without increasing the cycle time.     

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.     

Design and experimental evaluation of robust controllers for a two-wheeled robot

The paper presents the design and experimental evaluation of two alternative μ-controllers for robust vertical stabilisation of a two-wheeled self-balancing robot. The controllers design is based on models derived by identification from closed-loop experimental data. In the first design, a signal-based uncertainty representation obtained directly from the identification procedure is used, which leads to a controller of order 29. In the second design the signal uncertainty is approximated by an input multiplicative uncertainty, which leads to a controller of order 50, subsequently reduced to 30. The performance of the two μ-controllers is compared with the performance of a conventional linear quadratic controller with 17th-order Kalman filter. A proportional-integral controller of the rotational motion around the vertical axis is implemented as well. The control code is generated using Simulink® controller models and is embedded in a digital signal processor. Results from the simulation of the closed-loop system as well as experimental results obtained during the real-time implementation of the designed controllers are given. The theoretical investigation and experimental results confirm that the closed-loop system achieves robust performance in respect to the uncertainties related to the identified robot model.     

Symbolic control of linear systems based on symbolic subsystems

This paper describes an approach to the control of continuous systems through the use of symbolic models describing the system behavior only at a finite number of points in the state space. These symbolic models can be seen as abstract representations of the continuous dynamics enabling the use of algorithmic controller design methods. We identify a class of linear control systems for which the loss of information incurred by working with symbolic subsystems can be compensated by feedback. We also show how to transform symbolic controllers designed for a symbolic subsystem into controllers for the original system. The resulting controllers combine symbolic controller dynamics with continuous feedback control laws and can thus be seen as hybrid systems. Furthermore, if the symbolic controller already accounts for software/hardware requirements, the hybrid controller is guaranteed to enforce the desired specifications by construction thereby reducing the need for formal verification.     

Contextual Policy Search for Linear and Nonlinear Generalization of a Humanoid Walking Controller

We investigate learning of flexible robot locomotion controllers, i.e., the controllers should be applicable for multiple contexts, for example different walking speeds, various slopes of the terrain or other physical properties of the robot. In our experiments, contexts are desired walking linear speed of the gait. Current approaches for learning control parameters of biped locomotion controllers are typically only applicable for a single context. They can be used for a particular context, for example to learn a gait with highest speed, lowest energy consumption or a combination of both. The question of our research is, how can we obtain a flexible walking controller that controls the robot (near) optimally for many different contexts? We achieve the desired flexibility of the controller by applying the recently developed contextual relative entropy policy search(REPS) method which generalizes the robot walking controller for different contexts, where a context is described by a real valued vector. In this paper we also extend the contextual REPS algorithm to learn a non-linear policy instead of a linear policy over the contexts which call it RBF-REPS as it uses Radial Basis Functions. In order to validate our method, we perform three simulation experiments including a walking experiment using a simulated NAO humanoid robot. The robot learns a policy to choose the controller parameters for a continuous set of forward walking speeds.     

一类2-D不确定离散系统的弹性保成本控制

当被控系统的数学模型存在不确定性时,需要设计鲁棒控制器才能使得受控系统稳定,然而,如果控制器本身也存在不确定性时,系统就会变得复杂难以控制,使用传统的鲁棒控制方法很难达到期望的控制目标,甚至不能保证受控系统的稳定性.本研究就是针对当系统模型和控制器同时存在不确定性时,给出了设计稳定控制器的简便方法.通过将控制器的不确定性分别表示为加法式和乘法式摄动,研究了以上两种系统的弹性保成本控制问题,并给出了相应控制器的设计方法.在主要结果推导过程中,巧妙运用了各种矩阵不等式放缩和等价参数变换等数学方法,最终将主要结果表示为线性矩阵不等式(LMI),利用Matlab的LMI工具箱,可以很方便地设计所需要的控制器.最后,对同一个受控系统,分别施加利用本文结果和已有结果设计的控制器,发现前者可以很好地控制系统,而后者却不能使受控系统稳定,从而验证了所得结果的有效性.     

An approximate-predictor approach to reduced-order models andcontrollers for distributed-parameter systems

Two reduced-order digital controllers for distributed parameter systems (DPS) are described. Reduced-order models approximate the optimal finite past predictor and error covariance for the full system to minimize an approximation to the Kullback-Leibler information distance (KLID). An LQG controller based on a reduced-order-system model is described. A reduced-order controller is found to minimize the KLID between the closed-loop system outputs with the full- and reduced-order controllers. Noncollocated control of a flexible beam is simulated     

A Neuro-interface with fuzzy compensator for controlling nonholonomic mobile robots

This paper describes a control method for mobile robots represented by a nonlinear dynamical system, which is subjected to an output deviation caused by drastically changed disturbances. We here propose some controllers in the framework of neuro-interface. It is assumed that a neural network (NN)-based feedforward controller is construcetd by following the concept of virtual master-slave robot, in which a virtual master robot as a feedforward controller is used to control the slave (i.e., actual) robot. The whole system of the present neuro-interface consists of an NN-based feedforward controller, a feedback PD controller and an adaptive fuzzy feedback compensator. The NN-based feedforward controller is trained offline by using a gradient method, the gains of the PD controller are to be chosen constant, and the adaptive fuzzy compensator is constructed with a simplified fuzzy reasoning. Some simulations are presented to confirm the validity of the present approach, where a nonholonomic mobile robot with two independent driving wheels is assmued to have a disturbance due to the change of mass for the robot.     

Development of membrane controllers for mobile robots

The main contribution of this paper is the introduction of the new concept of membrane controller based on the structure and functioning of a deterministic numerical P system. The procedure for developing a membrane controller and for using it to control a mobile robot is explained and several test cases are given in which membrane controllers are used to control both simulated and real mobile robots and to generate various desired behaviours (obstacle avoidance, wall following, and follow the leader). The experiments reported in this paper validate the concept and prove that the performance of a membrane controller is comparable to or better than that of other controllers (such as fuzzy logic controllers).