Simulation Technique of Velocity-Based Discrete-time Control System with Intelligent Control Concepts for Open Architectural Industrial Robots

Industrial robots have drastically rationalized many kinds of manufacturing processes in industrial fields. For this decade, open architectural industrial robots have been produced from several industrial robot makers such as KAWASAKI Heavy Industries, Ltd., MITSUBISHI Heavy Industries, Ltd. and YASKAWA Electric Corp. and so on. Open architecture described in this article means that the servo system and kinematics of the robot are technically opened, so that various applications required in industrial fields are allowed to be planned and developed at the user side. For example, non-taught operation by using a CAD/CAM system can be considered due to the opened accurate kinematics. Also, force control strategy using a force sensor can be implemented due to the opened servo system. In this article, a simulation technique of velocity-based discrete-time control system for open architectural industrial robots is introduced by giving examples of intelligent control methods. In order to develop a novel velocity-based discrete-time control system for an open architectural industrial robot, it is required from the points of view concerning safety, cost and easiness to preliminarily examine and evaluate the characteristics and performance. In such a case, the proposed simulation technique is useful. INTRODUCTION Industrial robots have drastically rationalized many kinds of manufacturing processes in industrial fields. The user interface provided by the robot maker has been almost limited to so-called the teaching pendant. The teaching pendant is a useful and safe tool to obtain positions and orientations at the tip of a robot along a desired trajectory, but the teaching task is very complicated and time-consuming task. Especially, when the target trajectory is a free curved line, many through points must be given to acquire a smooth trajectory; the task is further not easy. For this decade, open architectural industrial robots as shown in Fig. (1) have been produced from several industrial robot makers such as KAWASAKI Heavy Industries, Ltd., MITSUBISHI Heavy Industries, Ltd. and YASKAWA Electric Corp. and so on. Open architecture described in this article means that the servo system and kinematics of the robot are technically opened, so that various applications required in industrial fields are allowed to be planned and developed at the user side. For example, non-taught operation by using a CAD/CAM system can be considered due to the opened accurate kinematics. Also, force control strategy using a force sensor can be easily implemented due to the technically opened discrete-time servo system. It is now possible to model and simulate many types of robots. For example, Chen et al. presented a new design of an environment for simulation, animation, and viAddress corresponding to this author at the Department of Electronics and Computer Science, Tokyo University of Science, Yamaguchi; E-mail: [email protected] sualization of sensor-driven robots. Although conventional computer-graphics-based robot simulation and animation software packages lacked of capabilities for robot sensing simulation, the system was designed to overcome the deficiency [1]. Also, Benimeli et al. addressed the implementation and comparison of an indirect and a direct identification procedures on an industrial robot provided with an open control architecture. The estimation of dynamic parameters in mechanical systems constituted an issue of crucial importance for dynamic simulation applications where high accuracy was required [2]. In this article, we present a simulation technique of velocity-based discrete-time control system for open architectural industrial robots by giving and combining examples of intelligent control concepts such as genetic algorithms, fuzzy control and neural network. In order to develop a novel velocity-based control system represented in discretetime domain for an open architectural industrial robot, it is required from the points of view concerning safety, cost and easiness to preliminarily examine and evaluate the characteristics and performance. In such a case, the proposed simulation techniques will be useful. The validation and promise are evaluated through simulations by using a dynamic model of PUMA560 manipulator as shown in Fig. (2) [3], [4]. BASIC SERVO SYSTEM In order to simulate an industrial robot, first of all, a servo system is considered and designed. Here, the resolved acceleration controller [5] is picked up in a servo system. The resolved acceleration control method or computed torque control method is used for nonlinear control of industrial 1874-4443/08 2008 Bentham Open 32 The Open Automation and Control Systems Journal, 2008, Volume 1 Fusaomi Nagata KAWASAKI JS10 YASKAWA UP6 MITSUBISHI PA10 Fig. (1). Open architectural industrial robots. manipulators, which is composed of a model base portion and a servo portion. The servo portion is a close loop with respect to the position and velocity. On the other hand, the model base portion has the inertia term, gravity term and centrifugal/Coriolis term, which work for canceling the nonlinearity of manipulator. In order to realize high control stability, the position and velocity feedback gains used in the servo portion should be selected suitably. In this section, a simple but effective fine tuning method after manual tuning process is introduced for the position and velocity feedback gains in the servo portion. At the first step, search space for the gains is roughly narrowed down by a controller designer, e.g. considering the critically damped condition. At the second step, the gains are finely tuned by using genetic algorithms. Genetic algorithms search for a better combination of the position and velocity feedback gains. Resolved Acceleration Control The dynamic model of a manipulator without friction term is generally given by M(„)„̈ +H(„, „̇) +G(„) = fi (1) where, M(„) ∈ R6×6 is the inertia term in joint space. H(„, „̇) ∈ R6×1 and G(„) ∈ R6×1 are the Coriolis/centrifugal term and gravity term in joint space, respectively. „ ∈ R6×1, „̇ ∈ R6×1 and „̈ ∈ R6×1 are the position, velocity and acceleration vectors in joint coordinate system, respectively. fi ∈ R6×1 is the joint driving torque vector. In the case that the resolved acceleration control law is employed in the servo system of a manipulator, desired position, velocity and acceleration vectors in Cartesian coordinate system are respectively given to the references of the servo system, so that the joint driving torque is calculated from fi = M̂(„)J(„)× [ ẍr +Kv{ẋr − ẋ}+Kp{xr − x} − J̇(„)„̇ ] +Ĥ(„, „̇) + Ĝ(„) (2) where,ˆdenotes the modeled term. x ∈ R6×1, ẋ ∈ R6×1 and ẍ ∈ R6×1 are the position/orientation, velocity and acceleration vectors in Cartesian coordinate system, respectively. xr, Fig. (2). PUMA560 manipulator.