• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 1996.04a
• /
• pp.419-423
• /
• 1996

These days, industrial robots are required to have high speed and high precision in doing various tasks. Recently, the adaptive control algorithms for those nonlinear robots have been developed. With spatial vector space, these adaptive algorithms including recursive implementation are simply described. Without sensing joint acceleration and computing the inversion of inertia matrix, these algorithms which include P.D. terms and feedforward terms have global tracking convergence. In this paper, the feasibility of the proposed control method is illustrated by applying to 2 DOF SCARA robot in DSP(Digital Signal Processing).

• Proceedings of the Korean Society of Machine Tool Engineers Conference
• /
• 1999.10a
• /
• pp.216-220
• /
• 1999

In this paper, the feedback linearization technique is used with the sliding mode control for nonlinear system. The combination of these two control techniques can be achieved by proposing a novel sliding surface which has the nonminal dynamics of the original system controlled by feedback linearization technique. The noble design of the sliding surface is based on the augmented system whose dynamics have a higher order than that of the original system. The reaching phase is removed by using an initial virtual state which makes the initial sliding function equal to zero

• Transactions of the Korean Society of Machine Tool Engineers
• /
• v.10 no.4
• /
• pp.40-47
• /
• 2001

In this pater, the QLQG/LTR control method is applied for the position control of the nonlinear timing belt driving sys-tem. Parameters fo the plant are identified by genetic algorithm and nonlinear elements, such as Coulomb friction and dead-zone, and quasi-linearized by RIDE method. Comparing with the LQG/LTR contro. the QLQG/LTR has similar structures of the LQG/LTR, but this method can consider nonlinear effects in designing the controller. Thus, the QLQG/LTR control system is robust to hard nonlinearities such as Coulomb friction, dead-zone, etc. Forma given hard non-linear system through experiments, it is shown that the tracking performance of the QLQG/LTR control system can be very improved that the LQF/LTR control system.

• 제어로봇시스템학회:학술대회논문집
• /
• 1992.10a
• /
• pp.927-930
• /
• 1992

VSS identification approach is based on following concept, i.e. while in sliding motion, the switching of control inputs refects system uncertainites. Therefore, if there exist some operations that make the information form the switiching control inputs be achievable, then the unknown parameters can be actually identification mechanisms which can fully make use of the available information. Two different types of VSS identifiers are taken into consideration. The first type uses adjustable model whose structure is similar to that of identified systems. From the viewpoint of contro, this type of VSS identifiers may be regraded as direct identifier vecause the identified system is handled as an open loop. On the other hand, if the identified system is controlable in the sense of VSS(sliding mode can be generated through chosing control inputs), the second type of VSS identifier, the indirect VSS identifier, can be constructed according to the linerized system strucutre while staying in sliding mode. Therefroe it can be applied to some nonlinear systems which are not linear in parametric space by general identification algorithms, whereas linear in parametric space when sliding mode is existed.

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.50 no.1
• /
• pp.38-44
• /
• 2001

This paper investigates the direct adaptive control of nonlinear systems using RBFN(radial basis function networks). The structure of the controller consists of a fixed PD controller and a RBFN controller in parallel. An adaptation law for the parameters of RBFN is developed based on the Lyapunov stability theory to guarantee the stability of the overall control system. The filtered tracking error between the system output and the desired output is shown to be UUB(uniformly ultimately bounded). To evaluate the performance of the controller, the proposed method is applied to the trajectory contro of the two-link manipulator.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.5 no.1
• /
• pp.52-64
• /
• 1995

Robot manipulator is a highly nonlinear-time varying system. Therefore, a lot of control theory has been applied to the system. Robot manipulator has two types of control; one is path planning, another is path tracking. In this paper, we select the path tracking, and for this purpose, propose the intelligent control¬ler which is combined with fuzzy logic and neural network. The fuzzy logic provides an inference morphorlogy that enables approximate human reasoning to apply to knowledge-based systems, and also provides a mathematical strength to capture the uncertainties associated with human cognitive processes like thinking and reasoning. Based on this fuzzy logic, the fuzzy logic controller(FLC) provides a means of converhng a linguistic control strategy based on expert knowledge into automahc control strategy. But the construction of rule-base for a nonlinear hme-varying system such as robot, becomes much more com¬plicated because of model uncertainty and parameter variations. To cope with these problems, a auto-tuning method of the fuzzy rule-base is required. In this paper, the GA-based Fuzzy-Neural control system combining Fuzzy-Neural control theory with the genetic algorithm(GA), that is known to be very effective in the optimization problem, will be proposed. The effectiveness of the proposed control system will be demonstrated by computer simulations using a two degree of freedom robot manipulator.