• International Journal of Fuzzy Logic and Intelligent Systems
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• v.7 no.2
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• pp.96-101
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• 2007

Neural networks are known as kinds of intelligent strategies since they have learning capability. There are various their applications from intelligent control fields; however, their applications have limits from the point that the stability of the intelligent control systems is not usually guaranteed. In this paper we propose an adaptive tracking control for robot manipulators using the radial basis function network (RBFN) that is e. kind of neural networks. Adaptation laws for parameters of the RBFN are developed based on the Lyapunov stability theory to guarantee the stability of the overall control scheme. Filtered tracking errors between actual outputs and desired outputs are discussed in the sense of the uniformly ultimately boundedness(UUB). Additionally, it is also shown that parameters of the RBFN are bounded. Experimental results for a SCARA-type robot manipulator show that the proposed adaptive tracking controller is adaptable to the environment changes and is more robust than the conventional PID controller and the neuro-controller based on the multilayer perceptron.

• International Journal of Control, Automation, and Systems
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• v.1 no.1
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• pp.35-42
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• 2003

This paper proposes a simple, robust, nonlinear controller based on Lyapunov stability for tracking the reference welding path and velocity of a two-wheeled welding mobile robot (WMR). The system has three degrees of freedom including two wheels and one torch slider. Torch slider motion is used for faster tracking because the welding speed is very slow. Control law is obtained from the Lyapunov control function to ensure the asymptotical stability of the system. The controller has three free parameters for adjusting the performance of the controlled system. A simple way of measuring the errors using two potentiometers is introduced. The effectiveness of the proposed controller is shown through simulation results.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.156-160
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• 1993

This paper presents a controller design to coordinate a robot manipulator under unknown system parameters and bounded disturbance inputs. To control the motion of the manipulator, an inverse dynamics control scheme is applied. Since parameters of the robot manipulators such as mass and inertia are not perfectly known, the difference between the actual and estimated parameters works as a disturbance force. To identify the unknown parameters, an inproved adaptive control algorithm is directly derived from a chosen Lyapunov's function candidate based on the Lyapunov's Second Method. A robust control algorithm is devised to counteract the bounded disturbance inputs such as contact forces and disturbing force coming from the difference between th actual and the estimated system parameters. Numerical examples are shown using three degree-of-freedom planar arm.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.7
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• pp.73-81
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• 1997

An new class of robust position/force hybrid control law is proposed for uncertain robot manipulators. The uncertainty is nonlinear and (plssibly fast) time-varying. Therefore, the uncertain factors such as imper- fect modeling, friction, payload change, and external disturbance are all addressed. Based on the possible bound of the uncertainty, the controller is constructed and the stability study based on Lyapunov function is presented. To show that the proposed control laws are indeed applicable, the theoretical result is applied to a SCARA-type robot manipulator and simulation result is presented.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.32 no.4
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• pp.24-33
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• 2004

In this paper, the helicopter flight control system using online adaptive neural networks which have the universal function approximation property is considered. It is not compensation for modeling errors but approximation two functions required for feedback linearization control action from input/output of the system. To guarantee the tracking performance and the stability of the closed loop system replaced two nonlinear functions by two neural networks, weight update laws are provided by Lyapunov function and the simulation results in low speed flight mode verified the performance of the control system with the neural networks.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.5
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• pp.487-492
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• 2007

This paper presents the development of digital robust fuzzy controller for uncertain nonlinear systems. The proposed approach is based on the intelligent digital redesign(IDR) method with considering the relaxed stability condition of fuzzy control system. The term IDR in the concerned system is to convert an existing analog robust control into an equivalent digital counterpart in the sense of the state-matching. We shows that the IDR problem can be reduced to find the digital fuzzy gains minimizing the norm distance between the closed-loop states of the analog and digital robust control systems. Its constructive conditions are expressed as the linear matrix inequalities(LMIs) and thereby easily tractable by the convex optimization techniques. Based on the nonquadratic Lyapunov function, the robust stabilization conditions are given for the sampled-data fuzzy system, and hence less conservative. A numerical example, chaotic Lorentz system, is demonstrated to visualize the feasibility of the proposed methodology.

• Proceedings of the KIEE Conference
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• 1995.11a
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• pp.210-212
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• 1995

Its is proposed that a stable adaptive control system composed of a fuzzy and a compensating controller, is designed to control nonlinear systems. In fuzzy and proposed compensating controller, parameters of membership functions characterizing the linguistic terms change according to some adaptive law. The adaptive law are based on the Lyapunov systhesis approach. the closed-loop system using the adaptive control structure proposed in this paper is globally stable in the sense that the Lyapunov function decreases as time goes. the following simulation shows the results.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1901-1904
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• 2003

MPC or model predictive control is representative of control methods which are able to handle physical constraints. Closed-loop stability can therefore be ensured only locally in the presence of constraints of this type. However, if the system is neutrally stable, and if the constraints are imposed only on the input, global aymptotic stability can be obtained; until recently, use of infinite horizons was thought to be inevitable in this case. A globally stabilizing finite-horizon MPC has lately been suggested for neutrally stable continuous-time systems using a non-quadratic terminal cost which consists of cubic as well as quadratic functions of the state. The idea originates from the so-called small gain control, where the global stability is proven using a non-quadratic Lyapunov function. The newly developed finite-horizon MPC employs the same form of Lyapunov function as the terminal cost, thereby leading to global asymptotic stability. A discrete-time version of this finite-horizon MPC is presented here. The proposed MPC algorithm is also coded using an SQP (Sequential Quadratic Programming) algorithm, and simulation results are given to show the effectiveness of the method.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.137-140
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• 2001

In this paper, we design 75(Takagi-Sugeno) fuzzy controllers for the systems that can be represented by the 75 fuzzy model. We use inverse optimal approach in which the cost function is determined later than the Lyapunov function and its corresponding control input satisfying the design requirements such as stability and decay rate. The obtained design procedure is in the form of solving LMI(Linear Matrix Inequalities), thus very efficient in practice.

• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.1339-1340
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• 2015

In this paper, we propose a low-complexity control scheme for segway. To design the controller, we use the prescribed performance function and analyze the stability of the proposed control system using the Lyapunov stability theorem. By prescribed performance function, we can adjust the transient and steady-state response. Finally, the simulation results are provided to illustrate the effectiveness of the proposed scheme.