• 제어로봇시스템학회:학술대회논문집
• /
• 1991.10a
• /
• pp.280-285
• /
• 1991

Neural network control has many innovative potentials for fast, accurate and intelligent adaptive control. In this paper, a learning control architecture for the dynamic control of a robot manipulator is developed using inverse dynamic neurocontroller and linear neurocontroher. The inverse dynamic neurocontrouer consists of a MLP (multi-layer perceptron) and the linear neurocontroller consists of SLPs (single layer perceptron). Compared with the previous type of neurocontroller which is using an inverse dynamic neurocontroller and a fixed PD gain controller, proposed architecture shows the superior performance over the previous type of neurocontroller because linear neurocontroller can adapt its gain according to the applied task. This superior performance is tested and verified through the control of PUMA 560. Without any knowledge on the dynamic model, its parameters of a robot , (The robot is treated as a complete black box), the neurocontroller, through practice, gradually and implicitly learns the robot's dynamic properties which is essential for fast and accurate control.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.3 no.2
• /
• pp.146-156
• /
• 2003

Nonlinear dynamic system exist widely in many types of systems such as chemical processes, biomedical processes, and the main steam temperature control system of the thermal power plant. Up to the present time, PID Controllers have been used to operate these systems. However, it is very difficult to achieve an optimal PID gain with no experience, because of the interaction between loops and gain of the PID controller has to be manually tuned by trial and error. This paper suggests control approaches by immune fuzzy for the nonlinear control system inverted pendulum, through computer simulation. This paper defines relationship state variables $x,\dot{x},{\theta},\dot{\theta}$ using immune fuzzy and applied its results to stability.

• Journal of Institute of Control, Robotics and Systems
• /
• v.14 no.12
• /
• pp.1218-1225
• /
• 2008

This paper presents the design of a neural network based adaptive control for missile is presented. The application model is Exocet MM40, which is derived from missile DATCOM database. Acceleration of missile by tail Fin control cannot be controllable by DMI (Dynamic Model Inversion) directly because it is non-minimum phase system. So, the inner loop consists of DMI and NN (Neural Network) and the outer loop consists of PI controller. In order to satisfy the performances only with PI controller, it is necessary to do some additional process such as gain tuning and scheduling. In this paper, all flight area would be covered by just one PI gains without tuning and scheduling by applying mixture control technique of conventional controller and NN to the outer loop. Also, the simulation model is designed by considering non-minimum phase system and compared the performances to distinguish the validity of control law with conventional PI controller.

• Proceedings of the KIEE Conference
• /
• 1995.07b
• /
• pp.685-688
• /
• 1995

In this paper we proposed new scheme replacing adaptive mechanism part in MRAC by fuzzy logic in order to improve transient response in adaptive control system. Conventional adaptive control system has good performance in steady state but it has large error or problem with rise time in transient state. We need to increase adaptation gain of control variable but it causes robustness problem that makes ststem unstable for set-point, load-variation, and dynamic change. To demonstrate presented FTAC(fuzzy tunning adaptive control)'s superiority, presented method is introduced for a class of SISO systems and compare with MRAC. By analyzing simulation result, we can see transient response is improved and the system is not affected by disturbance in proposed method in comparison to MRAC.

• Journal of the Korean Society for Precision Engineering
• /
• v.18 no.4
• /
• pp.167-175
• /
• 2001

This paper presents design of active roll controller of a vehicle and experimental study using the electric actuating roll control system. Firstly, parameter sensitivity analysis is performed based on 3DOF linear vehicle model. The controller is designed in the frame work of gain-scheduled H$\infty$ control scheme considering the varying parameters induced by laden and running vehicle condition. In order to investigate a feasibility of an active control system, experimental work is performed using hardware-in-the -loop setup which has been constructed by the devised electric actuating system and the full vehicle model with tire characteristics. The performance is evaluated by experiment using hardware-in-the -loop simulation under the conditions of some steer maneuvers and parameter variations.

• International Journal of Aeronautical and Space Sciences
• /
• v.8 no.1
• /
• pp.129-139
• /
• 2007

In this paper, attitude control laws for simultaneous pointing of multiple spacecrafts are considered under a formation flying scenario. The basic approach lies in adaptive feedback gains using relative attitude information or maneuver time approximation for coordinated attitude control. Each control law is targeted to balancing mean motion or to correcting system response to the slowest satellite. The control gain adaptation is constructed by two approaches. The first one is using variable damping gain to manipulate speed of a fast system response, and the second one uses alternate natural frequency of the system under control. The validity and stability of the proposed approaches are examined analytically and tested through numerical simulations.

• The Transactions of the Korean Institute of Electrical Engineers P
• /
• v.53 no.3
• /
• pp.134-141
• /
• 2004

Recently, neural network techniques are widely used in adaptive and learning control schemes for production systems. However, in general it takes up a lot of time to learn in the case applied in control system. Furthermore, the physical meaning of neural networks constructed as a result is not obvious. And in practice since it is difficult for the PID gains suitably, lots of researches have been reported with respect of turning schemes of PID gains. A neural network-based PID control scheme is proposed, which extracts skills of human experts as PID gains. This controller is designed by using three-layered neural networks. The effectiveness of the proposed neural network-based PID control scheme is investigated through an application for a production control system. This control method can enable a plant to operate smoothy and obviously as the plant condition varies with any unexpected accidents.

• Proceedings of the KIEE Conference
• /
• 2003.11c
• /
• pp.717-719
• /
• 2003

Recently Neural Network techniques have widely used in adaptive and learning control schemes for production systems. However, generally it costs a lot of time for learning in the case applied in control system. Furthermore, the physical meaning of neural networks constructed as a result is not obvious. And in practice since it is difficult to the PID gains suitably lots of researches have been reported with respect to turning schemes of PID gains. A Neural Network-based PID control scheme is proposed, which extracts skills of human experts as PID gains. This controller is designed by using three-layered neural networks. The effectiveness of the proposed Neural Network-based PID control scheme is investigated through an application for a production control system. This control method can enable a plant to operate smoothy and obviously as the plant condition varies with any unexpected accident.

• Proceedings of the KIEE Conference
• /
• 2004.05a
• /
• pp.89-91
• /
• 2004

Recently Neural Network techniques have widely used in adaptive and learning control schemes for production systems. However, generally it costs a lot of time for learning in the case applied in control system. Furthermore, the physical meaning of neural networks constructed as a result is not obvious. And in practice since it is difficult to the PID gains suitably lots of researches have been reported with respect to turning schemes of PID gains. A Neural Network-based PID control scheme is proposed, which extracts skills of human experts as PID gains. This controller is designed by using three-layered neural networks. The effectiveness of the proposed Neural Network-based PID control scheme is investigated through an application for a production control system. This control method can enable a plant to operate smoothy and obviously as the plant condition varies with any unexpected accident. This paper goal is convergence speed progress about applied gain of PID controller using the neural networks.

• International Journal of Precision Engineering and Manufacturing
• /
• v.7 no.1
• /
• pp.62-66
• /
• 2006

The learning control develops controllers that learn to improve their performance at executing a given task, based on experience performing this specific task. In a previous work, the authors presented an iterative precision of linear decentralized learning control based on p-integrated learning method for the vertical dynamic multiple systems. This paper develops an indirect decentralized learning control based on adaptive control method. The original motivation of the learning control field was learning in robots doing repetitive tasks such as an assembly line works. This paper starts with decentralized discrete time systems, and progresses to the robot application, modeling the robot as a time varying linear system in the neighborhood of the nominal trajectory, and using the usual robot controllers that are decentralized, treating each link as if it is independent of any coupling with other links. Some techniques will show up in the numerical simulation for vertical dynamic robot. The methods of learning system are shown for the iterative precision of each link.