Won-Ho Kim

Implementation of Fuzzy Self-Tuning PID and Feed-Forward Design for High-Performance Motion Control System

The existing conventional motion controller does not perform well in the presence of nonlinear properties, uncertain factors, and servo lag phenomena of industrial actuators. Hence, a feasible and effective fuzzy self-tuning proportional integral derivative (PID) and feed-forward control scheme is introduced to overcome these problems. In this design, a fuzzy tuner is used to tune the PID parameters resulting in the rejection of the disturbance, which achieves better performance. Then, both velocity and acceleration feed-forward units are added to considerably reduce the tracking error due to servo lag. To verify the capability and effectiveness of the proposed control scheme, the hardware configuration includes digital signal processing (DSP) which plays the main role, dual-port RAM (DPRAM) to guarantee rapid and reliable communication with the host, field-programmable gate array (FPGA) to handle the task of the address decoder and receive the feed-back encoder signal, and several peripheral logic circuits. The results from the experiments show that the proposed motion controller has a smooth profile, with high tracking precision and real-time performance, which are applicable in various manufacturing fields.

Adaptive robust fuzzy control for path tracking of a wheeled mobile robot

This paper proposes an adaptive robust fuzzy control scheme for path tracking of a wheeled mobile robot with uncertainties. The robot dynamics including the actuator dynamics is considered in this work. The presented controller is composed of a fuzzy basis function network (FBFN) to approximate an unknown nonlinear function of the robot complete dynamics, an adaptive robust input to overcome the uncertainties, and a stabilizing control input. The stability and the convergence of the tracking errors are guaranteed using the Lyapunov stability theory. When the controller is designed, the different parameters for two actuator models in the dynamic equation are taken into account. The proposed control scheme does not require the accurate parameter values for the actuator parameters as well as the robot parameters. The validity and robustness of the proposed control scheme are demonstrated through computer simulations.

Fuzzy sliding mode control for a robot manipulator

This work presents the design of a robust control system using a sliding mode controller that incorporates a fuzzy control scheme. The presented control law superposes a sliding mode controller and a fuzzy logic controller. A fuzzy tuning scheme is employed to improve the performance of the control system. The proposed fuzzy sliding mode control (FSMC) scheme utilizes the complementary cooperation of the traditional sliding mode control (SMC) and the fuzzy logic control (FLC). In other words, the proposed control scheme has the advantages which it can guarantee the stability in the sense of Lyapunov function theory and can ameliorate the tracking errors, compared with the FLC and SMC, respectively. Simulation results for the trajectory tracking control of a two-link robot manipulator are presented to show the feasibility and robustness of the proposed control scheme.

Autotuning Controller for Motion Control System Based on Intelligent Neural Network and Relay Feedback Approach

In this paper, we introduce a proportional-integral-derivative (PID) autotuning controller using an intelligent neural network control based on the relay feedback approach. The proposed controller takes advantage of offline learning and self-learning capability of the online control strategy, in which the initial knowledge of the control system is recognized by the relay feedback approach, and the online learning capability of the neural network controller helps the control system respond quickly to the dynamics changes. Furthermore, the proposed control algorithms are implemented in the high performance digital signal processor TMS320F28335. The robustness and motion tracking performance are validated through simulation and experimental results.

Adaptive robust fuzzy control and implementation for path tracking of a mobile robot

In this paper, an adaptive robust fuzzy control scheme with a genetic algorithm (GA) is proposed to solve the path tracking problem of a wheeled mobile robot. The presented controller consists of a fuzzy basis function network (FBFN) to approximate an unknown nonlinear function of the robot dynamics with uncertainties. A genetic algorithm is employed in the fuzzy inference to optimize the fuzzy rules of FBFN. The robust term with adaptive update rules is designed to suppress the external disturbances, hence it makes the system insensitive to the noises and disturbances of the environment. The robot dynamics including the actuator dynamics is considered. The stability and the convergence of the tracking errors are guaranteed by using the Lyapunov stability theory. The validity and robustness of the proposed control scheme are demonstrated through computer simulations and experiments with a wheeled mobile robot.

Implementation of input shaping control to reduce residual vibration in industrial network motion system

In this paper, a method to implement the input shaping control in Mechatrolink-III motion system is introduced. Firstly, the underdamped system is theoretically built by modeling. Then, the motion controller of Mechatrolink-III network is designed to apply the input shaper for reducing residual vibration in manufacturing machine. Later, the experimental beam is set-up to verify the effective performance of input shaping technique. The validity of the control strategy is practically performed by testing ZV, ZVD and ZVDD input shapers. Especially, the successful implementation provides an opportunity to apply the input shaper for multi-axes since Mechatrolink-III motion controller supports up to 32 axes.

Nonlinear adaptive control of a 3D overhead crane

In this paper, a nonlinear adaptive control of a 3D overhead crane is investigated. A dynamic model of the overhead crane is developed, where the crane system is assumed as a lumped mass model. Under the mutual effects of the sway motions of the payload and the hoisting motion, the nonlinear behavior of the crane system is considered. A nonlinear control model-based scheme is designed to achieve the three objectives: (i) drive the crane system to the desired positions, (ii) suppresses the vibrations of the payload, and (iii) velocity tracking of hoisting motion. The nonlinear control scheme employs adaptation laws that estimate unknown system parameters, friction forces and the mass of the payload. The estimated values are used to compute control forces applied to the trolley of the crane. The asymptotic stability of the crane system is investigated by using the Lyapunov method. The effectiveness of the proposed control scheme is verified by numerical simulation results.

A Study on an Adaptive Robust Fuzzy Controller with GAs for Path Tracking of a Wheeled Mobile Robot

This paper proposes an adaptive robust fuzzy control scheme for path tracking of a wheeled mobile robot with uncertainties. The robot dynamics including the actuator dynamics is considered in this work. The presented controller is composed of a fuzzy basis function network (FBFN) to approximate an unknown nonlinear function of the robot complete dynamics, an adaptive robust input to overcome the uncertainties, and a stabilizing control input. Genetic algorithms are employed to optimize the fuzzy rules of FBFN. The stability and the convergence of the tracking errors are guaranteed using the Lyapunov stability theory. When the controller is designed, the different parameters for two actuator models in the dynamic equation are taken into account. The proposed control scheme does not require the accurate parameter values for the actuator parameters as well as the robot parameters. The validity and robustness of the proposed control scheme are demonstrated through computer simulations.

ECAM Control System Based on Auto-tuning PID Velocity Controller with Disturbance Observer and Velocity Compensator

This paper proposed an ECAM (Electronic cam) control system which has simple and general structure. The proposed cam controller adopted the linear and polynomial curve-fitting method to generates a smooth cam profile curve function. Smooth motion trajectory of master actuator guarantees the good performance of slave motion and has an important effect on the interpolation quality of ECAM. The auto-tuning PID velocity controller was applied to overcome the uncertainties in ECAM, and the gains of the controller are updated continuously to ensure the consistency of system performance under varying working conditions. The robustness of system against the varying load torque disturbances and noises is guaranteed by using the load torque disturbance observer to suppress the disturbance on master actuator. The velocity compensator was applied to compensate the degradation of performance of slave motion caused from the varying driving speed of master motion. The stability and validity of the proposed ECAM control system was verified by simulation results.

Implementation of an Adaptive Robust Neural Network Based Motion Controller for Position Tracking of AC Servo Drives

The neural network with radial basis function is introduced for position tracking control of AC servo drive with the existence of system uncertainties. An adaptive robust term is applied to overcome the external disturbances. The proposed controller is implemented on a high performance digital signal processing DSP TMS320C6713-300. The stability and the convergence of the system are proved by Lyapunov theory. The validity and robustness of the controller are verified through simulation and experimental results