Kuo-Ho Su

Adaptive enhanced fuzzy sliding-mode control for electrical servo drive

The design and properties of an adaptive enhanced fuzzy sliding-mode control (AEFSMC) system for an indirect field-oriented induction motor (IM) drive to track periodic commands are addressed in this study. A newly designed EFSMC system, in which a translation-width idea is embedded into the FSMC, is introduced initially. Moreover, to confront the uncertainties existed in practical applications, an adaptive tuner, which is derived in the sense of the Lyapunov stability theorem, is utilized to adjust the EFSMC parameter for further assuring robust and optimal control performance. The indirect field-oriented IM drive with the AEFSMC scheme possesses the salient advantages of simple control framework, free from chattering, stable tracking control performance, and robust to uncertainties. In addition, numerical simulation and experimental results due to periodic sinusoidal commands are provided to verify the effectiveness of the proposed control strategy, and its advantages are indicated in comparison with FSMC and EFSMC systems.

Supervisory control for linear piezoelectric ceramic motor drive using genetic algorithm

This paper presents a supervisory genetic algorithm (SGA) control system for a linear piezoelectric ceramic motor (LPCM) driven by a two-inductance two-capacitance resonant driving circuit. First, the motor configuration and driving circuit of an LPCM are introduced, and its hypothetical dynamic model is described briefly. Since the dynamic characteristics and motor parameters of the LPCM are highly nonlinear and time varying, an SGA control system is therefore investigated to achieve high-precision position control. The proposed SGA control system is composed of two parts. One is a GA control that is utilized to search an optimum control effort online via gradient descent training process, and the other is a supervisory control to stabilize the system states around a predefined bound region. Compared with conventional GA control systems, the proposed control scheme possesses the salient advantages of simple structure, fewer executing time, and good self-organizing properties. The effectiveness of the proposed driving circuit and control system is verified with numerical simulations and hardware experiments under the occurrence of uncertainties. In addition, the advantages of the proposed control scheme are indicated in comparison with a traditional integral-proportional position-control system.

Design of neural-fuzzy-based controller for two autonomously driven wheeled robot

An intelligent control architecture for two autonomously driven wheeled robot is developed in this paper. Consider the parametric variation, external load disturbance, nonlinear friction, unpredicted and unstructured uncertainties for the practical applications, the transient and unmodelled uncertainty will be occurred. In the proposed control scheme, the fuzzy inference is designated as a main controller and the neural network is an auxiliary part. In the fuzzy controller, the translation width and total sliding surface are adopted to reduce the chattering phenomena. The neural uncertainty observer is added in the balance, speed and synchronous controllers to reduce the accumulated error and ascend the stability. The hardware includes a microcontroller, gyroscope, accelerometer, and two autonomous motors, etc. The effectiveness is verified by simulation and experimental results, and the result is compared with conventional PD control scheme for the same robot.

Robust tracking control design and its application to balance a two-wheeled robot steering on a bumpy road

A reinforced adaptive fuzzy sliding-mode controller was developed to track the desired state for a certain class of nonlinear dynamic system. Initially, adaptive fuzzy logic was used to derive the update laws for approximating the uncertain nonlinear functions of a dynamic system. Subsequently, a reinforced adaptive mechanism and sliding-mode control were incorporated into the adaptive fuzzy control scheme. In the reinforced adaptive mechanism, both the model and controller parameters were adjusted simultaneously to attenuate the effects caused by the unmodeled dynamics and disturbances. Performance analysis demonstrated the superiority of the proposed reinforced adaptive law over the conventional direct adaptive scheme regarding faster tracking and convergence of parameters. To verify its effectiveness and extend its application, the proposed reinforced adaptive fuzzy sliding-mode controller was applied to balance a two-wheeled robot steering on a bumpy road. A number of simulations and experiments were performed. In addition, a conventional direct adaptive scheme under the same environment was also performed for comparison. These results demonstrated that the balance performance of a nonlinear dynamic two-wheeled robot improved because of embedding the proposed reinforced adaptive fuzzy sliding-mode controller into the microcontroller.

Implementation of adaptive enhanced fuzzy sliding-mode control for indirect field-oriented induction motor drive

An adaptive enhanced fuzzy sliding-mode control (AEFSMC) system is proposed for an indirect field-oriented induction motor (IM) drive to track periodic commands. A newly-design enhanced fuzzy sliding-mode control (EFSMC) system, in which a translation-width idea is embedded into the fuzzy sliding-mode control (FSMC), is introduced initially. Moreover, to confront the uncertainties, an adaptive tuner is utilized to adjust the EFSMC parameter for further assuring robust and optimal control performance. The AEFSMC scheme possesses the salient advantages of simple control framework, free from chattering, stable tracking control performance and robust to uncertainties. Numerical simulation due to periodic sinusoidal command is provided to verify the effectiveness of the proposed control strategy.

Supervisory enhanced genetic algorithm control for indirect field-oriented induction motor drive

A supervisory enhanced genetic algorithm control (SEGAC) system is proposed for an indirect field-oriented induction motor (IM) drive to track periodic commands. The proposed control scheme comprises an enhanced genetic algorithm control (EGAC) and a supervisory control. In the EGAC design, the spirit of gradient descent training is embedded in genetic algorithm (GA) to construct the major controller for searching optimum control effort under the possible occurrence of uncertainties. To stabilize the system states around a defined bound region, a supervisory controller, which is derived in the sense of Lyapunov stability theorem, is designed within the EGAC. The effectiveness of the proposed control strategy is verified by numerical simulation and experimental results, and its advantages are indicated in comparison with a conventional supervisory genetic algorithm control (SGAC) system in the previous works.

Development of Robotic Grasping Gripper Based on Smart Fuzzy Controller

Abstract An alternative robotic grasping gripper including a vision system, machine fingers, pressure modules, and smart fuzzy grasping controller is designed and implemented in this paper. To avoid the redundant computation of inverse kinematics, the relative coordinates are adopted in the proposed architecture. To identify the stiffness and shape of different grasping objects, a smart fuzzy grasping controller is embedded into the recognition process first. According to the identifying results, the membership functions of the smart fuzzy grasping controller are precisely tuned to generate the joint angles of the servo motors online. The effectiveness is verified by some experimental results, and the proposed architectures are implemented in the home-made robotic grasping gripper in laboratory.

Robot path planning and smoothing based on fuzzy inference

To guide an autonomous robot in the space of obstacles, an enhanced path planning system including image processing, cluster reduction, path planning and smoothing based on fuzzy inference is proposed in this paper. To shorten the path planning time, the obstacles in the captured image are clustered into smaller groups firstly. Being a roadmap method for path generation, the fuzzy inference mechanism is employed consecutively. To smooth the planned path, a novel algorithm based on second fuzzy inference mechanism is proposed to move the rough waypoints. To verify the effectiveness of the proposed system, some simulations are carried out. From the simulations, the autonomous robot possesses good path planning and smoothing performance to reach its goal safely under various obstacle layouts.

Adaptive fuzzy balance controller for two-wheeled robot

An adaptive fuzzy sliding-mode balance controller (AFSMBC) for a two-wheeled robot is developed in this study. In the proposed balance controller, a novel sliding surface is adopted as the input variable of fuzzy system to outstanding its merit of insensitivity to uncertainties. In the fuzzy membership function, the translation width is utilized to reduce the chattering phenomena. Moreover, consider the parametric variation, external disturbance and nonlinear friction for the practical wheeled robot motions, the transient and unmodelled uncertainty will be occurred. An adaptive tuner, which is derived in the sense of Lyapunov stability theorem, is added into the fuzzy controller to reduce the accumulated error and to ascend the stability. The hardware of whole control system includes a microcontroller, gyroscope, accelerometer, and two autonomous motors. The effectiveness is verified by simulated and experimental results, and the performance is compared with conventional PD control scheme for the same wheeled robot.

Image-based smooth path planning for wheeled robot

A smooth path planning system including cluster reduction, Voronoi path planning and fuzzy path smoothing algorithm is developed for wheeled robot in this paper. To shorten the path planning time, the obstacles in the captured image are clustered into smaller groups firstly. Being a roadmap method for path generation, the Voronoi path diagram is employed consecutively. To smooth the planned path, a novel algorithm based on fuzzy inference mechanism is proposed to adjust the position of waypoints. To verify the effectiveness of the proposed system, some simulations are carried out. From the simulation, the wheeled robot possesses good path planning and path smoothing performance to reach its goal safely and smoothly under various obstacle layouts.