Shui-Chun Lin

Adaptive Neural Network Control of a Self-Balancing Two-Wheeled Scooter

This paper presents an adaptive control using radial-basis-function neural networks (RBFNNs) for a two-wheeled self-balancing scooter. A mechatronic system structure of the scooter driven by two dc motors is briefly described, and its mathematical modeling incorporating two frictions between the wheels and the motion surface is derived. By decomposing the overall system into two subsystems (yaw motion and mobile inverted pendulum), one proposes two adaptive controllers using RBFNN to achieve self-balancing and yaw control. The performance and merit of the proposed adaptive controllers are exemplified by conducting several simulations and experiments on a two-wheeled self-balancing scooter.

FPGA-Based Parallel DNA Algorithm for Optimal Configurations of an Omnidirectional Mobile Service Robot Performing Fire Extinguishment

This paper presents a coarse-grain parallel deoxyribonucleic acid (PDNA) algorithm for optimal configurations of an omnidirectional mobile robot with a five-link robotic arm. This efficient coarse-grain PDNA is proposed to search for the global optimum of the redundant inverse kinematics problem with minimal movement, thereby showing better population diversity and avoiding premature convergence. Moreover, the pipelined hardware implementation, hardware/software co-design, and System-on-a-Programmable-Chip (SoPC) technology on a field-programmable gate array (FPGA) chip are employed to realize the proposed PDNA in order to significantly shorten its processing time. Simulations and experimental results are conducted to illustrate the merit and superiority of the proposed FPGA-based PDNA algorithm in comparison with conventional genetic algorithms (GAs) for omnidirectional mobile robot performing fire extinguishment.

Adaptive Robust Self-Balancing and Steering of a Two-Wheeled Human Transportation Vehicle

This paper presents adaptive robust regulation methods for self-balancing and yaw motion of a two-wheeled human transportation vehicle (HTV) with varying payload and system uncertainties. The proposed regulators are aimed at providing consistent driving performance for the HTV with system uncertainties and parameter variations caused by different drivers. By decomposing the overall system into the yaw motion subsystems and the wheeled inverted pendulum, two proposed adaptive robust regulators are synthesized to achieve self-balancing and yaw motion control. Numerical simulations and experimental results on different terrains show that the proposed adaptive robust controllers are capable of achieving satisfactory control actions to steer the vehicle.

Adaptive Neural Network Control of a Self-balancing Two-wheeled Scooter

This paper presents an adaptive neural network control for a two-wheeled self-balancing scooter for pedagogical purposes. A mechatronic system structure driven by two DC motors is described, and its mathematical modeling incorporating the friction between the wheels and motion surface is derived. By decomposing the overall system into two subsystems: rotation and inverted pendulum, we design two adaptive radial-basis-function (RBF) neural network (DOF) controllers to achieve self- balancing and rotation control. Experimental results indicate that the proposed controllers are capable of providing appropriate control actions to steer the vehicle in desired manners.

Adaptive nonlinear control using RBFNN for an electric unicycle

This paper presents an adaptive nonlinear control using radial-basis-function neural network (RBFNN) for an electric unicycle. A mechatronic system structure of the unicycle is constructed and its simplified mathematical modeling is then established by using Newtonian mechanics and incorporating the frictions between the wheel and the terrain surface. An adaptive nonlinear control together with RBFNN is developed based on adaptive backstepping technique, in order to simultaneously achieve self-balancing and forward motion. Simulation results are conducted to illustrate feasibility and effectiveness of the proposed control method. The performance and merit of the proposed method are well exemplified by real riding test.

Dynamic Modeling and Tracking Control of a Nonholonomic Wheeled Mobile Manipulator with Dual Arms

This paper presents methodologies for dynamic modeling and trajectory tracking of a nonholonomic wheeled mobile manipulator (WMM) with dual arms. The complete dynamic model of such a manipulator is easily established using the Lagrange’s equation and MATHEMATICA. The structural properties of the overall system along with its subsystems are also well investigated and then exploited in further controller synthesis. The derived model is shown valid by reducing it to agree well with the mobile platform model. In order to solve the path tracking control problem of the wheeled mobile manipulator, a novel kinematic control scheme is proposed to deal with the nonholonomic constraints. With the backstepping technique and the filtered-error method, the nonlinear tracking control laws for the mobile manipulator system are constructed based on the Lyapunov stability theory. The proposed control scheme not only achieves simultaneous trajectory and velocity tracking, but also compensates for the dynamic interactions caused by the motions of the mobile platform and the two onboard manipulators. Simulation results are performed to illustrate the efficacy of the proposed control strategy.

Adaptive Polar-Space Motion Control for Embedded Omnidirectional Mobile Robots with Parameter Variations and Uncertainties

This paper presents an adaptive polar-space motion controller for trajectory tracking and stabilization of a three-wheeled, embedded omnidirectional mobile robot with parameter variations and uncertainties caused by friction, slip and payloads. With the derived dynamic model in polar coordinates, an adaptive motion controller is synthesized via the adaptive backstepping approach. This proposed polar-space robust adaptive motion controller was implemented into an embedded processor using a field-programmable gate array (FPGA) chip. Furthermore, the embedded adaptive motion controller works with a reusable user IP (Intellectual Property) core library and an embedded real-time operating system (RTOS) in the same chip to steer the mobile robot to track the desired trajectory by using hardware/software co-design technique and SoPC (system-on-a-programmable-chip) technology. Simulation results are conducted to show the merit of the proposed polar-space control method in comparison with a conventional proportional-integral (PI) feedback controller and a non-adaptive polar-space kinematic controller. Finally, the effectiveness and performance of the proposed embedded adaptive motion controller are exemplified by conducting several experiments on steering an embedded omnidirectional mobile robot.

Adaptive backstepping control with integral action for PWM buck DC-DC converters

Abstract This paper develops a novel control methodology for voltage regulation and implementation of a buck DC‐DC converter using a digital signal processor (DSP). Such a converter is modeled as a linear averaged state‐space system model with an adjustable load. An adaptive backstepping voltage regulator is presented based on the measurements of an output voltage and a capacitor current. An approximate averaged circuit model is derived in order to show that the fast and stable mode of the capacitor current can be ignored and the buck DC‐DC converter can be well approximated by the averaged circuit model. With the approximate averaged model, an adaptive backstepping control with integral action is proposed to regulate a stand‐alone buck DC‐DC converter. This proposed control method has been verified by computer simulation and implemented utilizing a stand‐alone digital signal processor (DSP) TMS320C542 from Texas Instruments. Experimental results show that the proposed control method is capable of giving satisfactory voltage regulation performance under a wide range of input voltage variations and load changes.

Nonlinear adaptive sliding-mode control design for two-wheeled human transportation vehicle

This paper presents adaptive sliding-mode control methods for self-balancing and yaw rate control of a dynamically two-wheeled human transportation vehicle (HTV) with mass variations and system uncertainties. The proposed controllers aim to provide consistent driving performance for system uncertainties and different drivers whose weights cause parameter variations of the HTV. By decomposing the overall system into the yaw subsystems and the self-balancing subsystems with parameters variations with respect to different riders, two adaptive sliding mode controls are proposed to achieve self-balancing and yaw control. Numerical simulations and experimental results on different terrains show that the proposed adaptive sliding mode controllers are capable of achieving satisfactory control actions to steer the vehicle.

SoPC-based parallel elite genetic algorithm for global path planning of an autonomous omnidirectional mobile robot

This paper presents an efficient parallel elite genetic algorithm (PEGA) for global path planning of an omnidirectional mobile robot moving in a static environment expressed by a grid-based map. This efficient PEGA, consisting of two parallel EGAs along with a migration operator, is proposed for global path planning of the mobile robots. The PEGA takes advantages of maintaining better population diversity, inhibiting premature convergence and keeping parallelism than conventional GAs do. The generated collision-free path is optimal in the sense of the shortest distance. The pipelined hardware implementation of IP (Intellectual Property) core library on a field-programmable gate array (FPGA) chip is employed to significantly speedup the processing time. Furthermore, a soft-core processor and a real-time operating system (RTOS) are embedded into the same chip to perform the global path planning using hardware/software co-design technique and SoPC (System-on-a-Programmable-Chip) concept. The merit and performance of the proposed SoPC-based PEGA are illustrated by conducting several simulations and experiments.