Ching-Chih Tsai

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.

Parallel Elite Genetic Algorithm and Its Application to Global Path Planning for Autonomous Robot Navigation

This paper presents a parallel elite genetic algorithm (PEGA) and its application to global path planning for autonomous mobile robots navigating in structured environments. This PEGA, consisting of two parallel EGAs along with a migration operator, takes advantages of maintaining better population diversity, inhibiting premature convergence, and keeping parallelism in comparison with conventional GAs. This initial feasible path generated from the PEGA planner is then smoothed using the cubic B-spline technique, in order to construct a near-optimal collision-free continuous path. Both global path planner and smoother are implemented in one field-programmable gate array chip utilizing the system-on-a-programmable-chip technology and the pipelined hardware implementation scheme, thus significantly expediting computation speed. Simulations and experimental results are conducted to show the merit of the proposed PEGA path planner and smoother for global path planning of autonomous mobile robots.

A localization system of a mobile robot by fusing dead-reckoning and ultrasonic measurements

This paper develops a novel system hardware structure and systematic digital signal processing algorithms for self-localization of an autonomous mobile robot by fusing dead-reckoning and ultrasonic measurements. The multisensorial dead-reckoning (DR) subsystem is established based on the optimal filtering by first fusing heading readings from a magnetic compass, a rate-gyroscope and two encoders mounted on the robot wheels, thereby computing the dead-reckoned location estimate. The novel ultrasonic localization subsystem consists of one ultrasonic transmitter and one radio-frequency (RF) controlled switch mounted on the known location fixed to an inertial frame of reference, four ultrasonic receivers and one RF controlled switch installed on the mobile robot. Four ultrasonic Time-of-Flight (TOF) measurements together with the dead-reckoned location information are fused to update vehicles position by utilizing the extended Kalman filtering (EKF) algorithm. The proposed algorithms are implemented by using a host PC 586 computer and standard C++ programming techniques. A built system prototype together with its experimental results is used to confirm that the system not only retains its strengths of high accuracy and magnetic interferencing immunity, but also provides a simple and practical structure of use and installation calibration.

Adaptive Predictive Control With Recurrent Neural Network for Industrial Processes: An Application to Temperature Control of a Variable-Frequency Oil-Cooling Machine

An adaptive predictive control with recurrent neural network prediction for industrial processes is presented. The neural predictive control law with integral action is derived based on the minimization of a modified predictive performance criterion. The stability and steady-state performance of the closed-loop control system are well studied. Numerical simulations reveal that the proposed control gives satisfactory tracking and disturbance rejection performance for two illustrative nonlinear systems with time-delay. Experimental results for temperature control of a variable-frequency oil-cooling process show the efficacy of the proposed method for industrial processes with set-points changes and load disturbances.

Backlight power reduction and image contrast enhancement using adaptive dimming for global backlight applications

The paper presents an adaptive dimming technique to reduce backlight power consumption and enhance image contrast for global backlight applications. The proposed adaptive dimming technique consists of two new algorithms: backlight dimming algorithm and contrast enhancement algorithm. The backlight-dimming algorithm obtains appropriate 0% to 50% backlight power reduction depending on characteristics of the image data. The contrast enhancement algorithm not only reduces the adverse effect of backlight power saving, but also improves 20.75% enhancement of image contrast ratio on the average. Numerous simulation results are used for illustration of the effectiveness and merits of the proposed adaptive dimming technique. Experimental results are conducted to show the performance and usefulness of the proposed technique on a 2.2-inch mobile phone liquid crystal display (LCD) made by thin-film-transistor (TFT) technology.

FPGA Implementation of an Embedded Robust Adaptive Controller for Autonomous Omnidirectional Mobile Platform

This paper presents an embedded adaptive robust controller for trajectory tracking and stabilization of an omnidirectional mobile platform with parameter variations and uncertainties caused by friction and slip. Based on a dynamic model of the platform, the adaptive controller to achieve point stabilization, trajectory tracking, and path following is synthesized via the adaptive backstepping approach. This robust adaptive controller is then implemented into a high-performance field-programmable gate array chip using hardware/software codesign technique and system-on-a-programmable-chip design concept with a reusable user intellectual property core library. Furthermore, a soft-core processor and a real-time operating system are embedded into the same chip for realizing the control law to steer the mobile platform. Simulation results are conducted to show the effectiveness and merit of the proposed control method in comparison with a conventional proportional-integral feedback controller. The performance and applicability of the proposed embedded adaptive controller are exemplified by conducting several experiments on an autonomous omnidirectional mobile robot.

Multivariable self-tuning temperature control for plastic injection molding process

This paper develops a multivariable self-tuning predictive control for improving set-point tracking performance, disturbance rejection, and robustness of a temperature control system for an extruder barrel in a plastic injection molding process. The stochastic discrete-time multivariable mathematical model is built and its unknown system parameters are identified by using the recursive least-squares estimation method. The multivariable predictive control is derived based on the minimization of a generalized predictive performance criterion. A real-time self-tuning control algorithm is proposed and then implemented by using a digital signal processor (DSP) TMS320C31 from Texas Instruments. Experimental results are used to show the feasibility and effectiveness of the proposed method.

Adaptive decoupling predictive temperature control for an extrusion barrel in a plastic injection molding process

This paper presents an adaptive decoupling temperature control for an extrusion barrel in a plastic injection molding process. After establishing a stochastic polynomial matrix model of the system, a corresponding decoupling system representation was then developed. The decoupling control design was derived based on the minimization of a generalized predictive performance criterion. The set-point tracking, disturbance rejection, and robustness capabilities of the proposed method can be improved by appropriate adjustments to the tuning parameters in the criterion function. A real-time control algorithm, including the recursive least-squares method, is proposed which was implemented using a digital signal processor TMS320C31 from Texas Instruments. Through the experimental results, the proposed method has been shown to be powerful under set-point changes, load disturbances, and significant plant uncertainties. The proposed control law is shown to be less computational and more effective than other well-known multivariable control strategies, and more powerful than single-loop temperature-zone control policies.

Localization of an Autonomous Mobile Robot Based on Ultrasonic Sensory Information

Based on ultrasonic sensory information, an approach is proposed for localization of autonomous mobile robot (AMRs). In the proposed method, it will be proven that the combination of three ultrasonic transmitters and two receivers can determine both the position and the orientation of an AMR with respect to a reference frame uniquely. In this manner, since only ultrasonic sensors are used, the proposed method will be highly cost-effective and easy to implement. To show the validity and feasibility of the proposed method, the hardware configuration and a series of experiments will be given for illustration.

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.