Tsong-Li Lee

Genetic Auto-Tuning and Rule Reduction of Fuzzy PID Controllers

This paper presents a novel method for parameter auto-tuning of a fuzzy proportional-integral-derivative (PID) controller. Different from PID controllers with fixed gains, the fuzzy PID controller is expressed in terms of fuzzy rules, in which the input variables are the error signals and their derivatives, while the output variables are the PID gains. In this manner, the PID gains are adaptive and the fuzzy PID controller has more flexibility and capability than the traditional ones. When tuning the fuzzy PID gains, a genetic-algorithm-based method is proposed, in which the centers and the widths of the Gaussian membership functions, the fuzzy control rules corresponding to every possible combination of input linguistic variables, and the PID gains are chosen as parameters to be determined. In encoding the parameters into corresponding chromosomes, a mixed-coding technique is adopted. To expedite the convergence speed of the evolutionary process, the concept of enlarged sampling space and ranking mechanism are used. To show the effectiveness and validity of the designed fuzzy PID controller, a multivariable seesaw system is used for illustration.

A fuzzy algorithm for navigation of mobile robots in unknown environments

A fuzzy algorithm is proposed to navigate a mobile robot in a completely unknown environment. The mobile robot is equipped with an electronic compass and two optical encoders for dead-reckoning, and two ultrasonic modules for self-localization and environment recognition. From the readings of sensors at every sampling instant, the proposed fuzzy algorithm determines the priorities of thirteen possible heading directions. Then, the robot is driven to an intermediate configuration along the heading direction that has the highest priority. The navigation procedure is iterated until the final configuration is reached. To show the feasibility of the proposed method, experimental results are given.

A Nonlinear programming method for time-optimal control of an omni-directional mobile robot

The time-optimal control problem of a three-wheeled omni-directional mobile robot is studied in this paper. In contrast to traditional methods, in which the Pontryagins Minimum Principle is usually used, an iterative procedure is proposed to transform the time-optimal problem into a nonlinear programming (NLP) one. In the formulated NLP problem, the number of control steps is fixed initially and the sampling period is treated as a variable to be minimized in the optimization process. An upper limit on the sampling period is set in advance considering the accuracy of discretization. If the value of the sampling period is larger than the upper limit, then an update of the number of control steps is needed. To generate initial feasible solutions of the NLP problem, a genetic algorithm is adopted. Since different initial feasible solutions can be generated, the optimization process can be started from different points to find the optimal solution. In this manner, one can find a time-optimal movement of the omni-directional mobile robot between two configurations. To show the feasibility of the proposed method, simulation results are included for illustration.

Navigation of a mobile robot in outdoor environments

Abstract Implementation of an outdoor navigation system for a mobile robot is described in this paper. In this system, two optical encoders are used to perform dead‐reckoning for the mobile robot. Meanwhile, an electronic compass and a GPS receiver are used for self‐localization of the robot. Fusing the sensory data from dead‐reckoning and self‐localization, position and orientation of the robot can be determined accurately. Communication between the robot and the host computer is achieved through GSM modems. In controlling the right and left angular velocities of the robot, a PID control law will be used. For illustration, computer simulation and a practical experiment are presented to show that the outdoor navigation of a mobile robot is feasible, in a convenient and cost‐effective manner.

Simultaneous Auto-Tuning of Membership Functions and Fuzzy Control Rules Using Genetic Algorithms

A novel genetic algorithm is proposed to design membership functions and to reduce the number of fuzzy control rules simultaneously since these two components are interdependent in a fuzzy logic controller. With Gaussian membership functions, the centers and the widths of these functions, the fuzzy control rules corresponding to every possible combination of input linguistic variables are chosen as parameters to be optimized. In transforming these parameters into corresponding chromosomes, a mixed coding technique is adopted. Moreover, the concept of enlarged sampling space and ranking mechanism are used to expedite the convergence of the evolutionary process. To show the feasibility and validity of the proposed method, the design of a cart-centering controller using as few as possible fuzzy control rules will be given. Simulation results will demonstrate that the designed fuzzy logic controller can drive the cart system from a given initial state to a desired final state even when the cart mass varies within a wide range.

Time-Optimal Control of an Omni-Directional Mobile Robot

The time-optimal control problem of a three-wheeled omni-directional mobile robot is addressed in this paper. Different from usual cases, in which the Pontryagins minimum principle (PMP) is used, an iterative procedure is proposed to transform the time-optimal problem into a nonlinear programming (NLP) one. In the NLP problem, the count of control steps is fixed initially and the sampling period is treated as a variable in the optimization process. The optimization object is to minimize the sampling period such that it is below a specific minimum value, which is set in advance considering the accuracy of discretization. To generate initial feasible solutions of the formulated NLP problem, genetic algorithms (GAs) are adopted. Since different initial feasible solutions can be generated, the optimization process can be started from different points to find the optimal solution. In this manner, one can find a time-optimal movement of the omni-directional mobile robot between two configurations. To show the feasibility of the proposed method, simulation results are included for illustration

Self-Localization of Mobile Robots Based on Visual Information

A CCD camera, a stepping motor and three artificial landmarks are used for localization of a mobile robot. The CCD camera is mounted on the robot and its rotation is controlled by the stepping motor. Then from the images captured by the CCD camera, image-processing techniques are used to determine the distances between the CCD camera and the three landmarks. On the basis of these distances, one can prove that the position of the robot can be determined uniquely provided that the three artificial landmarks observed are not installed in a line. Moreover, the heading angle of the robot can be determined from the rotation angle of the stepping motor. To account for possible installation errors, a nonlinear programming method is proposed. Meanwhile, a least-squares method is also be used to increase the accuracy of localization. To show the feasibility of the proposed method, an experimental example is given for illustration

A Kinematics-Based Method for Time-Optimal Control of an Omni-Directional Robot

The main goal of this study is to investigate the time-optimal control problem of an omni-directional mobile robot between two configurations. In the proposed method, this problem is formulated and solved as a constrained nonlinear programming (NLP) one. During the optimization process, the count of control steps is fixed initially and the sampling period is treated as a variable to be determined. The goal is to minimize the sampling period such that it is below a specific minimum value, which is set in advance considering the accuracy of discretization. To generate initial feasible solutions of the NLP problem, a systematic approach is also proposed. Since different initial feasible solutions can be generated, the optimization process of the NLP problem can be started from many different points to find the optimal solution. To show the feasibility of the proposed method, simulation and experimental results are included for illustration.

A nonlinear programming method for 3D localization of mobile robots

This paper presents a system, which is composed of a laser range finder and four artificial reflectors, for the three-dimensional (3D) localization of a mobile robot. In this system, it will be proved that the position and the heading angle of a mobile robot in a 3D space with respect to a reference frame can be determined provided that the four artificial reflectors are not installed in the same plane. Since the artificial reflectors cannot be treated as points in practice, the proposed localization procedure will be formulated as a nonlinear programming problem to account for the actual sizes of the artificial reflectors. To show the feasibility of the proposed method, experiments will be given for illustration