I-Hsum Li

A Merged Fuzzy Neural Network and Its Applications in Battery State-of-Charge Estimation

To solve learning problems with vast number of inputs, this paper proposes a novel learning structure merging a number of small fuzzy neural networks (FNNs) into a hierarchical learning structure called a merged-FNN. In this paper, the merged-FNN is proved to be a universal approximator. This computing approach uses a fusion of FNNs using B-spline membership functions (BMFs) with a reduced-form genetic algorithm (RGA). RGA is employed to tune all free parameters of the merged-FNN, including both the control points of the BMFs and the weights of the small FNNs. The merged-FNN can approximate a continuous nonlinear function to any desired degree of accuracy. For a practical application, a battery state-of-charge (BSOC) estimator, which is a twelve input, one output system, in a lithium-ion battery string is proposed to verify the effectiveness of the merged-FNN. From experimental results, the learning ability of the newly proposed merged-FNN with RGA is superior to that of the traditional neural networks with back-propagation learning.

Dynamic Slip-Ratio Estimation and Control of Antilock Braking Systems Using an Observer-Based Direct Adaptive Fuzzy–Neural Controller

This paper proposes an antilock braking system (ABS), in which unknown road characteristics are resolved by a road estimator. This estimator is based on the LuGre friction model with a road condition parameter and can transmit a reference slip ratio to a slip-ratio controller through a mapping function. The slip-ratio controller is used to maintain the slip ratio of the wheel at the reference values for various road surfaces. In the controller design, an observer-based direct adaptive fuzzy-neural controller (DAFC) for an ABS is developed to online-tune the weighting factors of the controller under the assumption that only the wheel slip ratio is available. Finally, this paper gives simulation results of an ABS with the road estimator and the DAFC, which are shown to provide good effectiveness under varying road conditions.

An On-Line Robust and Adaptive T-S Fuzzy-Neural Controller for More General Unknown Systems

This paper proposes a novel method of on-line modeling via the Takagi-Sugeno (T-S) fuzzy-neural model and robust adaptive control for a class of general unknown nonaffine nonlinear systems with external disturbances. Although studies about adaptive T-S fuzzy-neural controllers have been made on some nonaffine nonlinear systems, little is known on the more complicated and general nonlinear systems. Compared with the previous approaches, the contribution of this paper is an investigation of the more general unknown nonaffine nonlinear systems using on-line adaptive T-S fuzzy-neural controllers. Instead of modeling these unknown systems directly, the T-S fuzzy-neural model approximates a so-called virtual linearized system (VLS), with modeling errors and external disturbances. We prove that the closed-loop system controlled by the proposed controller is robust stable and the effect of all the unmodeled dynamics, modeling errors and external disturbances on the tracking error is attenuated under mild assumptions. To illustrate the effectiveness and applicability of the proposed method, simulation results are given in this paper.

A hierarchical structure of observer-based adaptive fuzzy-neural controller for MIMO systems

An observer-based adaptive controller developed from a hierarchical fuzzy-neural network (HFNN) is employed to solve the controller time-delay problem for a class of multi-input multi-output (MIMO) non-affine nonlinear systems under the constraint that only system outputs are available for measurement. By using the implicit function theorem and Taylor series expansion, the observer-based control law and the weight update law of the HFNN adaptive controller are derived. According to the design of the HFNN hierarchical fuzzy-neural network, the observer-based adaptive controller can alleviate the online computation burden. Moreover, the common adaptive controller is utilized to control all the MIMO subsystems. Hence, the number of adjusted parameters of the HFNN can be further reduced. In this paper, we prove that the proposed observer-based adaptive controller can guarantee that all signals involved are bounded and that the outputs of the closed-loop system track asymptotically the desired output trajectories.

RGA-based on-line tuning of BMF fuzzy-neural networks for adaptive control of uncertain nonlinear systems

In this paper, an RGA-based indirect adaptive fuzzy-neural controller (RIAFC) for uncertain nonlinear systems is proposed by using a reduced-form genetic algorithm (RGA). Both the control points of B-spline membership functions (BMFs) and the weighting factors of the adaptive fuzzy-neural controller are tuned on-line via the RGA approach. Each gene represents an adjustable parameter of the BMF fuzzy-neural network with real number components. For the purpose of on-line tuning these parameters and evaluating the stability of the closed-loop system, a special fitness function is included in the RGA approach. In addition, in order to guarantee that the system states are confined to the safe region, a supervisory controller is incorporated into the RIAFC. To illustrate the feasibility and applicability of the proposed method, two examples of nonlinear systems controlled by the RIAFC are demonstrated.

Autonomous stair detection and climbing systems for a tracked robot

Currently autonomous mobile robots offer a diversity of types and functions. This paper mainly emphasizes on a functionality and practicality of self-made tracked robot, which can move autonomously on a bumpy road, such as stairs and slopes. With information on distance obtained by Kinect sensor, the tracked robot is able to identify different types of roads. In addition, we design four modes, including exploration mode, alignment mode, calculating tilt angle mode and climbing mode, to achieve the tasks of identifying and climbing stairs and slopes.

T-S Fuzzy Control for Uncertain Nonlinear Systems Using Adaptive Fuzzy Approach

This paper proposes on-line modeling via Takagi-Sugeno (T-S) fuzzy models and robust adaptive control for a class of unknown nonlinear dynamic systems with external disturbances. The T-S fuzzy model is established to approximate an unknown nonlinear dynamic system in a linearized way. Fuzzy B-spline membership functions (BMFs) which possesses a fixed number of control points are developed for on-line tuning. In this paper, the closed-loop system which is controlled by the proposed controller can be stabilized and the tracking error will converge to zero. An example is simulated in order to confirm the effectiveness and applicability of the proposed methods in this paper.

On-line modeling and control via T-S fuzzy models for nonaffine nonlinear systems using a second type adaptive fuzzy approach

This paper proposes a novel method for on-line modeling and robust adaptive control via Takagi-Sugeno (T-S) fuzzy models for nonaffine nonlinear systems, with external disturbances. The T-S fuzzy model is established to approximate the nonaffine nonlinear dynamic system in a linearized way. The so-called second type adaptive law is adopted, where not only the consequent part (the weighting factors) of fuzzy implications but also the antecedent part (the membership functions) of fuzzy implications are adjusted. Fuzzy B-spline membership functions (BMFs) are used for on-line tuning. Furthermore, the effect of all the unmodeled dynamics, BMF modeling errors and external disturbances on the tracking error is attenuated by a fuzzy error compensator which is also constructed from the T-S fuzzy model. In this paper, we can prove that the closed-loop system which is controlled by the proposed controller is stable and the tracking error will converge to zero. Three examples are simulated in order to confirm the effectiveness and applicability of the proposed methods in this paper.

A Kinect-sensor-based Tracked Robot for Exploring and Climbing Stairs

This paper focuses on the stair-climbing problem for a tracked robot. The tracked robot designed in this paper has the ability to explore stairs in an unknown indoor environment, climbing up and down the stairs, keeping balance while climbing, and successfully landing on the stair platform. Intelligent algorithms are proposed to explore and align stairs, and a fuzzy controller is introduced to stabilize the tracked robots movement during the exploration. An inexpensive Kinect depth sensor is the only equipment needed for all the control modes. Finally, experiments illustrate the effectiveness of the proposed approach for climbing stairs.

A Fourier series-based adaptive sliding-mode controller for nonlinear pneumatic servo systems

The behavior of nonlinearity and time-varying cause the pneumatic actuator systems are difficult to be controlled. This paper proposes a Fourier series-based adaptive sliding-mode controller for nonlinear pneumatic servo systems. The Fourier series-based functional approximation technique can approximate an unknown function, thus bypassing the model-based prerequisite. The learning laws for the coefficients of the Fourier-series functions are derived from a Lyapunov function to guarantee the system stability. Consequently, practical experiments on a rodless pneumatic servo system are successfully implemented with different path tracking profiles, which validates the proposed method.