Ming Chang Chen

Hierarchical T-S fuzzy-neural control of anti-lock braking system and active suspension in a vehicle

This paper proposes a novel method for identification and robust adaptive control of an anti-lock braking system with an active suspension system by using the hierarchical Takagi-Sugeno (T-S) fuzzy-neural model. The goal of a conventional ABS control system is to rapidly eliminate tracking error between the actual slip ratio and a set reference value in order to bring the vehicle to a stop in the shortest time possible. However, braking time and stopping distance can be reduced even further if the same control system also simultaneously considers the state of the active suspension system. The structure learning capability of the proposed hierarchical T-S fuzzy-neural network is exploited to reduce computational time, and the number of fuzzy rules. Thus, this proposed controller is applied to achieve integrated control over the anti-lock braking system (ABS) with the active suspension system. Our simulation results, presented at the end of this paper, show that the proposed controller is extremely effective in integrated control over the ABS and the active suspension system.

Control of uncertain active suspension system with anti-lock braking system using fuzzy neural controllers

This paper proposes anti-lock braking system to integrate with active suspensions system applied in a quarter vehicles model, and can use a road estimate to get the road condition. This estimate is based on the LuGre friction model with a road condition parameter, and can transmit a reference slip ration to slip ratio controller through a mapping function considering the effect of road characteristics. In the controller design, an observer-based direct adaptive fuzzy-neural controller (DAFC) for an ABS is developed. After, this paper will discuss that active suspension system influence on ABS. Active suspension systems are not ideal, unchanging, and certain, as many control systems assume. If parts of the suspension system fail, it becomes an uncertain system. In such cases, we need an approximator to remodel this uncertain system to maintain good control. We propose a new method to on-line identify the uncertain active suspension system and design a T-S fuzzy-neural controller to control it. Finally, integrating algorithm is constructed to coordinate these two subsystems. Simulation results of the ABS with active suspension system, and is shown to provide good effectiveness under varying conditions.

Dynamic obstacle avoidance path planning

This paper proposes an image-based parallel lines distance measurement system (IBPLDMS), which is capable of executing an obstacle-detection and obstacle-avoidance path planning in a dynamic environment. The proposed IBPLDMS contains an image processing unit and an obstacle-avoidance unit. The image processing unit preprocesses captured images for an obstacle-avoidance task, in which the images are processed by the procedures of the grayscale transform, image morphologies, canny edge detection, connected-component labeling, and Hough transform. According to the definition of standard parallel lines, the obstacle-avoidance path for robots can be determined in the real time. Note that for the proposed IBPLDMS, only one webcam and the definition of the standard parallel lines are necessary for preparing for the setup. From the experimental results, we prove the proposed method is effective.

Fuzzy measure based mobile robot controller for autonomous movement control

This paper proposes a novel fuzzy measure based mobile robot controller design method. We apply this method in a simulation where the movement of a mobile robot along a wall is governed by this controller. The ultrasonic range finder sensors onboard the mobile robot are used to measure the distance between the robot and the wall. The measurement results are recorded as fuzzy measure inputs and the results of the fuzzy measure are used to control the movement of the mobile robot along the wall. Our simulations compare the movements of the mobile robot with and without the fuzzy measure controller. The simulation results show that the mobile robot using the fuzzy measure controller exhibits a more controlled movement behavior than that using a controller without fuzzy measure.

Monocular image-based local collision-free path planning for autonomous robots

Monocular image-based local collision-free path planning for autonomous robots is presented in this paper. According to a pre-set pair of parallel lines, transformation equations from the image domain to the real world domain are easily defined. Moreover, the distances to obstacles in the robot’s visual domain can be estimated. Our proposed method can not only easily identify obstacles and wall edges, but also estimate the distances and plan a collision-free path. Besides, this paper successfully integrates an image processing module with a local collision-free path planning, and also applies them to the collision-free and path planning of a mobile robot. For the proposed local collision-free path planning, the webcam can be located at two different situations: one is setting a webcam located on the ceiling and the other is setting a webcam on a mobile robot. In addition, the measurement method only uses a webcam and four laser projectors. Thus, we do not need to purchase expensive equipment to accomplish the desired results. From the experimental results, it shows that our proposed method can be effectively applied to the local collision-free path planning.

Direct adaptive control via decomposed fuzzy Petri net

This paper presents a novel direct adaptive controller design via decomposed fuzzy Petri net to solve the control tracking problem. The controller combines decomposed fuzzy system (DFS) and Petri net to achieve good performance with less computation time. In the DFS structure, fuzzy variables are decomposed into several layers. DFS has been shown to have fast learning capability but with a complicated system structure. In this study, Petri net is employed to form a mechanism in constructing meaningful component fuzzy systems in the DFS so that the number of fuzzy components can be dramatically reduced without significantly degrading the modeling performance. Finally, the effectiveness of the proposed controller scheme is verified by simulation results.

On-line adaptive T-S fuzzy neural control for active suspension systems

Vehicles are not always driven on smooth roads. If parts of the suspension system fail, it becomes an uncertain system. Thus we need an approximator to remodel this uncertain system to maintain good control. In this paper, we propose a new method to on-line identify the uncertain suspension system and design a T-S fuzzy-neural controller to control it. We first use the mean value theorem to transform the active suspension system into a virtual linearized system. In addition, an on-line adaptive T-S fuzzy-neural modeling approach to the design of robust tracking controllers is developed for the uncertain active suspension system. Finally, this paper gives simulation results of an uncertain suspension system with the on-line adaptive T-S fuzzy-neural controller, and is shown to provide good effectiveness under the conditions that parts of the suspension system fail.