Fuzzy logic control, due to its simple control structure, easy and cost-effective design, has been successfully employed to the application of guidance and control in robotic fields. This paper aims to review fuzzy-logic-based guidance and control in an important branch of robots—marine robotic vehicles. First, guidance and motion forms including the maneuvering, path following, trajectory tracking, and position stabilization are described. Subsequently, the application of three major classes of fuzzy logic control, including the conventional fuzzy control (Mamdani fuzzy control and Takagi–Sugeno–Kang fuzzy control), adaptive fuzzy control (self-tuning fuzzy control and direct/indirect adaptive fuzzy control), and hybrid fuzzy control (fuzzy PID control, fuzzy sliding mode control, and neuro-fuzzy control) are presented. In particular, we summarize the design and analysis process of direct/indirect adaptive fuzzy control and fuzzy PID control in marine robotic fields. In addition, two comparative results between hybrid fuzzy control and the corresponding single control are provided to illustrate the superiority of hybrid fuzzy control. Finally, trends of the fuzzy future in marine robotic vehicles are concluded based on its state of the art.

Survey on Fuzzy-Logic-Based Guidance and Control of Marine Surface Vehicles and Underwater Vehicles

In existing fuzzy logic controllers (FLCs), input variables are mostly the error and the change-of-error regardless of complexity of controlled plants. Either control input u or the change of control input /spl Delta/u is commonly used as its output variable. A rule table is then constructed on a two-dimensional (2-D) space. This scheme naturally inherits from conventional proportional-derivative (PD) or proportional-integral (PI) controller. Observing that 1) rule tables of most FLCs have skew-symmetric property and 2) the absolute magnitude of the control input |u| or |/spl Delta/u| is proportional to the distance from its main diagonal line in the normalized input space, we derive a new variable called the signed distance, which is used as a sole fuzzy input variable in our simple FLC called single-input FLC (SFLC). The SFLC has many advantages: The total number of rules is greatly reduced compared to existing FLCs, and hence, generation and tuning of control rules are much easier. The proposed SFLC is proven to be absolutely stable using Popov criterion. Furthermore, the control performance is nearly the same as that of existing FLCs, which is revealed via computer simulations using two nonlinear plants.

Design and stability analysis of single-input fuzzy logic controller

This paper deals with the sliding mode control of a three-dimensional overhead crane. The model of the crane consists of five highly nonlinear second-order ordinary differential equations. The crane is an underactuated system, which makes the design of its controllers intricate. A sliding mode control scheme is proposed for the crane. This scheme, which guarantees the asymptotic stability of the closed-loop system, has two objectives: position regulation and anti-swing control. The performance of the closed-loop system is simulated using MATLAB. The simulation results indicate that the proposed control scheme works well. In addition, the robustness of the controller with respect to uncertainties in the crane parameters is investigated through simulations. It is found that the controller is robust to changes in the parameters. Moreover, since some of the states of the system are not measurable, a Luenberger-type observer is proposed. Simulation of the controlled system using the observer-based sliding mode controller produced good results.

Sliding Mode Control of a Three-dimensional Overhead Crane:

The antilock braking system (ABS) is designed to optimize braking effectiveness and maintain steerability; however, the ABS performance will be degraded in the case of severe road conditions. In this study, a self-learning fuzzy sliding-mode control (SLFSMC) design method is proposed for ABS. The SLFSMC ABS will modulate the brake torque for optimum braking. The SLFSMC system is comprised of a fuzzy controller and a robust controller. The fuzzy controller is designed to mimic an ideal controller and the robust controller is designed to compensate for the approximation error between the ideal controller and the fuzzy controller. The tuning algorithms of the controller are derived in the Lyapunov sense; thus, the stability of the system can be guaranteed. Also, the derivation of the proposed SLFSMC ABS does not need to use a vehicle-braking model. Simulations are performed to demonstrate the effectiveness of the proposed SLFSMC ABS in adapting to changes for various road conditions.

https://ir.nctu.edu.tw:443/bitstream/11536/28070/1/000181626700014.pdf

Self-learning fuzzy sliding-mode control for antilock braking systems

Adaptive fuzzy sliding-mode control for electrical servo drive

Design of a single-input fuzzy logic controller and its properties

This article proposes a resilient corrective control scheme for input/state asynchronous sequential machines (ASMs) against a class of actuator faults in which certain actuator outputs cannot be generated temporarily. We first present a mathematical formulation to describe the reachability of the controlled ASM damaged by the intermittent loss of actuator outputs. Based on the mathematical formulation, we address the existence condition and design procedure for a state-feedback corrective controller and a diagnoser that achieve resilience, that is, to make the closed-loop system exhibit normal input/state behaviors despite the intermittent loss of actuator outputs. To validate the applicability of the proposed concept and methodology, the closed-loop system of a practical asynchronous digital system is implemented on a field-programmable gate array (FPGA) and experimental verifications are provided.

Seong-Woo Kwak

Papers

Design and stability analysis of single-input fuzzy logic controller

Design of a single-input fuzzy logic controller and its properties

Resilient Corrective Control of Asynchronous Sequential Machines Against Intermittent Loss of Actuator Outputs.

Design of a fuzzy logic control using a single variable

Design and implementation of robust corrective control systems with permanent sensor faults