Lyapunov-function-based design of fuzzy logic controllers and itsapplication on combining controllers

This paper presents the design of fuzzy logic controllers (FLCs) for nonlinear systems with guaranteed closed-loop stability and its application on combining controllers. The design is based on heuristic fuzzy rules. Although each rule in the FLC refers to a stable closed-loop subsystem, the overall system stability cannot be guaranteed when all these rules are applied together. In this paper, it is proved that if each subsystem is stable in the sense of Lyapunov (ISL) under a common Lyapunov function, the overall system is also stable ISL. Since no fuzzy plant model is involved, the number of subsystems generated is relatively small, and the common Lyapunov function can be found more easily. To probe further, an application of this design approach to an inverted pendulum system that combines a sliding-mode controller (SMC) and a state feedback controller (SFC) is reported. Each rule in this FLC has an SMC or an SFC in the consequent part. The role of the FLC is to schedule the final control under different antecedents. The stability of the whole system is guaranteed by the proposed design approach. More importantly, the controller thus designed can keep the advantages and remove the disadvantages of the two conventional controllers     

A new approach to numerical stability analysis of fuzzy controlsystems

The paper presents numerical methodology for the stability analysis of a fuzzy control system. The fuzzy control system analyzed is a closed-loop system controlled by a fuzzy logic controller (FLC) with singleton consequents. Compared with previous works based on numerical approaches (E. Kim et al., 1999), the method proposed in the paper employs two new strategies to release the conservatism of the previous methods: region-wise affine transformation and piecewise quadratic Lyapunov function. Finally, the effectiveness of the stability analysis is illustrated by a numerical example and its computer simulation     

A Study of a Robust Fuzzy Controller for Perturbed Nonlinear Systems

In this paper, a fuzzy logic controller (FLC) for a nonlinear system with uncertain perturbation is proposed. Based on the Lyapunov synthesis approach, we construct the FLC not only to stabilize the perturbed nonlinear system but also to guarantee a prescribed H_∞ norm bound of the closed-loop transfer function from the disturbance to the controlled output. A given example illustrates the availability of the proposed design method.     

Adaptive neuro-fuzzy control of a flexible manipulator

This paper describes an adaptive neuro-fuzzy control system for controlling a flexible manipulator with variable payload. The controller proposed in this paper is comprised of a fuzzy logic controller (FLC) in the feedback configuration and two dynamic recurrent neural networks in the forward path. A dynamic recurrent identification network (RIN) is used to identify the output of the manipulator system, and a dynamic recurrent learning network (RLN) is employed to learn the weighting factor of the fuzzy logic. It is envisaged that the integration of fuzzy logic and neural network based-controller will encompass the merits of both technologies, and thus provide a robust controller for the flexible manipulator system. The fuzzy logic controller, based on fuzzy set theory, provides a means for converting a linguistic control strategy into control action and offering a high level of computation. On the other hand, the ability of a dynamic recurrent network structure to model an arbitrary dynamic nonlinear system is incorporated to approximate the unknown nonlinear input–output relationship using a dynamic back propagation learning algorithm. Simulations for determining the number of modes to describe the dynamics of the system and investigating the robustness of the control system are carried out. Results demonstrate the good performance of the proposed control system.     

Adaptive Fuzzy Output-Feedback Control for Switched Nonlinear Systems with Arbitrary Switchings

This paper investigates the adaptive fuzzy output-feedback control problem for single-input and single-output switched uncertain nonlinear systems. The addressed systems in this paper have the characteristics of arbitrary switchings, unknown nonlinear dynamics and immeasurable states. A common state observer is designed independent of switching signals. Fuzzy logic systems are utilized to approximate unknown lumped nonlinear dynamics. Based on the framework of backstepping design technique, an adaptive fuzzy output-feedback control scheme is developed. By using the common Lyapunov function theory, the stability of the closed-loop system is proved. The proposed control scheme does not need the assumptions that the states of the controlled system are available for measurement and that the switching signals satisfy the average dwell time. Moreover, it can guarantee that all the closed-loop signals are bounded, and the system output eventually converges to a small neighborhood of the origin. Finally, simulation studies are provided to further check the effectiveness of the proposed control scheme.     

GA and Lyapunov theory-based hybrid adaptive fuzzy controller for non-linear systems

In this present article, a new hybrid methodology for designing stable adaptive fuzzy logic controllers (AFLCs) for a class of non-linear system is proposed. The proposed design strategy exploits the features of genetic algorithm (GA)-based stochastic evolutionary global search technique and Lyapunov theory-based local adaptation scheme. The objective is to develop a methodology for designing AFLCs with optimised free parameters and guaranteed closed-loop stability. Simultaneously, the proposed method introduces automation in the design process. The stand-alone Lyapunov theory-based design, GA-based design and proposed hybrid GA–Lyapunov design methodologies are implemented for two benchmark non-linear plants in simulation case studies with different reference signals and one experimental case study. The results demonstrate that the hybrid design methodology outperforms the other control strategies on the whole.     

Fuzzy Feedback Linearization Control for MIMO Nonlinear System and Its Application to Full-Vehicle Suspension System

The paper presents a novel fuzzy feedback linearization control of nonlinear multi-input multi-output (MIMO) systems for the tracking and almost disturbance decoupling (ADD) performances based on the fuzzy logic control (FLC). The main contribution of this study is to construct a controller, under appropriate conditions, such that the resulting closed-loop system is valid for any initial condition and bounded tracking signal with the following characteristics: input-to-state stability with respect to disturbance inputs and almost disturbance decoupling. The feedback linearization control guarantees the almost disturbance decoupling performance and the uniform ultimate bounded stability of the tracking error system. As soon as the tracking errors are driven to touch the global final attractor with the desired radius, the fuzzy logic control immediately is applied via a human expert’s knowledge to improve the convergence rate. One example, which cannot be solved by the previous paper on the almost disturbance decoupling problem, is proposed in this paper to exploit the fact that the tracking and the almost disturbance decoupling performances are easily achieved by the proposed approach. In order to demonstrate the applicability, this paper has investigated a full-vehicle suspension system.     

A Hybrid Neuro-Fuzzy System for Adaptive Vehicle Separation Control

The primary purpose of this paper is to develop a robust adaptive vehicle separation control in the increasingly important roles of intelligent transportation system (ITS). A hybrid neuro-fuzzy system (HNFS) is proposed for developing the adaptive vehicle separation control to minimize the distance (headway) between successive cars. This hybrid system consists of two modules: a headway identification (prediction) module and a control decision module. Each of these modules is realized with a distinct neuro-fuzzy network that upgrades hierarchical granularity and reduces the complexity in the control system. Given the current headway and relative velocity between the two consecutive cars, the headway identification module predicts the headway of the next time instant. This identified headway, together with the desired velocity are input to the control decision module whose output is the actual acceleration/deceleration control output. The HNFS encapsulates the adaptive learning capabilities of a neural network into a fuzzy logic control framework to fine-tune the fuzzy control rules. On the other hand, rules derived initially from well-defined fuzzy phase plane accelerate the training of the neural network. Simulation results are very encouraging. It is observed that the headway decreases significantly without sacrificing speed. Furthermore, both stability and robustness of HNFS are demonstrated.     

New hybrid fuzzy controller for direct torque control induction motor drives

A new hybrid fuzzy controller for direct torque control (DTC) induction motor drives is presented in this paper. The newly developed hybrid fuzzy control law consists of proportional-integral (PI) control at steady state, PI-type fuzzy logic control at transient state, and a simple switching mechanism between steady and transient states, to achieve satisfied performance under steady and transient conditions. The features of the presented new hybrid fuzzy controller are highlighted by comparing the performance of various control approaches, including PI control, PI-type fuzzy logic control (FLC), proportional-derivative (PD) type FLC, and combination of PD-type FLC and I control, for DTC-based induction motor drives. The pros and cons of these controllers are demonstrated by intensive experimental results. It is shown that the presented induction motor drive is with fast tracking capability, less steady state error, and robust to load disturbance while not resorting to complicated control method or adaptive tuning mechanism. Experimental results derived from a test system are presented confirming the above-mentioned claims.     

光电跟踪系统的模糊Ⅱ型控制技术研究

为了解决高型控制方法在光电伺服跟踪系统应用中存在的稳态精度与超调量之间的矛盾,设计了一种由多种群遗传算法(MPGA)优化的模糊Ⅱ型控制方法.该方法在经典Ⅰ型双闭环反馈控制器的速度环之前并联一个积分环节,将系统型别增加到Ⅱ型,以此加快反应速度、减小稳态误差;并引入模糊控制器(FLC),根据系统状态动态调节积分环节的增益,...     

Event-triggered type-2 fuzzy-based sliding mode control for steer-by-wire systems

This paper proposes an event-triggered higher-order sliding mode control for steer-by-wire (SbW) systems subject to limited communication resources and uncertain nonlinearity. First, an interval type-2 fuzzy logic system (IT2 FLS) is adopted to approximate the uncertain nonlinearities. A fuzzy-based state observer is developed to estimate unavailable states of the extended SbW system. Then, to save communication resources and eliminate chattering, an event-triggered higher-order sliding mode control is proposed for the SbW system. The key advantage is that the proposed control scheme can offset the observation error and the event-triggering error. After that, the practical finite-time stability of the closed-loop SbW system is proved in the framework of the Lyapunov theory. Finally, numerical simulations and vehicle experiments are given to evaluate the effectiveness and superiority of the proposed scheme.     

Robust Petri Fuzzy-Neural-Network Control for Linear Induction Motor Drive

This study focuses on the development of a robust Petri-fuzzy-neural-network (PFNN) control strategy applied to a linear induction motor (LIM) drive for periodic motion. Based on the concept of the nonlinear state feedback theory, a feedback linearization control (FLC) system is first adopted in order to decouple the thrust force and the flux amplitude of the LIM. However, particular system information is required in the FLC system so that the corresponding control performance is influenced seriously by system uncertainties. Hence, to increase the robustness of the LIM drive for high-performance applications, a robust PFNN control system is investigated based on the model-free control design to retain the decoupled control characteristic of the FLC system. The adaptive tuning algorithms for network parameters are derived in the sense of the Lyapunov stability theorem, such that the stability of the control system can be guaranteed under the occurrence of system uncertainties. The effectiveness of the proposed control scheme is verified by both numerical simulations and experimental results, and the salient merits are indicated in comparison with the FLC system     

Hybrid control system design using a fuzzy logic interface

A hybrid control system is proposed for regulating an unknown nonlinear plant. The interface between the continuous-state plant and the discrete-event supervisor is designed using a fuzzy logic approach. The fuzzy logic interface partitions the continuous-state space into a finite number of regions. In each region, the original unknown nonlinear plant is approximated by a fuzzy logic-based linear model, then state-feedback controllers are designed for each linear model. A high-level supervisor coordinates (mode switching) the set of closed-loop systems in a stable and safe manner. The stability of the system is studied using nonsmooth Lyapunov functions. For illustration and verification purposes, this technique has been applied to the well-known inverted pendulum balancing problem.     

A novel driver assist stability system for all-wheel-drive electric vehicles

A novel driver-assist stability system for all-wheel-drive electric vehicles is introduced. The system helps drivers maintain control in the event of a driving emergency, including heavy braking or obstacle avoidance. The system comprises a fuzzy logic system that independently controls wheel torque to prevent vehicle spin. Another fuzzy wheel slip controller is used to enhance vehicle stability and safety. A neural network is trained to generate the required reference for yaw rate. Vehicle true speed is estimated by a sensor data fusion method. The intrinsic robustness of fuzzy controllers allows the system to operate in different road conditions successfully. Moreover, the ease of implementing fuzzy controllers gives a potential for vehicle stability enhancement.     

基于模糊逼近的非线性多时延系统的自适应跟踪控制

针对一类多输入多输出非线性多时延系统,提出了基于模糊逼近的自适应跟踪控制方案.该方案构建了基于模糊T-S模型的自适应时延模糊逻辑系统,用来逼近未知非线性时延函数.从而实现了对非线性系统的建模.根据跟踪误差给出了时延模糊逻辑系统的参数自适应律.设计了H_∞补偿器来抵消模糊逼近误差和外部扰动.基于Lyapunov稳定性理论,提出的控制方案保证了闭环系统的稳定性并获得了期望的H_∞跟踪性能.机械臂的仿真结果表明了该方案的有效性.     

Lateral stabilization of a four wheel independent drive electric vehicle on slippery roads

In this paper, a new controller is proposed for lateral stabilization of four wheel independent drive electric vehicles without mechanical differential. The proposed controller has three levels including high, medium and low control levels. Desired vehicle dynamics such as reference longitudinal speed and reference yaw rate are determined by higher level of controller. Moreover, using a neural network observer and a fuzzy logic controller, a novel reference longitudinal speed generator system is presented. This system guarantees the vehicle’s stable motion on the slippery roads. In this paper, a new sliding mode controller is proposed and its stability is proved by Lyapunov stability theorem. This sliding mode control structure is faster, more accurate, more robust, and with smaller chattering than classic sliding mode controller. Based on the proposed sliding mode controller, the medium control level is designed to determine the desired traction force and yaw moment. Therefore, suitable wheel forces are calculated. Finally, the effectiveness of the introduced controller is investigated through conducted simulations in CARSIM and MATLAB software environments.     

An adaptive fuzzy logic controller: its VLSI architecture andapplications

Most previous work about the hardware design of a fuzzy logic controller (FLC) intended to either improve its inference performance for real-time applications or to reduce its hardware cost. To our knowledge, there has been no attempt to design a hardware FLC that can perform an adaptive fuzzy inference for the applications of on-line adaptation. The purpose of this paper is to present such an adaptive memory-efficient FLC and its applications. Taking advantage of the adaptability provided by a symbolic fuzzy rule format and the dynamic membership function generator, as well as the high-speed integration capability afforded by VLSI, the proposed adaptive fuzzy logic controller (AFLC) can perform an adaptive fuzzy inference process using various inference parameters, such as the shape and location of a membership function, dynamically and quickly. Three examples are used to illustrate its applications, and the experimental results show the excellent adaptability provided by AFLC     

Decentralized nonlinear adaptive control of an HVAC system

This paper presents a new decentralized nonlinear adaptive controller (DNAC) for a heating, ventilating, and air conditioning (HVAC) system capable of maintaining comfortable conditions under varying thermal loads. In this scheme, an HVAC system is considered to be two subsystems and controlled independently. The interactions between the two subsystems are treated as deterministic types of uncertain disturbances and their magnitudes are supposed to be bounded by absolute value. The decentralized nonlinear adaptive controller (DNAC) consists of an inner loop and an outer loop. The inner loop is a single-input fuzzy logic controller (FLC), which is used as the feedback controller to overcome random instant disturbances. The outer loop is a Fourier integral-based control, which is used as the frequency-domain adaptive compensator to overcome steady, lasting uncertain disturbances. The global DNAC controller ensures that the system output vector tracks a desired trajectory vector within the system bandwidth and that the tracking error vector converges uniformly to a zero vector. The simulated experimental results on the HVAC system show that the performance is dramatically improved.     

Neuro-Fuzzy Dynamic-Inversion-Based Adaptive Control for Robotic Manipulators—Discrete Time Case

In this paper, we present a stable discrete-time adaptive tracking controller using a neuro-fuzzy (NF) dynamic-inversion for a robotic manipulator with its dynamics approximated by a dynamic T-S fuzzy model. The NF dynamic-inversion constructed by a dynamic NF (DNF) system is used to compensate for the robot inverse dynamics for a better tracking performance. By assigning the dynamics of the DNF system, the dynamic performance of a robot control system can be guaranteed at the initial control stage, which is very important for enhancing system stability and adaptive learning. The discrete-time adaptive control composed of the NF dynamic-inversion and NF variable structure control (NF-VSC) is developed to stabilize the closed-loop system and ensure the high-quality tracking. The NF-VSC enhances the stability of the controlled system and improves the system dynamic performance during the NF learning. The system stability and the convergence of tracking errors are guaranteed by the Lyapunov stability theory, and the learning algorithm for the DNF system is obtained thereby. An example is given to show the viability and effectiveness of the proposed control approach     

Multiobjective control of a vehicle with triple trailers

We consider the backing-up control of a vehicle with triple trailers using a model-based fuzzy-control methodology. First, the vehicle model is represented by a Takagi-Sugeno fuzzy model. Then, we employ the so-called "parallel distributed compensation" design to arrive at a controller that guarantees the stability of the closed-loop system consisted of the fuzzy model and controller. The control-design problem is cast in terms of linear matrix inequalities (LMIs). In addition to stability, the control performance considerations such as decay rate, constraints on input and output, and disturbance rejection are incorporated in the LMI conditions. In application to the vehicle with triple trailers setup, we utilize these LMI conditions to explicitly avoid the saturation of the steering angle and the jackknife phenomenon in the control design. Both simulation and experimental results are presented. Our results demonstrate that the fuzzy controller effectively achieves the backing-up control of the vehicle with triple trailers while avoiding the saturation of the actuator and "jackknife" phenomenon.