Fuzzy Control Law Research Articles

Folding Angle Measurement and Control Technology of Deformable UAV

The deformable UAV has the advantage over the traditional fixed-wing aircraft in expanding the flight envelope. In this paper, a folding wing mechanism is designed to meet the accurate angle control requirement. Accordingly, an angle measurement method based on MEMS (micro-electromechanical systems) accelerometers is studied. The accurate folding angle in any condition can be derived from analysis of the folding wing’s acceleration in different positions, upon which the fuzzy control law of the wing’s folding angle is proposed. Finally, the validity and accuracy of the algorithm is verified with experiment. DOI: http://dx.doi.org/10.11591/telkomnika.v11i4.2345

LMI Relaxations for $$\mathcal{H }_{\infty }$$ and $$\mathcal{H }_{2}$$ Static Output Feedback of Takagi–Sugeno Continuous-Time Fuzzy Systems

This paper presents new results concerning the problem of static output feedback $$\mathcal{H }_{\infty }$$ and $$\mathcal{H }_{2}$$ control design for continuous-time Takagi–Sugeno (T–S) fuzzy systems. A fuzzy line integral Lyapunov function with arbitrary polynomial dependence on the premise variables is used to certify closed-loop stability with a bound to the $$\mathcal{H }_{\infty }$$ and $$\mathcal{H }_{2}$$ norms, allowing the membership functions to vary arbitrarily (i.e., no bounds on the time-derivative of the membership functions are assumed). The static output feedback fuzzy controller is obtained through a two-step procedure: first, a fuzzy state feedback control gain is determined by means of linear matrix inequalities (LMIs). Then, the state feedback gain matrices are used in the LMI conditions of the second step that, if satisfied, provide the fuzzy static output feedback control law. The proposed approach also allows the output feedback gains to have independent and arbitrary polynomial dependence on some specific premise variables, selected by the designer, with great advantages for practical applications. The efficiency of the proposed strategy is demonstrated by means of numerical examples and time domain simulations.

Fuzzy Track-Keeping Steering Design for a Precise Control of the Ship

In this paper we design the fuzzy logic autopilot to precise control and a track-keeping control of the ship. Precise control of ship has known an increasing interest in the last years. The main benefits of usage of autopilot for control of ship can be removing a man from the dangers of the undersea environment and reduction in cost of exploration of deep seas. Currently, it is common to use the special ship to accomplish missions as the inspection of coastal and off-shore structures, cable maintenance, as well as hydrographical surveys. In the military field they are employed in such tasks as surveillance, intelligence gathering, torpedo recovery and mine counter measures. Hence, a problem of its ability to move along a reference path and keep a desired orientation is regarded. A non-linear mathematical model describes the vehicle’s dynamics. Command signals are generated by an autopilot with fuzzy control law implemented. Some results of computer simulations are provided to demonstrate effectiveness, correctness and usefulness of the approach.

Adaptive fuzzy sliding mode control using adaptive sliding gain for MEMS gyroscope

In this paper, an adaptive fuzzy sliding mode control strategy with bound estimation is proposed to control the position of a micro-electro-mechanical systems gyroscope in the presence of model uncertainties and external disturbances. The proposed adaptive fuzzy sliding mode control system is composed of a fuzzy controller and a sliding mode controller. The sliding controller is designed to compensate for the approximation error between fuzzy controller and optimal fuzzy control law. The adaptation laws based on the Lyapunov analysis can adaptively adjust the fuzzy rules, thus guaranteeing the stability of the closed loop adaptive fuzzy control system. Moreover, an estimation mechanism is derived to identify the unknown upper bound of approximation error. Numerical simulations are investigated to verify the effectiveness of the proposed adaptive fuzzy control scheme.

Robust control for a class of T-S fuzzy systems with interval time-varying delay

The problem of delay-dependent stability analysis and controller design for a class of T-S fuzzy systems with interval state time-varying delay is considered. Based on Lyapunov stability theory, defining a new Lyapunov-Krasovskii functional and introducing some free-weighting matrices, a new delay-dependent criterion is given to ensure the systems asymptotically stable. The merit of the proposed conditions lies in the less conservativeness than the existing ones, which is achieved by considering the mean of time-delay interval and the introduction of the free variables. By the concept of parallel distributed compensation (PDC), a delay-dependent condition for the existence of a fuzzy state feedback control law with memory is proposed. Another merit is the consideration of the memory of the controller. All conditions are shown in terms of linear matrix inequalities (LMIs), which can be solved efficiently by using the LMI optimization techniques. Two numerical examples are given to illustrate the feasibility and validity of the proposed approach.

Vibration suppression control of smart piezoelectric rotating truss structure by parallel neuro-fuzzy control with genetic algorithm tuning

The main goal of this paper is to develop a novel approach for vibration control on a piezoelectric rotating truss structure. This study will analyze the dynamics and control of a flexible structure system with multiple degrees of freedom, represented in this research as a clamped–free–free–free truss type plate rotated by motors. The controller has two separate feedback loops for tracking and damping, and the vibration suppression controller is independent of position tracking control. In addition to stabilizing the actual system, the proposed proportional–derivative (PD) control, based on genetic algorithm (GA) to seek the primary optimal control gain, must supplement a fuzzy control law to ensure a stable nonlinear system. This is done by using an intelligent fuzzy controller based on adaptive neuro-fuzzy inference system (ANFIS) with GA tuning to increase the efficiency of fuzzy control. The PD controller, in its assisting role, easily stabilized the linear system. The fuzzy controller rule base was then constructed based on PD performance-related knowledge. Experimental validation for such a structure demonstrates the effectiveness of the proposed controller. The broad range of problems discussed in this research will be found useful in civil, mechanical, and aerospace engineering, for flexible structures with multiple degree-of-freedom motion.

Performance Evaluation of Fuzzy Switching Position Controller for Automation and Process Industry Control

This paper presents the development and implementation of a new and practical fuzzy switching position controller for process industry control. Compatible with the structure of a bang-bang controller, a two-input fuzzy switching position controller with five rules is proposed. The idea is based on constructing a fuzzy switching control law which is functionally analogous to a traditional bang-bang controller. Fuzzy logic rules are used to enable an improved version of the conventional bang-bang control. The objective is to replace the conventional bang-bang controller with the proposed fuzzy switching position controller. A modified three-stage bang-bang controller is also designed and implemented. This controller is tested under the same conditions as that of the proposed fuzzy switching position controller, and its performance is used as a basis of comparison by which both controllers are measured. Both controllers are implemented in real time, using the position control of a brushless dc motor drive found in the majority of these new robotic devices. A test bed was built and tested in the laboratory using a collection of tools that include both off-the-shelf hardware (dSPACE DSP DS1104) and software (MATLAB/Simulink). Experimental results show that the fuzzy switching position controller produces adequate control performance, particularly in handling nonlinearities and external disturbances. The efficacy of the fuzzy switching position controller is demonstrated by its positive results, practicality, and feasibility in the process industry sector, when compared with a modified traditional bang-bang controller.

An LMI-Based Approach to Static Output Feedback Stabilization of T—S Fuzzy Systems

AbstractThe problem of static output feedback control design for continuous-time Takagi-Sugeno (T–S) fuzzy systems is addressed in this paper. The membership functions are modeled in a space defined by the Cartesian product of simplexes, called multi-simplex, and are allowed to vary arbitrarily (i.e. no bounds on the time-derivative of the membership functions are assumed). The static output feedback fuzzy controller is obtained through a two-step procedure: first, a stabilizing fuzzy state feedback control gain is determined by means of linear matrix inequalities (LMIs). Then, the state feedback gain matrices are used in LMI conditions that, if satisfied, provide the fuzzy static output feedback control law. A fuzzy line integral Lyapunov function with arbitrary polynomial dependence on the premise variables is used to assess closed-loop stability. The main appeal of the approach is that the output feedback gains can have independent and arbitrary polynomial dependence on some specific premise variables, selected by the designer, with great advantages for practical applications. An example illustrates that the proposed strategy can provide less conservative results when compared to other methods from the literature for output feedback stabilization of continuous-time T–S fuzzy systems.

Application of switching traction and braking systems in longitudinal control for seriesâ parallel hybrid electric buses

In this paper, a fuzzy adaptive control law for switching traction and braking systems to stop a hybrid electric bus is proposed. Moreover, the fuzzy adaptive controller takes the actuator constraints into account. Also, online estimation of external disturbance has been used in the design of control law for the robustness of the controller. The stability and convergence properties of the longitudinal brake controller are analytically proved by using Lyapunov stability theory and Barbalat?s lemma. The proposed algorithm is examined for platoon performance. The results of numerical simulation and processor-in-loop simulation demonstrate the control stability of trajectory tracking.

Fuzzy logic based PWM control and neural controlled-variable estimation of pneumatic artificial muscle actuators

There is increasing research to explore both efficient and cost-effective control of pneumatic artificial muscle (PAM) actuators against inherent nonlinear behaviors of the actuators themselves. This paper presents a fuzzy logic based Pulse-Width-Modulation (PWM) control of PAM actuators together with controlled-variable estimation of a neural network. The PAM actuator consists of two main non-linear elements: a PAM and an on/off control valve unit. Dynamic modeling of the PAM actuator is carried out so as to represent a real PAM actuator in simulation of the dynamic behaviors for gaining knowledge in the controller design. The proportional-type fuzzy control law based on a minimum-time control design is proposed to determine the mass flow rate of compressed air in manipulating the controlled variables of the PAM actuators, such as position and force. In circumstances, when the controlled variables are inaccessible, a neural network model is proposed to estimate those variables instead of using direct measurement. The class of PAM actuators available in industry is used as a practical example to show the effectiveness of the proposed methodology in real working conditions. The concept of the proposed knowledge-based control system, which can emulate the reasoning procedures in this work, can be generalized to systematically implement other PAM actuators in real-time control.

Modelling and control of manipulators with flexible links working on land and underwater environments

SUMMARYIn this study, modelling and control of a two-link robot manipulator whose first link is rigid and the second one is flexible is considered for both land and underwater conditions. Governing equations of the systems are derived from Hamilton's Principle and differential eigenvalue problem. A computer program is developed to solve non-linear ordinary differential equations defining the system dynamics by using Runge–Kutta algorithm. The response of the system is evaluated and compared by applying classical control methods; proportional control and proportional + derivative (PD) control and an intelligent technique; integral augmented fuzzy control method. Modelling of drag torques applied to the manipulators moving horizontally under the water is presented. The study confirmed the success of the proposed integral augmented fuzzy control laws as well as classical control methods to drive flexible robots in a wide range of working envelope without overshoot compared to the classical controls.

Clinical system engineering of long-term automatic thermal control during brain hypothermia under changing conditions

Automatic control systems of brain temperature for water surface-cooling were first-ever applied to the brain hypothermic treatment of patients. A patient in ICU was regarded as a unity controlled system with an input (temperature of water into blanket) and an output (tympanic membrane temperature). The proposed algorithm of optimal-adaptive and fuzzy control laws inclusive of our developed cooling and warming machine were well confirmed during the hypothermic course to keep brain temperature of patients within its allowable range. It was well controlled without much influence due to room temperature, metabolic and circulatory change caused by various medical treatments including the effect of nonlinear and time-varying characteristics of individual patients. The clinical control of brain temperature was almost continuously performed in around 10 days, under the brain temperature between 35 degrees C and 37 degrees C scheduled by physicians according to the state of patients. Their state had been monitored during the therapeutic course of pharmacological treatment with almost everyday examinations by CT imaging, referring various vital signs inclusive of the temperature of urinary bladder with continuous measurement of intracranial pressure by a catheter placement in cerebral sinus. The patients had good recovery to their rehabilitation after mild hypothermia by the proposed automatic control systems.

Guaranteed cost control analysis and iterative design for constrained Takagi–Sugeno systems

This paper presents a novel control design technique in order to obtain a guaranteed cost fuzzy controller subject to constraints on the input channel. This guaranteed cost control law is obtained via multi-parametric quadratic programming. The result is a piecewise fuzzy control law where the state partition is defined by fuzzy inequalities. The parameters of the Lyapunov function can be obtained previously using Linear Matrix Inequalities optimization.

Relaxações convexas de convergência garantida para o projeto de controladores para sistemas nebulosos de Takagi-Sugeno

Este artigo fornece condições convexas com convergência garantida para o projeto de controladores por realimentação de estados que estabilizam quadraticamente e também asseguram desempenhos ótimos H2 e H∞ sob estabilidade quadrática para sistemas nebulosos de Takagi-Sugeno contínuos no tempo. As condições propostas são formuladas como desigualdades matriciais lineares (LMIs) dependentes de parâmetros com variáveis de folga provenientes do Lema de Finsler e com parâmetros pertencentes ao simplex unitário. A lei de controle nebulosa é dada por um ganho de realimentação de estados que é um polinômio homogêneo de grau g, que tem como caso particular o compensador paralelo distribuído quando g = 1. Propriedades algébricas dos parâmetros do sistema e resultados recentes sobre positividade de polinômios são usados para construir relaxações LMIs que, diferentemente da maioria das relaxações da literatura, convergem assintoticamente para a solução sempre que esta existir. Graças ao grau de liberdade obtido pelo uso de variáveis de folga, as condições apresentadas no artigo representam avanços em relação a condições recentemente publicadas baseadas no Teorema de Pólya, e podem ser vistas como uma alternativa a técnicas baseadas na relaxação de formas quadráticas. A eficiência numérica em termos de precisão e esforço computacional é demonstrada por meio de comparações com outros métodos da literatura.

Fuzzy Longitudinal Controller Design and Experimentation for Adaptive Cruise Control and Stop&Go

This paper presents a fuzzy longitudinal control system with car-following speed ranging from 0 to 120 km/h, thereby achieving the main functions of both adaptive cruise control (ACC) and Stop&Go control. A fuzzy longitudinal controller is synthesized by inputting the difference of the actual relative distance and the safe distance obtained from the preceding vehicle, and the relative speed, and then outputting the pulse-width-modulation (PWM) signal to control the output forces of the vacuum boosters. With the use of the high-level controller from dSPACE, the fuzzy control law is easily and rapidly implemented using Matlab/Simulink for the experimental car, and the controller’s parameters can be changed and updated by analyzing data based on the relative distance using Lidar, the speed of the host vehicle, the opening of the throttle and the position of the braking pedal. For the sake of safe driving, experimental results are conducted by simulating the various possible car-following conditions for the ACC and Stop&Go controllers, thereby obtaining virtually relative distances and speeds to tune the controller’s parameters and ensure the safety of the controller. Several car following experiments are conducted to show that the proposed fuzzy longitudinal controller is capable of achieving the requirements of comfort and safety, and giving a satisfactory performance at high and low speed conditions.

Linear-like discrete-time fuzzy control in the regulation of irrigation canals

A linear-like discrete-time fuzzy controller was designed to control and stabilize a single-pool irrigation canal. Saint Venant equations for open-channel flow were linearized using the Taylor series and a finite-difference approximation of the original nonlinear partial differential equations. Using the linear optimal control theory, a traditional linear quadratic regulator (LQR) was first developed for an irrigation canal with a single-pool, and the results were observed. Then a linear-like global system representation of a discrete-time fuzzy system was proposed by viewing a discrete-time fuzzy system in a global concept and unifying the individual matrices into synthetic matrices. This linear-like representation aided development of a design scheme for a global optimal fuzzy controller in the way of the general linear quadratic approach. Based on this kind of system representation, a discrete-time optimal fuzzy control law that can achieve global minimum effect was developed. An example problem with a single-pool was considered for evaluating the performance of the discrete-time optimal fuzzy controller in the control of irrigation canals. The results obtained with the optimal fuzzy controller were compared to the results obtained with a traditional linear quadratic regulator. The discrete-time fuzzy controller was the best for the operation of the canal system, reaching the optimal performance index under unknown demands.

Experimental Comparison Research on Active Vibration Control for Flexible Piezoelectric Manipulator Using Fuzzy Controller

Space manipulators are flexible structures. Vibration problem will be unavoidable due to motion or external disturbance excitation. Model based control methods will not maintain the required accuracy because of the existence of nonlinear factors and parameter uncertainties. To solve these problems, fuzzy logic control laws with different membership function groups are adopted to suppress vibrations of a flexible smart manipulator using collocated piezoelectric sensor/actuator pair. Also, dual-mode controllers combining fuzzy logic and proportional integral control are designed, for suppressing the lower amplitude vibration near the equilibrium point significantly. Experimental comparison research is conducted, using fuzzy control algorithms and the dual-mode controllers with different membership functions. The experimental results show that the adopted fuzzy control algorithms can substantially suppress the larger amplitude vibration; and the dual-mode controllers can also damp out the lower amplitude vibration significantly. The experimental results demonstrate that the proposed fuzzy controllers and dual-mode controllers can suppress vibration effectively, and the optimal placement is feasible.

수중청소로봇의 운항 제어용 시뮬레이터 연구

In this paper, a 3-D simulator was developed to estimate visually the performance of propelling and integrated control system of the underwater cleaning robot. Based on the dynamics analysis of the UCR, the 3-D model of the UCR was used in the simulator in which position and velocity are included Also, an input and control system using a joystick was developed, and the simulator was applied to the input and control of the simulator. Moreover, an integrated navigation control system was designed, and its performance was validated by a way-point simulator including a PI-based fuzzy control law.

Self learning fuzzy controllers using iterative learning tuner

This paper describes the design of an adaptive fuzzy controller using iterative learning to tune input membership functions and scaling factor(s). The control scheme consists of a fuzzy controller and learning control laws. People's perception about the meaning of a linguistic variable differs from person to person or even from expert to expert. This difference in perception usually leads to different fuzzy control designs. Some where within these designs lays the required design which meets a specific performance criterion. This paper proposes an approach to tackle this uncertainty in perception, to find the required design using membership function modification. The membership function is adaptively adjusted through iterative learning technique. The results show that the scheme is robust, cost effective and very simple to implement. It makes use of the nonlinearity inherent in the fuzzy systems. This scheme can be used to design fuzzy controllers for different plants by finding the right membership functions to ensure the required design specifications. Designing fuzzy controllers with desired performance specifications is not a trivial task. Even the specification of linguistic variables, key concept in fuzzy system design, can be different from different experts. This scheme tries to fill this gap. Adaptive fuzzy techniques are computationally heavy to implement. The proposed scheme lays out a unique adaptive procedure for designing fuzzy controllers through iterative learning process.

A new approach to fuzzy modeling and control for nonlinear dynamic systems: Neuro-fuzzy dynamic characteristic modeling and adaptive control mechanism

The study on nonlinear control system has received great interest from the international research field of automatic engineering. There are currently some alternative and complementary methods used to predict the behavior of nonlinear systems and design nonlinear control systems. Among them, characteristic modeling (CM) and fuzzy dynamic modeling are two effective methods. However, there are also some deficiencies in dealing with complex nonlinear system. In order to overcome the deficiencies, a novel intelligent modeling method is proposed by combining fuzzy dynamic modeling and characteristic modeling methods. Meanwhile, the proposed method also introduces the low-level learning power of neural network into the fuzzy logic system to implement parameters identification. This novel method is called neuro-fuzzy dynamic characteristic modeling (NFDCM). The neuro-fuzzy dynamic characteristic model based overall fuzzy control law is also discussed. Meanwhile the local adaptive controller is designed through the golden section adaptive control law and feedforward control law. In addition, the stability condition for the proposed closed-loop control system is briefly analyzed. The proposed approach has been shown to be effective via an example.