Hardware design of asynchronous fuzzy controllers

Fuzzy logic control based on the Takagi–Sugeno inference method has been applied for the yeast alcoholic fermentation running in a continuous-time biochemical reactor in this paper. A type-1 fuzzy PID controller was designed to temperature control in a biochemical reactor. The fuzzy PID controller was also designed using type-2 fuzzy sets. The advantage of the fuzzy control is that it can be used very successfully for control of strongly non-linear processes and processes that are difficult to model because of complicated reaction kinetics. Obtained simulation results confirm this fact. The disadvantage of the fuzzy control design lies in the time-consuming tuning of controllers. The subtractive clustering method was used to identify the rule base. This approach was chosen to minimize the number of rules of the designed fuzzy logic controllers and to simplify the fuzzy controller design. Simulation results confirm that fuzzy PID controllers can assure better performance than conventional PID controllers. Yeast alcoholic fermentation is an exothermic process and the cooling is necessary to maintain the optimal temperature in the reactor. Fuzzy PID control reduces coolant consumption in comparison with conventional PID control and so it assures energy-efficient control of continuous-time alcoholic fermentation. Using type-2 fuzzy sets in the fuzzy PID controller design is very promising as the type-2 fuzzy controller offers even better results than the type-1 fuzzy controller.

This paper presents a method for fuzzy model reference adaptive control system design for a partially known multi-input multi-output (MIMO) linear dynamic plant. Differential or convolutional equations can describe the plant to be controlled, and the fuzzy controller is assumed to be from the MIMO sector-bounded nonstationary nonlinearities class. The task is to obtain robust adaptive stabilization. Some absolute stability theorems and integral evaluations of the state and control vectors are applied in the MIMO fuzzy adaptive controller design procedure for the first time. A method for automatically obtaining a near-optimal nonlinear dynamic reference model is described. A practical step-by-step procedure of the MIMO adaptive fuzzy controller design is proposed, resulting in highly interpretable MIMO fuzzy control rules based on triangular fuzzy sets and strong triangular fuzzy partition. An example of the fuzzy controller design according to the proposed procedure is given.

Fuzzy control as a method used in control design has been predominant when the plant mathematical model is unavailable. Granular computing has been seen as a process which speeds up the fuzzy methodology when combined to the fuzzy control design. Inverted pendulum control and stabilization are a classical problem investigated in this work. The stabilization of the pendulum is achieved with an advanced fuzzy granular computing-based controller under the influence of time delay. The controller which was a combination of fuzzy type-2 controller along with classical PID controller achieved the control. However, it was seen that the impact of time delay with advanced fuzzy controllers is significant even if there was a unit transportation delay in the system the output response suffers which becomes a major drawback and which indicates the need for possible boundaries in time delay systems.

An efficient genetic reinforcement learning algorithm for designing fuzzy controllers is proposed. The genetic algorithm (GA) adopted is based upon symbiotic evolution which, when applied to fuzzy controller design, matches well with the local mapping property of a fuzzy rule. Using this symbiotic-evolution-based fuzzy controller (SE-FC) design method, the number of control trials as well as consumed CPU time are reduced considerably as compared to traditional GA-based fuzzy controller design methods and other types of genetic reinforcement learning schemes. The proposed SE-FC design method has been applied to the cart-pole balancing system. Efficiency and superiority of the proposed SE-FC have been verified from this problem and from comparisons with the traditional GA-based fuzzy systems.

This paper includes the design and implementation of a fuzzy logic controller for level control system to overcome the limitations of the conventional controllers. Mostly preferred controller is a Proportional Integral Derivative (PID) controller which has fixed control parameters. Selection of wrong parameters in a PID design results into uncontrollable process. But fuzzy logic controller is a rule based. These rules are designed on the basis of heuristic information provided by an operator. Process model is identified and used for the design of Proportional Integral (PI) and fuzzy controllers by using MATLAB. To evaluate the performance of both the controllers, it is tested on a real time system with the help of Real Time Workspace (RTW) module of a MATLAB. From real time results it is understood that, fuzzy controller provides more stable and improved output for level control. Even fuzzy controller has ability to remove overshoot which is present in PI controller. Also it requires less settling time than PI controller. Hence fuzzy control system is more relevant control strategy to control the level.

The intelligent transportation is one of current scientific research hot spots. This article takes the fuzzy control as a foundation, analyzes the problem of intersection traffic signal control, proposes the multiple phase fuzzy control algorithm of intersection signal period and the design of fuzzy controller. It takes motorcade's length and the next phase difference of motorcade length as the fuzzy control variable, according to the fuzzy rule, synthesisly considered the assigned green time, thus forms one real-time, accurate, the highly effective comprehensive transportation system. The simulation result indicated that this plan approaches human's decision-making process, surpassed the timed control, can effectively alleviate the jam of intersection.

There are two main drawbacks in fuzzy control: 1) the design of fuzzy controllers is usually performed in an ad hoc manner where it is often difficult to choose some of the controller parameters; and 2) the fuzzy controller constructed for the nominal plant may later perform inadequately if significant and unpredictable plant parameter variations occur. In this paper we illustrate these two problems on a two-link flexible robot testbed by: 1) developing, implementing, and evaluating a fuzzy controller for the robotic mechanism, and 2) illustrating that payload variations can have negative effects on the performance of a well designed fuzzy control system. Next, we show how to develop and implement a fuzzy model reference learning controller for the flexible robot and illustrate that it can automatically synthesize a rule-base for a fuzzy controller that will achieve comparable performance to the case where it was manually constructed, and automatically tune the fuzzy controller so that it can adapt to variations in the payload. >

This paper proposes a robust design of Hybrid Fuzzy Controller for speed and steering angle control in an Intelligent Autonomous Parking System (IAPS). The Hybrid Fuzzy Controller consists of a Base Fuzzy Controller (BFC) and a Supervisory Fuzzy Decision Tree Controller (SFDTC). The BFC evolves from previous work on fuzzy logic control for unmanned parking and it ensures that optimal parking trajectory is achieved with minimal computational cost. SFDTC further increases the system robustness when there is noise in the operating environment. The design of SFDTC combines Decision Tree theory and fuzzy inference mechanism. A data training process is also formulated to achieve better control performance. As a result, IAPS equipped with the new Hybrid Fuzzy Controller with Fuzzy Decision Tree (HFC-FDT) demonstrates optimal performance with faster convergence and minimal deviation from optimal parking trajectory. The detailed design of Supervisory Fuzzy Decision Tree Controller is presented in this paper with a MATLAB simulated experiment which concludes the superior performance of the new design.

PID plus fuzzy controller structures as a design base for industrial applications

This paper presents fuzzy logic based velocity feedback control for active vibration control of beams. The controller is first developed for a single degree of freedom spring mass system. Rule base consisting of three simple rules based on velocity is used. It is found theoretically as well as experimentally, that for the same settling time maximum applied force required by fuzzy logic controller is much less than that required by direct negative velocity feedback control. The fuzzy controller so developed is then applied for active vibration control of beams. The controller is implemented experimentally on a test beam and the results are found satisfactory. The test system consists of a cantilevered beam with piezoelectric sensor and actuator patches mounted in collocated fashion. The fuzzy logic controller is based on modal velocity of the beam. Modal velocity of the beam acts as an input to the fuzzy controller and actuation force is output from the inference engine. The issues related to design of fuzzy logic controller based on velocity are discussed.

This paper discusses the problem of suboptimal local piecewise H ∞ fuzzy control of quasi-linear spatiotemporal dynamic systems with control magnitude constraints. A Takagi-Sugeno fuzzy partial differential equation (PDE) model with space-varying coefficient matrices is first assumed to be derived for exactly describing nonlinear system dynamics. In the light of the fuzzy model, a local piecewise fuzzy feedback controller is then constructed to guarantee the exponential stability with a prescribed H ∞ disturbance attenuation level for the resulting closed-loop system, while the control constraints are also ensured. A sufficient condition on the existence of such fuzzy controller is developed by the Lyapunov direct method and an integral inequality and presented in terms of space algebraic linear matrix inequalities (LMIs) coupled with LMIs. By virtue of extreme value theorem, a suboptimal-constrained local piecewise H ∞ fuzzy control design in the sense of minimizing the disturbance attenuation level is formulated as a minimization optimization problem with LMI constraints. Finally, the proposed method is applied to solve the feedback control of a quasi-linear FitzHugh-Nagumo equation with space-varying coefficients, and simulation results show its effectiveness and merit.

To solve the non-linear system control problem, a new self-adaptive fuzzy controller with two stages variable universe is proposed. In place of the conventional function models to obtain the contraction-expansion factors, the novel fuzzy control algorithm adjusts its universe by another fuzzy controller according to the values of the input-output variable and a fuzzy control rules list. The simulation results show that compared with the conventional fixed universe fuzzy controller, the proposed two stages variable universe fuzzy controller can work well to adjust the contraction-expansion factors, and improve response speed and control precision of the system. In addition, the novel method simplifies the design of variable universe fuzzy controller based on function models.

This paper proposes the design of fuzzy logic controllers to revolve a wheeled mobile robot (WMR) through digital images, and the design of WMR anti-collision system. After the image processing, the authors utilize the center of object image to design the fuzzy logic controllers that incorporate expert knowledge in terms of 9 linguistic rules to drive the WMR to revolve. By the way, the WMR can execute auto moving and anti-collision via infrared sensors. The program codes of fuzzy logic controllers are implemented on a PC which could send all the motion commands to the WMR by wireless communication.

Since the appearance of VHDL, the design of large blocks of digital circuits is possible at a reasonable cost. The advantages of these high level languages are obvious; among them, they increase the chances of design automation. This paper present a tool aimed at automating the design of digital neural and fuzzy controllers. Note that, in spite of the different neural and fuzzy structures proposed in the literature, only a few of them are used in real applications. Hence, the architecture of these controllers is repeated quite frequently, so that automation is possible. Automation significatively shortens the design time, and reduces design cost. Different tools have recently been proposed to automate the hardware design of neural and fuzzy controllers. Some of them use a digital approach [1], [2], an analog approach [3] or a mixed-signal approach [4].

Based on parameter self-tuning fuzzy PID controller, a fuzzy inference method is utilized to realize automatic regulating PID parameter, and the application of the controller in a system is studied with MATLAB in this paper. The combination of PID controller system and fuzzy controller system combines the convenience of PID control together with the flexibility of fuzzy control, and takes advantage of the traditional control, which has a great practical significance. The results of simulation show that fuzzy PID parameter self-tuning controller has a better control effect than the traditional one, and can improve the static and dynamic properties of the system well. DOI: http://dx.doi.org/10.11591/telkomnika.v14i2.7674