Fuzzy Control Solutions Research Articles

Fuzzy-based adaptive event-triggered control for nonlinear cyber-physical systems against deception attacks via a single parameter learning method

This paper investigates the adaptive fuzzy control issue of cyber-physical systems (CPSs) against unknown deception attacks and external disturbance. Based on the backstepping design framework, the uncertainty term composed of the approximation error term generated by the fuzzy logic system, the external disturbance and the deception attack signals are regarded as a whole in the recursive design process. Then, by combining the single parameter learning method with the adaptive fuzzy technique, the uncertain terms containing approximation error, disturbance and injection attacks can be transformed into the form of linear parameterization with only one unknown scalar parameter, which greatly reduce the calculation process. In addition, the dynamic event-triggered adaptive control technology can be integrated into the control design to reduce the amount of data transmission. Theoretical analysis shows that all the signals in the closed-loop system are bounded under the proposed control scheme, and the Zeno behavior is excluded. Finally, the two-stage chemical reactor and mass-spring-damper systems are employed to demonstrate the validity of the proposed adaptive fuzzy control solution.

An original fuzzy control approach for active anti-roll bars to prevent rollover.

In this article, the author introduces a new fuzzy control solution to direct active anti-roll bars (hydraulic stabilizer bars) in order to enhance the vehicle's roll stabilization efficiency. The original fuzzy algorithm designed in this work can satisfy all the roll stability, comfort, and response speed requirements, while previous algorithms could only meet one of these criteria. In addition, a fully dynamic model is established to simulate the vehicle's roll oscillations instead of only using a simple half-dynamic model combined with the single-track dynamic model. The calculation and simulation processes take place in the Simulink environment. Two cases of steering are used as input to the simulation problem; the car's speed is gradually increased through three levels. According to results of research, the roll angle and roll index of the car increase as the speed and steering angle increase. The interaction between the road and wheel decreases sharply as the roll angle increases, which can lead to a rollover. In the first case, the rollover occurs only when the car travels at v3 without the stabilizer bar and has a maximum roll angle of 9.81°. In the second case, this occurs for the (None) situation when traveling at speed v1 with a maximum roll angle of 9.52° and for the (Passive) situation when traveling at speed v2 with a peak value of 11.93°. Meanwhile, the vehicle's stability is still well guaranteed when utilizing active anti-roll bars controlled by an original fuzzy algorithm.

Event-based adaptive fuzzy tracking control for nonlinear systems with guaranteed performance

This article is concerned with the problem of event-based adaptive prescribed time tracking control for a class of uncertain strict-feedback nonlinear systems. To ensure that the considered system possesses the user-specified tracking performance, an improved error transformation is constructed and incorporated with a novel fixed-time performance function with an infinite initial value, completely removing the restriction that the tracking error is less than the pre-assigned performance boundary in the initial interval. Then, the utilization of command filtered technique and two single parameter estimators severally related to the upper bounds of fuzzy logic systems-based unknown constant parameters and external disturbances not only obviates the “explosion of complexity” issue but also simplifies the control design procedure. On this basis, by skillfully synthesizing adaptive technology and event-triggered mechanism within the framework of command filtered backstepping technique, a novel adaptive fuzzy event-triggered control solution is presented to save the limited network resources. Under the developed controller, the tracking error is steered to converge to an adjustable asymmetric region within a pre-specified time, and the boundedness of all closed-loop signals is ensured. Finally, simulation studies are performed to demonstrate the effectiveness and practicability of the developed control solution.

Nonlinear Adaptive Fuzzy Control Design for Wheeled Mobile Robots with Using the Skew Symmetrical Property

This research presents a nonlinear adaptive fuzzy control method as an analytical design and a simple control structure for the trajectory tracking problem in wheeled mobile robots with skew symmetrical property. For this trajectory tracking problem in wheeled mobile robots, it is not easy to find an analytical adaptive fuzzy control solution due to the complicated error dynamics between the controlled wheeled mobile robots and desired trajectories. For deriving the analytical adaptive fuzzy control law of this trajectory tracking problem, a filter link is firstly adopted to find the solvable error dynamics, then the research is based on the skew symmetrical property of the transformed error dynamics. This proposed nonlinear adaptive fuzzy control solution has the advantages of low computational resource consumption and elimination of modeling uncertainties. From the results for tracking two simulation scenarios (an S type trajectory and a square trajectory), the proposed nonlinear adaptive fuzzy control method demonstrates a satisfactory trajectory tracking performance for the trajectory tracking problem in wheeled mobile robots with huge modeling uncertainties and outperforms the existing H2 control method.

Hardware–In–The–Loop Assessment of Robust Fuzzy Control Solutions for Hydroelectric and Wind Turbine Models

The interest towards renewable energy resources is increasing, and in particular it concerns wind and hydro powers, where the key point regards their efficient conversion into electric energy. To this end, control techniques can be used to meet this purpose, especially the ones relying on fuzzy models, due to their capabilities to manage nonlinear dynamic processes working in different conditions, and affected by faults, measurement errors, uncertainty and disturbances. The design methods addressed in this paper were already developed and validated for wind turbine plants, and important results can be achieved from their appropriate design and application to hydroelectric plants. This is the key issue of the paper, which recalls some considerations on the proposed solutions, as well as their validation to these energy conversion systems. Note that works available in the related literature that consider both wind and hydraulic energy conversion systems investigate a limited number of common issues, thus leading to little exchange opportunities and reduced common research aspects. Another important point addressed in the paper is that the proposed control design solutions are able to take into account the different working conditions of these power plants. Moreover, faults, uncertainty, disturbance and model-reality mismatch effects are also considered when analyzing the reliability and robustness features of the derived control schemes. To this end, proper hardware-in-the-loop tools are considered to verify and validate the developed control schemes in more realistic environments.

Tracking control optimization scheme for a class of partially unknown fuzzy systems by using integral reinforcement learning architecture

In this paper, a novel fuzzy integral reinforcement learning (RL) based tracking control algorithm is first proposed for partially unknown fuzzy systems. Firstly, by using the precompensation and augmentation techniques, a new augmented fuzzy tracking system is constructed by combining the fuzzy logic model and desired reference trajectory, where the solution of actual working feedback control policy is converted into a virtual optimal control problem. Secondly, to overcome the requirements of exact original system information, the integral RL technique is utilized to learn the fuzzy control solution, which relaxes the repeatedly transmissions of system matrices during the solving process. Thirdly, compared with the existing standard solution, some crucial and strict aforementioned assumptions are removed and the system can be partially unknown by using the designed algorithm. Besides, under the novel fuzzy control policy, the tracking objective is achieved and the stability is guaranteed by Lyapunov theory. Finally, the developed integral RL tracking control algorithm for partially unknown systems is applied in a mechanical system and the simulation results demonstrate the effectiveness of the proposed new method.

Ant Colony Optimized Fuzzy Control Solution for Frequency Oscillation Suppression

This paper presents a novel approach in addressing a critical power system issue, i.e., automatic generation control (AGC) in a smart grid scenario. It proposes the design and implementation of an optimized fuzzy logic controller (FLC) for AGC of interconnected power network. There are three different sources of power generation considered in the two-area interconnected model of power system network. First area is equipped with a single reheat thermal unit and a superconducting magnetic energy storage (SMES) unit, while another area has a hydro-unit with SMES. A multi-stage optimization strategy for the optimal solution of FLC for tie-line and frequency oscillation suppression is proposed in this paper using an ant colony optimization technique. The optimization of FLC is carried out in four different stages. The first stage is the optimization of range of input and output variables; the second stage is the optimization of membership function; the third and fourth stages are the optimization for rule base and rule weight optimization, respectively. The performance of the proposed controller is also compared with another control approaches to stabilize Ptie-line and Δf oscillations; these are the Ziegler–Nichols-tuned proportional–integral–derivative (PID) controller and genetic algorithm optimized PID controller. A comprehensive analysis of the traditional techniques and proposed techniques is presented on the basis of major dynamic performance parameters, i.e., settling time and peak overshoot.

Robust Fuzzy Fault Detection and Isolation Approach Applied to Surge in Centrifugal Compressor Modeling and Control

This work presents the results of applying an advanced fault detection and isolation technique to centrifugal compressor; this advanced technique uses physics models of the centrifugal compressor with a fuzzy modeling and control solution method. The fuzzy fault detection and isolation has become an issue of primary importance in modern process engineering automation as it provides the prerequisites for the task of fault detection. In this work, we present an application of this approach in fault detection and isolation of surge in compression system. The ability to detect the surge is essential to improve reliability and security of the gas compressor plants. We describe and illustrate an alternative implementation to the compression systems supervision task using the basic principles of fuzzy fault detection and isolation associated with fuzzy modeling approach. In this supervision task, the residual generation is obtained from the real input-output data process and the residual evaluation is based on fuzzy logic method. The results of this application are very encouraging with relatively low levels of false alarms and obtaining a good limitation of surge in natural gas pipeline compressors.

Fuzzy Control System Performance Enhancement by Iterative Learning Control

This paper suggests low-cost fuzzy control solutions that ensure the improvement of control system (CS) performance indices by merging the benefits of fuzzy control and iterative learning control (ILC). The solutions are expressed in terms of three fuzzy CS (FCS) structures that employ ILC algorithms and a unified design method focused on Takagi-Sugeno proportional-integral fuzzy controllers (PI-FCs). The PI-FCs are dedicated to a class of servo systems with linear/linearized controlled plants characterized by second-order dynamics and integral type. The invariant set theorem by Krasovskii and LaSalle with quadratic Lyapunov function candidates is applied to guarantee the convergence of the ILC algorithms and enable proper setting of the PI-FC parameters. The linear PI controller parameters tuned by the extended symmetrical optimum method are mapped onto the PI-FC ones by the modal equivalence principle. Real-time experimental results for a dc-based servo speed CS are included.

Design and Experiments for a Class of Fuzzy Controlled Servo Systems

This paper proposes a new fuzzy control solution employing 2-DOF proportional-integral-fuzzy controllers dedicated to a class of servo systems. The controlled plants in these systems, widely used in mechatronics applications, can be characterized by second-order dynamics with integral type. The original design method suggested here starts with linear design results in terms of the extended symmetrical optimum method accompanied by an iterative feedback tuning (IFT) algorithm. Next, these results are transferred to the fuzzy case by the modal equivalence principle. The convergence of the IFT algorithm is guaranteed by the derivation of sufficient global asymptotic stability conditions based on Krasovskii-LaSalle's invariant set theorem with quadratic Lyapunov function candidate. Real-time experimental results corresponding to a low-cost fuzzy controlled servo system validate the theoretical approaches.

Linear and fuzzy control solutions for tape drives

This paper suggests new linear and fuzzy control solutions for tape drive-based transport systems. The linear control solution involves a cascade control system with inner state-feedback controller and outer PI controllers. The fuzzy control solution employs a Takagi–Sugeno fuzzy controller to schedule 11 separately designed linear controllers depending on measured take-up reel radius. Two simulation scenarios illustrate the control system performance enhancement ensured by the fuzzy control solution.

Fuzzy control of inverted pendulum and concept of stability using Java application

In this paper, a fuzzy controller for an inverted pendulum system is presented in two stages. These stages are: investigation of fuzzy control system modeling methods and solution of the “Inverted Pendulum Problem” by using Java programming with Applets for internet based control education. In the first stage, fuzzy modeling and fuzzy control system investigation, Java programming language, classes and multithreading were introduced. In the second stage specifically, simulation of the inverted pendulum problem was developed with Java Applets and the simulation results were given. Also some stability concepts are introduced.

LOW COST FUZZY CONTROLLED SERVO SYSTEMS IN MECHATRONIC SYSTEMS

The paper proposes original low cost fuzzy control solutions for controlling servo systems in mechatronic systems. In this situation the controlled plants can be characterized well by simplified mathematical models of second-order integral type. The development is done by using the linear case results on the basis of the Extended Symmetrical Optimum (ESO) method and of Iterative Feedback Tuning (IFT) techniques, and by the transfer of these results to the fuzzy case. There are proposed two fuzzy control system structures employing a basic PI-fuzzy controller. Real-time experimental results corresponding to a nonlinear servo system validate the proposed fuzzy control solutions.

Cascaded Fuzzy Congestion Controller for TCP/IP Traffic

This paper presents a fuzzy congestion control solution for active queue management in wireline packet switched networks, that use TCP to solve end-to-end flow control. Queue management is a trade-off between link utilisation, delay and the loss of packets. The targets of it are to reduce the average queue length in routers, decrease the end-to-end delay and reduce the packet loss probability by preventing buffer overflows. In the presented solution, a fuzzy controller regulates the rejection probability of received packets in routers to prevent buffer overflow and the rejection of all packets during congestion. Using simulations a fuzzy controller is compared with the traditional random early discard (RED) algorithm. Pareto distributed traffic was used in the comparison for the evaluation of controllers. The results showed that the fuzzy controller decreased the packet loss rate with lower buffer occupation and increased the link utilisation and throughput. It also decreased control traffic between the transmitting nodes and routers via decreasing the number of ICMP (Internet Control Message Protocol) messages. In addition, by keeping the average queue size small, the developed algorithm had the ability to accommodate itself more effectively to very bursty traffic than the RED algorithm without dropping packets.

Control solutions in mechatronics systems

This paper presents control solutions dedicated to a class of controlled plants widely used in mechatronics systems, characterized by simplified mathematical models of second-order and third-order plus integral type. The conventional control solution is focused on the Extended Symmetrical Optimum method proposed by the authors in 1996. There are proposed six fuzzy control solutions employing PI-fuzzy controllers. These solutions are based on the approximate equivalence in certain conditions between fuzzy control systems and linear ones, on the application of the modal equivalence principle, and on the transfer of results from the continuous-time conventional solution to the fuzzy solutions via a discrete-time expression of the controller where Prof. Milic R. Stojic's book [1] is used. There is performed the sensitivity analysis of the fuzzy control systems with respect to the parametric variations of the controlled plant, which enables the development of the fuzzy controllers. In addition, the paper presents aspects concerning Iterative Feedback Tuning and Iterative Learning Control in the framework of fuzzy control systems. The theoretical results are validated by considering a real-world application.

On some Low Cost Fuzzy Control Solutions for Third-Order Integral Actuators

The paper proposes four low cost fuzzy control solutions applicable to the control of third-order integral actuators. These solutions are developed by incorporating two input-single output fuzzy controllers in the conventional state feedback control structures for the sake of perfonnance enhancement while maintaining the low cost. There is derived an original systematical development method for the proposed fuzzy control solutions. The case study presented in the paper deals with position control of an electro-hydraulic system applicable to actuators as part of the speed control systems of large power hydrogenerators. Finally, the performance enhancement ensured by the fuzzy control solutions with respect to the conventional ones is highlighted.

Development of Conventional and Fuzzy Controllers for Output Coupled Drive Systems and Variable Inertia

The control of output coupled drive systems having variable inertia is difficult due to the presence of possible vibrations due to the elastic coupling, the variance of plant parameters, and the effect of output coupling of the control subsystems. Typical applications for such complex large-scale multivariate systems are the fields of rolling mills, of winding mechanisms, and of electrical driven transportation systems. The paper presents results concerning the development of conventional and fuzzy control solutions for a drive system with two output coupled motors applicable to the rolls of a hot rolling mill and to a variable inertia strip winding system.

Fuzzy control in process automation†

Abstract Fuzzy logic provides an extremely practical method of incorporating empirical process knowledge and linguistically formulated control strategies appropriately into process automation applications. This potential makes fuzzy logic controllers attractive for a wide range of industrial processes. In view of its significant appeal, the obvious way of implementing practical fuzzy control solutions quickly and successfully is by integrating the fuzzy functions into existing systems and providing software tools for support. A universal fuzzy function block concept is introduced which gives process control systems comfortable access to the world of fuzzy logic. The three new function blocks FUZ, RULE and DFUZ enable fuzzy logic to be integrated into existing system environments flexibly and with a high degree of acceptance. Combined with the fuzzy tool SIFLOC TM, these software blocks form a powerful enhancement to conventional control systems for a large number of tasks that have hitherto evaded satisfa...