In this technical note, a new fault detection design scheme is proposed for interval type-2 (IT2) Takagi-Sugeno (T-S) fuzzy systems with sensor fault based on a novel fuzzy observer. The parameter uncertainties can be captured by the membership functions of the IT2 fuzzy model. The premise variables of the plant are perfectly shared by the fuzzy observer. A stochastic process between the plant and the observer is considered in the system. A fault sensitive performance is established, and then sufficient conditions are obtained for determining the fuzzy observer gains. Finally, simulation results are provided to verify the effectiveness of the presented scheme.

Observer-Based Fault Detection for Nonlinear Systems With Sensor Fault and Limited Communication Capacity

In this paper, the problem of fuzzy control for nonlinear networked control systems with packet dropouts and parameter uncertainties is studied based on the interval type-2 fuzzy-model-based approach. In the control design, the intermittent data loss existing in the closed-loop system is taken into account. The parameter uncertainties can be represented and captured effectively via the membership functions described by lower and upper membership functions and relative weighting functions. A novel fuzzy state-feedback controller is designed to guarantee the resulting closed-loop system to be stochastically stable with an optimal performance. Furthermore, to make the controller design more flexible, the designed controller does not need to share membership functions and amount of fuzzy rules with the model. Some simulation results are provided to demonstrate the effectiveness of the proposed results.

Control of Nonlinear Networked Systems With Packet Dropouts: Interval Type-2 Fuzzy Model-Based Approach

A brief overview on the model-based control and data-driven control methods is presented. The data-driven equivalent dynamic linearization, as a foundational analysis tool of data-driven control methods for discrete-time nonlinear systems, is introduced in detail with motivations and distinct features. The prototype model-free adaptive control schemes by using the dynamic linearization to an unknown nonlinear plant model, as well as the alternative model-free adaptive control methods by using the dynamic linearization to an unknown ideal nonlinear controller, are discussed. Furthermore, the extensions of the dynamic linearization to unknown nonlinear repetitive systems and the corresponding model-free adaptive iterative learning control methods are also overviewed and summarized. This work highlights the characteristics and comments of the different model-free adaptive control schemes in detail to facilitate the understanding of the readers. Finally, some perspectives on data-driven control methods in information-rich age are given.

An Overview of Dynamic-Linearization-Based Data-Driven Control and Applications

This paper is concerned with the adaptive sliding mode control problem of uncertain nonlinear systems. Interval type-2 Takagi–Sugeno (T–S) fuzzy model is employed to represent uncertain nonlinear systems. The input matrices of the nonlinear systems are allowed to be different for the sliding mode controller design. The uncertain parameters are described by the lower and upper membership functions. An integral sliding mode surface is designed for analysis of sliding motion. Based on the sliding mode surface, a novel sliding mode controller is designed to guarantee that the closed-loop system is uniformly ultimately bounded. Some simulation results are given to illustrate the effectiveness of the presented control scheme.

Adaptive Sliding Mode Control for Interval Type-2 Fuzzy Systems

https://isiarticles.com/bundles/Article/pre/pdf/46311.pdf

A review on interval type-2 fuzzy logic applications in intelligent control

This paper focuses on designing interval type-2 (IT2) control for nonlinear systems subject to parameter uncertainties. To facilitate the stability analysis and control synthesis, an IT2 Takagi-Sugeno (T-S) fuzzy model is employed to represent the dynamics of nonlinear systems of which the parameter uncertainties are captured by IT2 membership functions characterized by the lower and upper membership functions. A novel IT2 fuzzy controller is proposed to perform the control process, where the membership functions and number of rules can be freely chosen and different from those of the IT2 T-S fuzzy model. Consequently, the IT2 fuzzy-model-based (FMB) control system is with imperfectly matched membership functions, which hinders the stability analysis. To relax the stability analysis for this class of IT2 FMB control systems, the information of footprint of uncertainties and the lower and upper membership functions are taken into account for the stability analysis. Based on the Lyapunov stability theory, some stability conditions in terms of linear matrix inequalities are obtained to determine the system stability and achieve the control design. Finally, simulation and experimental examples are provided to demonstrate the effectiveness and the merit of the proposed approach.

/pdf/control-design-for-interval-type-2-fuzzy-systems-under-4b1awzvx1b.pdf

Control Design for Interval Type-2 Fuzzy Systems Under Imperfect Premise Matching

"In the modern wind turbine industry, one of the core processes is the assembly of the bolt-nut connections of the hub, which requires tightening bolts and nuts to obtain well-distributed clamping force all over the hub. This force deals with nonlinear uncertainties due to the mechanical properties and it depends on the final torque and relative angular position of the bolt/nut connection. 
This paper handles the control problem of automated bolt tightening processes. To develop a controller, the process is divided into four stages, according to the mechanical characteristics of the bolt/nut connection: a Fuzzy Logic Controller (FLC) with expert knowledge of tightening process and error detection capability is proposed. For each one of the four stages, an individual FLC is designed to address the highly non-linearity of the system and the error scenarios related to that stage, to promptly prevent and avoid mechanical damage.
The FLC is implemented and real time executed on an industrial PC and finally validated. Experimental results show the performance of the controller to reach precise torque and angle levels as well as desired clamping force. The capability of error detection is also validated.
"

Accurate Bolt Tightening using Model-Free Fuzzy Control for Wind Turbine Hub Bearing Assembly

In the wind turbine manufacturing industry, the bolt-nut joint tightening process is one of the core processes in the full production chain and concerned with assembling the hub body, the pitch system and the bearing unit. This operation is currently executed manually with the aid of different tools and gauges; the main disadvantages are a relatively high degree of variability and the necessity to repeat this task several times during a production run to achieve a satisfactory, final tightening torque within a specified angle range. Moreover, the bolt tightening process includes various uncertainties such as the presence of friction forces and the use of different bolt sizes with different stiffness values which make it highly nonlinear and uncertain resulting in a challenging control problem. To facilitate the development of an effective control strategy, we study the bolt tightening process and propose 4 tightening stages, namely, bolt-nut alignment, partial and full engagement and final bolt tightening. Based on the characteristics of each stage, a fuzzy controller is designed for each stage to realize the respective control objectives. A fuzzy error detector incorporating the knowledge of each stage is proposed for early error detection, making use of the input from a torque and encoder (angular position) sensor. Errors can be detected in each stage to interrupt the process and prevent any damage to the system.

Model-free fuzzy tightening control for bolt/nut joint connections of wind turbine hubs

Stability analysis of polynomial fuzzy model-based (PFMB) control systems under the parallel distributed compensation (PDC) design technique is investigated. A new polynomial fuzzy controller (PFC) is introduced to release conservativeness in the existing approaches. Compared to the conventional (PFC), the controller under consideration in this paper has a favorable property which introduces some more variables in the stability conditions such that, the solution of the derived stability conditions can be explored in a larger group of potential solutions. This property may lead to further relax stability conditions. However, the stability conditions will not be in the form of SOS conditions. Hence, particle swarm optimization (PSO) is employed to explore the solution of the derived stability conditions which are in the form of state-dependent BMI. It is shown that the conventional PFC is a special case of that of proposed one. Finally, a simulation example is given to illustrate the effectiveness of the proposed approach.

An approach for stability analysis of polynomial fuzzy model-based control systems

One of the core steps of the assembly of wind turbines is the assembly of the bearings on the wind turbine hub. The hub can contain up to 128 bolt connections to install the bearing blades: nuts need to be precisely tightened to ensure a uniformly distributed clamping force as well as avoiding assembly errors, e.g. nut misalignments. The bolt-nut connection is a non-linear system with uncertainties making it difficult to design a numerical model and PI Gains. This paper presents a novel two-stage Proportional-Integral (PI) controller with assembly error detection capability for bolt tightening process. It is based on the combination of a numerical model (offline training) and a genetic algorithm (GA) for online training on the physical bolt system. Since the model does not include all non-linearity and uncertainties of the physical plant (here the bolt-nut connection), it is used at first to estimate the range of the PI values, followed by a fine tuning of the values online by the GA.

C. Deters

Papers

Control Design for Interval Type-2 Fuzzy Systems Under Imperfect Premise Matching

Accurate Bolt Tightening using Model-Free Fuzzy Control for Wind Turbine Hub Bearing Assembly

Model-free fuzzy tightening control for bolt/nut joint connections of wind turbine hubs

An approach for stability analysis of polynomial fuzzy model-based control systems

Model-Based Self-Tuning PI Control of Bolt-Nut Tightening for Wind Turbine Bearing Assembly