Actuator Saturated Fuzzy Controller Design for Interval Type-2 Takagi-Sugeno Fuzzy Models with Multiplicative Noises

Chang, Wen‐Jer; Lin, Yu‐Wei; Lin, Yann-Horng; Pen, Chin-Lin; Tsai, Ming-Hsuan

doi:10.3390/pr9050823

Cited by 20 publications

(6 citation statements)

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“…In [15], the authors address the problem of actuator saturation in controller design. For this purpose, they present a design method of fuzzy controllers subject to actuator saturation for nonlinear systems with uncertain parameters.…”

Section: Of 30mentioning

confidence: 99%

Fuzzy Control Strategies Development for a 3-DoF Robotic Manipulator in Trajectory Tracking

Kern,

Marrero,

Urrea

2023

Processes

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This research delves into the development and evaluation of two distinct controllers for a 3-DoF robotic arm in the context of Industry 4.0. Two primary control strategies are presented in the study. The first is a Fuzzy Logic Controller that utilizes joint position error and its derivative as inputs, employing a set of 9 control knowledge rules. The second is an Adaptive Neuro-Fuzzy Inference System (ANFIS) Controller, trained to learn the inverse dynamic model of the robot through a structured dataset. The research emphasizes the importance of accurate parameter tuning and data acquisition to achieve optimal control system performance. Extensive experimentation was conducted to evaluate the controllers’ performance in trajectory tracking and their response against external disturbances, such as load variations. The controllers exhibited remarkable precision and proficiency in tracking reference trajectories, with minimal deviations, overshoots, or oscillations. A quantitative analysis using performance indices such as root mean square error (RMSE) and the integral of the absolute value of the time-weighted error (ITAE) further confirmed the controllers’ effectiveness. Notably, the ANFIS Controller consistently outperformed the Fuzzy Logic Controller, demonstrating superior precision in trajectory tracking. The study underscored the importance of selecting the right control method and obtaining high-quality training data. Challenges in parameter tuning for Fuzzy Logic Controllers and potential time constraints in training ANFIS were discussed. The findings have significant implications for advancing robotic control systems, particularly in the era of Industry 4.0.

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Section: Of 30mentioning

confidence: 99%

Fuzzy Control Strategies Development for a 3-DoF Robotic Manipulator in Trajectory Tracking

Kern,

Marrero,

Urrea

2023

Processes

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“…According to Assumption 1, E Rij is turned invertible by gains F dj and L di . The following equation can thus be obtained from Equation (10).…”

Section: Assumption 2 ([30]mentioning

confidence: 99%

“…As a result, researchers have extensively investigated control synthesis for nonlinear singular systems. T-S fuzzy models [8][9][10][11][12][13] are an effective approach for describing nonlinear systems. By constructing several linear subsystems using fuzzy sets and IF-THEN rules, the final output of the T-S Fuzzy Singular System (T-SFSS) [14][15][16][17][18][19] can be obtained through membership functions.…”

Section: Introductionmentioning

confidence: 99%

Observer-Based Fuzzy Control of Uncertain Nonlinear Singular Systems under Multi-Performance Requirements

2023

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This paper discusses an observer-based fuzzy control problem for uncertain nonlinear singular systems under Multi-Performance Requirements (MPRs). The approach used in the paper is to model the system using a Takagi–Sugeno (T-S) fuzzy model that can be analyzed using linear control theories. The proposed control scheme is based on the Parallel Distributed Compensation (PDC) approach and Proportional Derivative (PD) control scheme. The goal is to design an observer-based fuzzy controller that achieves stability of the system and also satisfies the Guarantee Cost Control (GCC) constraint while maintaining a desired passive constraint. The stability analysis is performed using Lyapunov theory, and the sufficient conditions are transformed into a Linear Matrix Inequality (LMI) form using a Shur Complement, free-weighting matrix method and Singular Value Decomposition (SVD) techniques. The LMI conditions are then solved using convex optimization algorithms. Finally, the proposed control method is validated using a bio-economic system to demonstrate its effectiveness.

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“…If there are uncertainties in the dynamics of each boiler unit, the purpose will be difficult to achieve and the performance of whole multi-boiler systems may be deteriorated. Thus, the interval type-2 (IT-2) T-S fuzzy model has been developed with the IT-2 membership function, which can represent the uncertainties in the nonlinear system more completely [36][37][38]. Based on the IT-2 T-S fuzzy model, the designed IT-2 fuzzy controller can better deal with the uncertainties problem.…”

Section: Introductionmentioning

confidence: 99%

Solving the Formation and Containment Control Problem of Nonlinear Multi-Boiler Systems Based on Interval Type-2 Takagi–Sugeno Fuzzy Models

Lin

Chang

2022

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An interval type-2 (IT-2) fuzzy control design method is developed to solve the formation and containment problem of nonlinear multi-boiler systems. In most practical industrial systems such as airplanes, vessels, and power plants, the boiler system often exists as more than one piece of equipment. An efficient control theory based on the leader-following multi-agent system is applied to achieve the control purpose of multiple boiler systems simultaneously. Moreover, a faithful mathematical model of the nonlinear boiler system is extended to construct the multi-boiler system so that the dynamic behaviors can be accurately presented. For the control of practical multi-agent systems, the uncertainties problem, which will deteriorate the performance of the whole system greatly, must be considered. Because of this, the IT-2 Takagi–Sugeno (T–S) fuzzy model is developed to represent the nonlinear multi-boiler system with uncertainties more completely. Based on the fuzzy model, the IT-2 fuzzy formation and containment controllers are designed with the imperfect premise matching scheme. Thus, the IT-2 fuzzy control method design can be more flexible for the nonlinear multi-boiler system. Solving the formation problem, a control method without the communication between leaders differs from the previous research. Since leaders achieve the formation objective, the followers can be forced into the specific range formed by leaders. Via the IT-2 fuzzy control method in this paper, not only can the more flexible process of the controller design method be developed to solve the uncertainties problem magnificently, but a more cost-effective control purpose can also be achieved via applying the lower rules of fuzzy controllers. Finally, the simulation results of controlling a nonlinear multi-boiler system with four agents are presented to demonstrate the effectiveness of the proposed IT-2 fuzzy formation and containment control method.

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Actuator Saturated Fuzzy Controller Design for Interval Type-2 Takagi-Sugeno Fuzzy Models with Multiplicative Noises

Cited by 20 publications

References 50 publications

Fuzzy Control Strategies Development for a 3-DoF Robotic Manipulator in Trajectory Tracking

Fuzzy Control Strategies Development for a 3-DoF Robotic Manipulator in Trajectory Tracking

Observer-Based Fuzzy Control of Uncertain Nonlinear Singular Systems under Multi-Performance Requirements

Solving the Formation and Containment Control Problem of Nonlinear Multi-Boiler Systems Based on Interval Type-2 Takagi–Sugeno Fuzzy Models

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