Membership-Function-Dependent Stability Analysis and Control Synthesis of Guaranteed Cost Fuzzy-Model-Based Control Systems

Lam, Hak‐Keung; Xiao, Bo; Yu, Yan; Yin, Xiuxing; Han, Hugang; Tsai, Shun-Hung; Chen, Chin‐Sheng

doi:10.1007/s40815-016-0162-4

Cited by 21 publications

(5 citation statements)

References 42 publications

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“…The PI and the FLC with metaheuristic adjustment are used again in both plants of the systems, it is possible to observe the controllers in the different plants of the systems have very similar characteristics. The parameters of the FLC tuning by means of the optimization techniques have the ability to adapt to the required parameter setting requirements, virtually eliminating the requirement additional adjustments when compared to the conventional tuning methods (Lam et al, 2016 ).…”

Section: Resultsmentioning

confidence: 99%

A PLC-based Fuzzy Logic Control with Metaheuristic Tuning

Silva¹,

Marques²,

Aquino³

et al. 2019

SIC

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In recent times, artificial intelligence applications in the field of dynamic systems control have proved to be an efficient tool for the improvement of processes. Programmable Logic Controller-based control systems are found in the vast majority of industrial automation systems. In this paper, a fuzzy controller approach is proposed to enhance the design of industrial control systems using commercial PLCs. The fuzzy controller is tuned by artificial intelligent techniques, and the performances of different fuzzy controller-tuning methods are compared, including the metaheuristic ones. The results shown in this paper validate the proposal that metaheuristics tuning methods of fuzzy controller have better performance in intelligent automation.

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Section: Resultsmentioning

confidence: 99%

A PLC-based Fuzzy Logic Control with Metaheuristic Tuning

Silva¹,

Marques²,

Aquino³

et al. 2019

SIC

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show abstract

“…Matrices

E

and

C

are time constant and invariable. Such that the system dynamics can be described as

({, \begin{matrix} E \dot{x} false(t false) = & 0true \sum_{i = 1}^{r} m_{i} (v) {A_{i} x false(t false) + B_{i} u}, \\ y = & C x (t), \end{matrix})

where

\sum_{i = 1}^{r} m_{i} false(v false) = 1

\sum_{i = 1}^{r} {\overset{m}{true}}_{i} false(v false) = 0

[34–36].…”

Section: Model Dynamicsmentioning

confidence: 99%

Fuzzy model‐based multi‐objective dynamic programming with modified particle swarm optimization approach for the balance control of bicycle robot

Zhao

Chen

et al. 2021

IET Control Theory & Appl

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Existing studies for the balance control of unmanned bicycle robots only consider constant forward velocity and a single optimal objective that cannot be applied to the complex motion situation. To balance the bicycle robot with time‐varying forward velocity, only with the steering actuator, the multiple objective optimal balance control issue is studied here. A fuzzy state‐space model under different forward velocities is firstly offered based on the non‐linear Euler–Lagrange model. Based on this, a closed‐loop equation under a fuzzy controller is verified. To regulate the feedback gain of the fuzzy controller, a modified particle swarm optimization (MPSO) algorithm with two stages is proposed. In the MPSO's second stage, a novel objective fitness function, consisting of multiple objectives and combining the conventional Hurwitz stability analysis criterium, is designed. Procedures for the MPSO dynamic programming approach are presented. By two examples, the efficiency of the MPSO algorithm, for time‐varying and time‐constant velocity situations, and faster capacity for iteration convergence, are examined.

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“…However, it should be pointed out that the MFI stability conditions may bring great conservatism, due to ignoring characteristic information of the nonlinear plant expressed via membership functions. Hence, we shall adopt the MFD analysis approaches as in [34], [46]- [48] to deal with the membership-functions in (30) by approximating the original membership functions h ij (x(k)) h i (x(k))h j (x(k)) with the piecewise linear membership functions (PLMFs) h ij (x(k)) [34], [46], which approximate these infinite number of inequalities ( 30) by finite ones. The idea of constructing PLMFs is to sample the original membership functions, and then, linear interpolation is employed to approximate the original membership functions based on sample points.…”

Section: B Reachability Analysismentioning

confidence: 99%