PID self-tuning control using a fuzzy adaptive mechanism

He, Shiyi; Shi, Tan; Xu, Feng-Lan; Wang, P.Z.

doi:10.1109/fuzzy.1993.327402

Cited by 28 publications

(18 citation statements)

References 2 publications

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“…-Calculate the approximated controller parameters h À m by SVR estimator trained at previous step (n À 1) via (27).…”

Section: Adaptive Control Algorithm For the Generalized Str Based On Svrmentioning

confidence: 99%

“…Step 5: Prediction step for trained parameter estimator (h þ m ) and computation of control input by trained estimator (u þ n ) -Calculate the controller parameters by trained SVR estimator via (27).…”

Section: Nþ1mentioning

confidence: 99%

“…Methods in the second category use fuzzy logic controllers with tunable parameters which are updated via self-tuning algorithms as in [30,31]. He et al [27] reduced the three parameters of the PID controller to a single unknown variable inspired by Ziegler-Nichols formula and considered a fuzzy adaptation mechanism to estimate this new single parameter. Gautam and Ha [28] proposed a fuzzy self-tuning estimator for PID parameters to control a quadrotor.…”

Section: Introductionmentioning

confidence: 99%

“…Fuzzy logicbased control methods can be categorized mainly into two classes, methods in the first category employ fuzzy estimators to tune the parameters of conventional controllers as in [27][28][29]. Methods in the second category use fuzzy logic controllers with tunable parameters which are updated via self-tuning algorithms as in [30,31].…”

Section: Introductionmentioning

confidence: 99%

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Generalized self-tuning regulator based on online support vector regression

Uçak

Günel

2016

Neural Comput & Applic

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This paper introduces a novel generalized selftuning regulator based on online support vector regression (OSVR) for nonlinear systems. The main idea is to approximate the parameters of an adaptive controller by optimizing the regression margin between reference input and system output. For this purpose, ''closed-loop margin'' which depends on tracking error is defined, and the parameters of the adaptive controller are optimized so as to minimize the tracking error which leads simultaneously to the optimization of the closed-loop margin. The overall architecture consists of an online SVR which computes a forward model of the system, an adaptive controller with tunable parameters and an adaptation mechanism realized by separate online SVRs to estimate each tunable controller parameter. The proposed architecture is implemented with adaptive proportional-integral-derivative (PID) and adaptive fuzzy PID in the controller block. The performance of the generalized self-tuning regulator mechanism has been examined via simulations performed on a bioreactor benchmark system, and the results show that the generalized adaptive controller and OSVR model attain good control and modeling performances.

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“…-Calculate the approximated controller parameters h À m by SVR estimator trained at previous step (n À 1) via (27).…”

Section: Adaptive Control Algorithm For the Generalized Str Based On Svrmentioning

confidence: 99%

Section: Nþ1mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Generalized self-tuning regulator based on online support vector regression

Uçak

Günel

2016

Neural Comput & Applic

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“…As a key-point, let us note that they allow increasing significantly the robustness of the control system. Their structure is similar to that of fuzzy controllers: while the outputs are the PID parameter increments, the inputs can be both the process output error and its increment [49,50] or the performance values [48,49,50,51]. Although supervising a PID controller (already implemented and used in a building) can be useful for improving its performance, this kind of approaches is not the best way to manage energy resources in multi-energy buildings.…”

Section: Fuzzy Supervision Of a Pid Controllermentioning

confidence: 99%

Hybrid PID-fuzzy control scheme for managing energy resources in buildings

Paris

Eynard

Grieu

et al. 2011

Applied Soft Computing

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both indoor temperature regulation and energy resources management in buildings require the design and the implementation of efficient and readily adaptable control schemes. One can use standard schemes, such as "on/off" and PID, or "advanced" schemes, such as MPC (Model Predictive Control). Another approach would be considering artificial intelligence tools. In this sense, fuzzy logic allows controlling temperature and managing energy sources, taking advantage of the flexibility offered by linguistic reasoning. With this kind of approaches, both the specific use of a building and the specificities of a proposed energy management strategy can be easily taken into account when designing or adjusting the control scheme, without having to model the process to be controlled. PID controllers being commonly used in buildings engineering, the proposed control scheme is built on the basis of a PID controller. This allows implementing the scheme even if a control system based on such a controller is already in use. So, a hybrid PID-fuzzy scheme is proposed for managing energy resources in buildings, as the combination of two usual control structures based on PID and fuzzy controllers: the "parallel" structure (according to the current dynamical state of the considered process, either the PID or the fuzzy controller is selected) and the "fuzzy supervision" of a PID controller. To test the scheme in simulation, a building mock-up has been built, instrumented and modeled. Finally, criteria describing the way energy is used and controlled in real-time have been defined with the aim of evaluating both the proposed strategy and the control scheme performance.

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