Design and Tuning of Standard Additive Model Based Fuzzy PID Controllers for Multivariable Process Systems

Harinath, Eranda; Mann, George K. I.

doi:10.1109/tsmcb.2008.919232

Cited by 34 publications

(16 citation statements)

References 16 publications

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“…The simulation results of the proposed RSPID control are shown in Figs. 8,9,10,11. Synchronization responses of x d1 ðtÞ; x 1 ðtÞ; x d2 ðtÞ; x 2 ðtÞ; x d3 ðtÞ and x 3 ðtÞ are depicted in Figs.…”

Section: Simulation Resultsmentioning

confidence: 99%

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A robust self-learning PID control system design for nonlinear systems using a particle swarm optimization algorithm

Lin

Ting

et al. 2011

Int. J. Mach. Learn. & Cyber.

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This study presents a robust self-learning proportional-integral-derivative (RSPID) control system design for nonlinear systems. This RSPID control system comprises a self-learning PID (SPID) controller and a robust controller. The gradient descent method is utilized to derive the on-line tuning laws of SPID controller; and the H 1 control technique is applied for the robust controller design so as to achieve robust tracking performance. Moreover, in order to achieve fast learning of PID controller, a particle swarm optimization (PSO) algorithm is adopted to search the optimal learning-rates of PID adaptive gains. Finally, two nonlinear systems, a two-link manipulator and a chaotic system are examined to illustrate the effectiveness of the proposed control algorithm. Simulation results show that the proposed control system can achieve favorable control performance for these nonlinear systems.

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Section: Simulation Resultsmentioning

confidence: 99%

“…However, the learning speed of these algorithms is slow; thus, they are not suitable for real time control systems. Harinath and Mann [9] proposed a fuzzy PID controller for multivariable process systems. However, it takes a two-level tuning algorithm; thus, it also takes too much computation time.…”

Section: Introductionmentioning

confidence: 99%

A robust self-learning PID control system design for nonlinear systems using a particle swarm optimization algorithm

Lin

Ting

et al. 2011

Int. J. Mach. Learn. & Cyber.

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“…This structure will be called PID-type fuzzy controller or PID-FLC in short throughout this paper. On the other hand, the name fuzzy PID controller (or FLC-PID) refers to the so-called gain scheduling-type PID controllers where the fuzzy logic is used to produce the required K P , K I , and K D gains of the linear PID controller [26,27], and it is not studied in this paper. The PID-type fuzzy controllers are usually classified based on the number of inputs to the controller.…”

Section: Pid-type Fuzzy Logic Controllersmentioning

confidence: 99%

Optimal PID-type fuzzy logic controller for a multi-input multi-output active magnetic bearing system

Noshadi

Shi

Lee

et al. 2015

Neural Comput & Applic

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The performance of the fuzzy controllers depends highly on the proper selection of some design parameters which is usually tuned iteratively via a trial and error process based primarily on engineering intuition. With the recent developments in the area of global optimization, it has been made possible to obtain the optimal values of the design parameters systematically. Nevertheless, it is well known that unless a priori knowledge is available about the optimization search-domain, most of the available time-domain objective functions may result in undesirable solutions. It is consequently important to provide guidelines on how these parameters affect the closedloop behavior. As a result, some alternative objective functions are presented for the time-domain optimization of the fuzzy controllers, and the design parameters of a PID-type fuzzy controller are tuned by using the proposed time-domain objective functions. Finally, the real-time application of the optimal PID-type fuzzy controller is investigated on the robust stabilization of a laboratory active magnetic bearing system. The experimental results show that the designed PID-type fuzzy controllers provide much superior performances than the linear on-board controllers while retaining lower profiles of control signals.

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“…For instance, in cases where the difference between normal and abnormal in each of the conditions (e.g., rest or walk) is not clear, such as in early detection of bradycardias, we have to deal with ambiguous information. Consequently, FPID control has the ability to produce better response performances and decrease the propagation of errors against the traditional control schemes for cardiac pacemaker systems [26,27]. Thus, the design of fuzzy PID controllers with dual-sensors adds predictive capability to the controller and improves the transient response so as to reduce error amplitude.…”

Section: Design Of Fpid Controllersmentioning

confidence: 99%

A Novel Design of Fuzzy PID Controllers for Dual-Sensor Cardiac Pacemaker Systems

Shi¹

2013

OJAB

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This work proposes to design a fuzzy proportional-integral derivative (FPID) controller for dual-sensor cardiac pacemaker systems, which can automatically control the heart rate to accurately track a desired preset profile. The combination of fuzzy logic and conventional PID control approaches is adopted for the controller design based on dual-sensors. This controller offers good adaptation of the heart rate to the physiological needs of the patient under different states (rest and walk). Through comparing with the conventional fuzzy control algorithm, FPID provides a more suitable control strategy to determine a pacing rate in order to achieve a closer match between actual heart rate and a desired profile. To assist the heartbeat recovery, the stimuli with adjustable pacing rate is generated by the pacemaker according to the FPID controller, such actual heart rate may track the preset heart rate faithfully. Simulation results confirm that this proposed control design is effective for heartbeat recovery and maintenance. This study will be helpful not only for the analysis and treatment of bradycardias but also for improving the performance of medical devices.

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Design and Tuning of Standard Additive Model Based Fuzzy PID Controllers for Multivariable Process Systems

Cited by 34 publications

References 16 publications

A robust self-learning PID control system design for nonlinear systems using a particle swarm optimization algorithm

A robust self-learning PID control system design for nonlinear systems using a particle swarm optimization algorithm

Optimal PID-type fuzzy logic controller for a multi-input multi-output active magnetic bearing system

A Novel Design of Fuzzy PID Controllers for Dual-Sensor Cardiac Pacemaker Systems

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