Tuning of PID controllers based on gain and phase margin specifications

Ho, Weng Khuen; Hang, Chang Chieh; Cao, L.S.

doi:10.1016/0005-1098(94)00130-b

Cited by 493 publications

(110 citation statements)

References 5 publications

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“…Two curves in Figure 17 show a high similarity, as we expected. To prove the validity of the reduced model, the PID control performance is shown in Figure 18, while the PID controller parameters are tuned by the SIMC method [18] based on the identified model (19). Figure 18 shows the excellent control quality of the heat exchanger with a short transition period and small overshoot, which confirms the effectiveness of our method.…”

Section: Experimental Verificationmentioning

confidence: 64%

Experimental Research on Heat Exchanger Control Based on Hybrid Time and Frequency Domain Identification

et al. 2018

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Abstract:A heat exchanger is widely used for energy management or heat recovery in sustainable energy systems. In many application cases, the outlet temperature should be strictly controlled as desired. However, it is challenging to obtain an accurate dynamic model due to the high-order dynamics, thus reducing the control performance. To this end, this paper proposes a novel identification method by considering the heating process as an approximate second-order plus time delay (SOPDT) model. A normalized analysis indicates that the time-scaled step responses of the general second-order models almost intersect at the same point, which leads to an equation describing the sum of the time constants. Critical stability analysis based on the Nyquist criterion gives another two equations in the frequency domain. Hence the time constants and time delay can be obtained by solving the equations. Illustrative examples show the identification efficiency of the proposed method in the parameter estimation, model reduction, and anti-noise performance. With an effective identification, the high-fidelity SOPDT model makes the PID controller tuning less challengeable. The simulation results based on a benchmark heat exchanger model demonstrate the feasibility of the identification and control. Finally, a real heat exchanger control facility is built and the experimental performance agrees well with the simulation expectation, depicting a promising application prospect in future sustainable applications.

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Section: Experimental Verificationmentioning

confidence: 64%

Experimental Research on Heat Exchanger Control Based on Hybrid Time and Frequency Domain Identification

et al. 2018

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“…For example, Knot (5,8) "Kp = YI,j X 101 + YZ,j X 10 ~ + Y3,j • 10-J + Y4,j x 10 -2 + Ys,j x 10 -3 , Ti = Y6,j • 10~ + Y7,j x 10 -t + Y8,j x 10 -2 + Y9,j x 10 -3 + Ylo,j x 10 -4 , Td = Yll,j X 10 ~ + Yl2,j X 10 -l + YI3,j X 10 -2 + Yl4,j X 10 -3 + Y15,j X 10 -4 .…”

Section: Generation Of Knots and Pathsmentioning

confidence: 98%

“…If a gain margin and a phase margin are used simultaneously to tune the PID controller parameters, the tuned system will have good control performance. Ho et al proposed a tuning method for PID controller parameters based on a given gain margin and a given phase margin, but the method is not suitable to the unstable objects [5].…”

Section: Introductionmentioning

confidence: 99%

Design of PID controller with incomplete derivation based on ant system algorithm

Tan

Zeng

2004

J. Control Theory Appl.

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A new and intelligent design method for PID controller with incomplete derivation is proposed based on the ant system algorithm (ASA). For a given control system with this kind of PID controller, a group of optimal PID controller parameters K~ , T[ , and T,~ can be obtained by taking the overshoot, settling time, and steady-state error of the system' s unit step response as the penComaance indexes and by use of our improved ant system algorithm. K 7 , T[, and T~ can be used in real-time control.This kind of controller is called the ASA-PID controller with incomplete derivation. To verify the performance of the ASA-PID controller, three different typical transfer functions were tested, and three existing typical tuning methods of PID controller parameters,including the Ziegler-Nichols method (ZN), the genetic algorithm (GA), and the simulated annealing (SA), were adopted for comparison. The simulation results showed that the ASA-PID controller can be used to control different objects and has better performance compared with the ZN-PID and GA-PID controllers, and comparable perfomaance compared with the SA-PID controller.

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“…Through combining the strengths of both PID and relay control, Astrom and Hagglund designed the relay auto-tuner for a single-input single output (SISO) PID controller [10] . The gain and phase margin-based method for auto-tuning PID controllers was presented [11] . Hang et al outlined the recent developments in this aspect [12] .…”

Section: Simulation Of Pid-controlled Differential Steeringmentioning

confidence: 99%

Simulation on driving system used for differential steering of electric scooter

Shu

Chen

et al. 2011

Trans. Tianjin Univ.

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Abstract：To investigate the feasibility and effectiveness of the designed control system used for driving and steering of an electric scooter, a model of differential steering was developed. The function of electronic differential steering was realized by controlling the speed of right or left wheel and the corresponding speed difference. The control system was simulated with MATLAB/SIMULINK and ADAMS. It is found that the motor load torque is proportional to the tire vertical force, so the adhesive capacity is met. The electric scooter can operate stably on the slope road at a speed of more than 1.5 m/s and turn stably at yawing velocities of 10°and 90°per second.

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Tuning of PID controllers based on gain and phase margin specifications

Cited by 493 publications

References 5 publications

Experimental Research on Heat Exchanger Control Based on Hybrid Time and Frequency Domain Identification

Experimental Research on Heat Exchanger Control Based on Hybrid Time and Frequency Domain Identification

Design of PID controller with incomplete derivation based on ant system algorithm

Simulation on driving system used for differential steering of electric scooter

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