Improvement of the Control System Performance based on Fractional-Order PID Controllers and Models with Robustness Considerations

Meneses, H.; Guevara, Esteban; Arrieta, O.; Padula, Fabrizio; Vilanova, Ramón; Visioli, Antonio

doi:10.1016/j.ifacol.2018.06.153

Cited by 13 publications

(6 citation statements)

References 17 publications

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“…The second was a comparison against the performance obtained with PID controllers designed by the same constraint on the maximum sensitivity and minimizing the same performance index (8). The optimal performance indexes for PID controllers, denoted by J tPID , were taken from [39] and the J t performance index was obtained by applying the FOMCoRoT tuning rule developed in this work using FOPID controllers. For this comparison, the index η is defined as:…”

Section: Optimal Tuningmentioning

confidence: 99%

FOPI/FOPID Tuning Rule Based on a Fractional Order Model for the Process

et al. 2022

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This paper deals with the design of a control system based on fractional order models and fractional order proportional-integral-derivative (FOPID) controllers and fractional-order proportional-integral (FOPI) controllers. The controller design takes into account the trade-off between robustness and performance as well as the trade-off between the load disturbance rejection and set-point tracking tasks. The fractional order process model is able to represent an extensive range of dynamics, including over-damped and oscillatory behaviors and this simplifies the process modelling. The tuning of the FOPID and FOPI controllers is achieved by using an optimization, as a first step, and in a second step, several fitting functions were used to capture the behavior of the optimal parameters of the controllers. In this way, a new set of tuning rules called FOMCoRoT (Fractional Order Model and Controllers Robust Tuning) is obtained for both FOPID and FOPI controllers. Simulation examples show the effectiveness of the proposed control strategy based on fractional calculus.

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Section: Optimal Tuningmentioning

confidence: 99%

FOPI/FOPID Tuning Rule Based on a Fractional Order Model for the Process

et al. 2022

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“…However, there exists some limits. Meneses [31] et al proposed a combined performance evaluation index to solve the performance tradeoff between servo control and regulation control, which is to evaluate FO-PID controller and also based on the theory that the achievable performance is influenced by the process normalized dead time and subject to a robustness constraint. And this study of its performance index is just to guide the selection whether to use fractional order controller or integer order controller when assessing a fractional order system.…”

Section: Introductionmentioning

confidence: 99%

Performance Assessment of FO-PID Temperature Control System Using a Fractional Order LQG Benchmark

Zou

2020

IEEE Access

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In this paper, a fractional order LQG benchmark is proposed for the control performance assessment of fractional order control systems. Similar to the conventional LQG benchmark, the fractional order LQG performance benchmark curve is determined by the numerical calculation method, which avoids the calculation of the complex interaction matrix. The fractional order process model is discretized via fractional order calculus. Meanwhile, the fractional order integral is introduced into the conventional LQG cost function. Then solving the linear quadratic Gaussian problem under the fractional order model and fractional control, the optimal input and output variances are determined for different weighting factors and the performance curves can be achieved. The comparison between fractional order LQG and the conventional LQG shows the improvement of the proposed benchmark under the same condition. The proposed benchmark can provide a more direct and superior reference standard to evaluate the performance of fractional order control system. Finally, a case study of fractional order PID(FO-PID) controller in industrial heating furnace temperature control experiment with model matching and model mismatch conditions is used to verify the effectiveness of the proposed benchmark.

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“…Various FOC tuning rules based on minimisation of integral square error (ISE), integral time square error etc. have been presented [12–16] and some of them incorporate the frequency domain specifications to ensure robustness [17, 18]. To design optimal controllers, several global numerical optimisation algorithms such as big bang–big crunch [13], grey wolf optimiser [14], and particle swarm [19] are utilised.…”

Section: Introductionmentioning

confidence: 99%