Practical Guidelines for Tuning PD and PI Delay-Based Controllers

Hernández-Díez, José-Enrique; Méndez-Barrios, César-Fernando; Niculescu, Silviu–Iulian

doi:10.1016/j.ifacol.2019.12.207

Cited by 8 publications

(6 citation statements)

References 8 publications

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“…The drawbacks of this method is that in the region between the boundaries of the stability locus and the convex region, we cannot find any stable solution using convex optimization. Another study on PI-PD controller tuning for time delay system is proposed in (Hernández-Diez et al 2019) which uses the graphical method to find out the stability region in the controller parameter space. For the digital implementation of the proposed control scheme in (Hernández-Diez et al 2019), the PD controller uses the Euler approach for approximating the derivative term which is termed as the Pδ controller.…”

Section: Previous Work On Stability Regions For Handling Delayed Systems With Pid Controllersmentioning

confidence: 99%

“…Another study on PI-PD controller tuning for time delay system is proposed in (Hernández-Diez et al 2019) which uses the graphical method to find out the stability region in the controller parameter space. For the digital implementation of the proposed control scheme in (Hernández-Diez et al 2019), the PD controller uses the Euler approach for approximating the derivative term which is termed as the Pδ controller. However, all these methods, discussed above are designed to find a stabilizing set of PID controller parameters in 2D space, keeping one controller parameter fixed, especially the proportional gain.…”

Section: Previous Work On Stability Regions For Handling Delayed Systems With Pid Controllersmentioning

confidence: 99%

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Time delay handling in dominant pole placement with PID controllers to obtain stability regions using random sampling

Halder

Das

Gupta

2020

International Journal of Control

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This paper proposes a new formulation of proportional-integral-derivative (PID) controller design using the dominant pole placement method for handling second order plus time delay (SOPTD) systems. The proposed method does not contain any finite term approximation like different orders of Pade for handling the time-delay term, in the quasi-polynomial characteristic equation. Rather it transforms the transcendental exponential delay term of the plant into finite number of discrete-time poles by a suitable choice of the sampling time. The PID controller has been represented by Tustin's discretization method and the PID controller gains are obtained using the dominant pole placement criterion where the plant is discretized using the pole-zero matching method. A random search and optimization method has been used to obtain the stability region in the desired closed loop parameters space by minimising the integral squared error (ISE) criterion by randomly sampling from the stabilizable region and then these closed loop parameters are mapped on to the PID controller gains. Effectiveness of the proposed methodology is shown for nine test-bench plants with different lag to delay ratios and open loop damping levels, and the effect of choosing different sampling times, using credible numerical simulations.

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Section: Previous Work On Stability Regions For Handling Delayed Systems With Pid Controllersmentioning

confidence: 99%

Section: Previous Work On Stability Regions For Handling Delayed Systems With Pid Controllersmentioning

confidence: 99%

Time delay handling in dominant pole placement with PID controllers to obtain stability regions using random sampling

Halder

Das

Gupta

2020

International Journal of Control

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“…where e m (t), e m (t − 1), and e m (t − 2) can be considered, respectively, as the x 4 value of the s t , s t−1 , and s t−2 , ▵ 𝜔 m is the output of main controller and k mp , k mi , and k md are the coefficients of the first three dimensions of actor network K t . The principles for choosing x 4 as error e m are given by (1) The controller needs to be forward-looking to make the mobile robot follow the path with large curvature accurately.…”

Section: The Lower Controller Design In the Sac-pid Control Systemmentioning

confidence: 99%

A self‐adaptive SAC‐PID control approach based on reinforcement learning for mobile robots

Fan

et al. 2021

Intl J Robust & Nonlinear

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Proportional-integral-derivative (PID) control is the most widely used in industrial control, robot control, and other fields. However, traditional PID control is not competent when the system cannot be accurately modeled and the operating environment is variable in real time. To tackle these problems, we propose a self-adaptive model-free SAC-PID control approach based on reinforcement learning for automatic control of mobile robots. A new hierarchical structure is developed, which includes the upper controller based on soft actor-critic (SAC), one of the most competitive continuous control algorithms, and the lower controller based on incremental PID controller. SAC receives the dynamic information of the mobile robot as input and simultaneously outputs the optimal parameters of incremental PID controllers to compensate for the error between the path and the mobile robot in real time. In addition, the combination of 24-neighborhood method and polynomial fitting is developed to improve the adaptability of SAC-PID control method to complex environment. The effectiveness of the SAC-PID control method is verified with several different difficulty paths both on Gazebo and real mecanum mobile robot. Furthermore, compared with fuzzy PID control, the SAC-PID method has merits of strong robustness, generalization, and real-time performance.

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“…On the other hand, this mere fact complicates the overall stability analysis, since the classical Routh-Hurwitz criterion of linear systems is no longer applicable. Nevertheless, as stated in [13], besides the fact that including a delay will induce a more complex behavior, it is important to point out that the delay phenomenon can also promote the system's stability, where classical PID controllers fail to stabilize the closedloop system.…”

Section: References Number Of Sensors In the Control Schemementioning

confidence: 99%

A Current Sensorless Delay–Based Control Scheme for MPPT–Boost Converters in Photovoltaic Systems

et al. 2020

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Practical Guidelines for Tuning PD and PI Delay-Based Controllers

Cited by 8 publications

References 8 publications

Time delay handling in dominant pole placement with PID controllers to obtain stability regions using random sampling

Time delay handling in dominant pole placement with PID controllers to obtain stability regions using random sampling

A self‐adaptive SAC‐PID control approach based on reinforcement learning for mobile robots

A Current Sensorless Delay–Based Control Scheme for MPPT–Boost Converters in Photovoltaic Systems

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