On PID Controller Design by Combining Pole Placement Technique with Symmetrical Optimum Criterion

Nicolau, Viorel

doi:10.1155/2013/316827

Cited by 15 publications

(21 citation statements)

References 17 publications

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“…For example, the steering parameters for normal adaptable PID autopilots have been developed during the last three decades as specified in [6][7][8], and the most important among them is the performance index regarding added resistance due to imperfect steering control. Also, designs based on neural network [9,10], fuzzy logic [11][12], backstepping [13], selftuning control [14], pole placement technique (PPT) [15], extended state observer technique (ESO) [16] and similar are widely used.…”

Section: Introductionmentioning

confidence: 99%

“…As already mentioned, in [15] an analytical method for determining the PID controller parameters was presented. This method is based on the use of a symmetrical optimum and provides very simple formulation for determining PID controller parameter values.…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, in [17], the application of the optimization method to determine the optimal values of PID parameters was obtained. It was considered that optimum values of the PID parameters are inside the range 10% of the parameter values compared to analytical method given in [15]. Considering the mentioned references [15] and [17], it is important to point out that the steering machine limitation was not taken into account in either study.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Pso-Based Pid Controller Design for Ship Course-Keeping Autopilot

Dlabač

Ćalasan

Krčum

et al. 2019

brod

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This paper deals with autopilot proportional-integral-derivative (PID) controller design. In the literature, various available methods for PID controller have been presented. Based on the fact that the existing methods do not guarantee the optimal response of the system on the input step change, in this paper we used a valuable technique called Particle Swarm Optimization (PSO) in order to optimally design PID controller taking into account the system limitation such as the value of the rudder angle saturation. Furthermore, we have compared system response on input step and ramp change of input signal for a few PID controller parameters values obtained by using different methods known in literature. It has been proven that by applying the PSO method, it is possible to determine the optimal PID controller parameters which guarantee fast and proper response from the aspect of the minimal overshoot and the minimal settling time. The obtained results confirm the applicability and efficiency of using PSO method for optimal autopilot PID controller design.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Pso-Based Pid Controller Design for Ship Course-Keeping Autopilot

Dlabač

Ćalasan

Krčum

et al. 2019

brod

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“…For many complex processes (especially with time delay, with overshoot or of nonminimum phase), the PID controllers cannot achieve a very good control performance. In addition, there is not a simple and intuitive method of controller tuning (Ziegler and Nichols, 1942;Garcia and Morari, 1982;Duma et al, 2011;Nicolau, 2013;Singh et al, 2014). The proposed control algorithm is inspired from the internal model control (IMC) concept, which states that an accurate control can be achieved if the control system encapsulates a representation of the controlled process (Francis and Wonham, 1976;Bengtsson, 1977;Garcia and Morari, 1982;Rivera et al, 1986;Horn et al, 1996).…”

Section: Introductionmentioning

confidence: 99%

On a Model Based Practical Control Algorithm

Cirtoaje¹,

Baiesu²

2018

STUD INFORM CONTROL

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Abstract:The design of the proposed algorithm relies on three basic ideas: (1) finding a model-based controller so that for any stable process of proportional type, the closed-loop controller output to a step reference has a step shape (or close to this form) and removes the steady-state error; (2) refining the controller structure so that the initial value of the controller output to a step reference is K times its final value, where K is a tuning parameter with standard value 1; (3) extending the controller structure to address integral processes and some unstable processes by turning them into stable compensated processes of proportional type. The overall controller is a series connection P-IMC of two systems: one of pure proportional type and another one of IMC type. There is a simple procedure to verify online if the model parameters (steady-state gain, time delay and transient time) have suitable values and to adjust them to improve the model. As the PID control algorithm, the proposed method is quasi-universal and practical, but it is superior by its control performance and the simplicity of the tuning procedure (which enables poorly trained workers to easily operate the control system). Also, it is more practical than the classical IMC algorithm because its equations have a unique form for all process types (as the PID algorithm), and has a control gain as tuning parameter instead of a filter time constant. Several applications are given to show the effectiveness of the algorithm for different types of process.

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“…Q.-G. Wang et al [16] dealt with the fourth-order object but without zeros. Nicolau [17] researched possibilities for PID controller design based on the pole placement technique in the combination with symmetrical optimum criterion. Consideration of tracking performance for a continuous-time PID controller (tuned using this method) with anti-windup compensator was described in [18].…”

Section: Introductionmentioning

confidence: 99%