Design and simulation of ship course controller based on auto disturbance rejection control technique

Han, Yaozhen; Xiao, Huining; Wang, Changshun; Zhou, Fengyu

doi:10.1109/ical.2009.5262835

Cited by 12 publications

(13 citation statements)

References 4 publications

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“…A conventional ADRC is better than PID controller in terms of forming new set of tools and control laws which include nonlinear control rules, the scheme and technique of total disturbance observation, estimation and rejection. 36 ADRC tackles the system known as well as unknown disturbances effectively by considering all the system disturbances as lumped ones. 36 However, the current improvement in fractional order calculus has adapted applications of fractional-order processes in control theory.…”

Section: Motivationmentioning

confidence: 99%

“…36 ADRC tackles the system known as well as unknown disturbances effectively by considering all the system disturbances as lumped ones. 36 However, the current improvement in fractional order calculus has adapted applications of fractional-order processes in control theory. One of the best example of application of fractional-calculus is fractional-order-PID controller (fo-PID).…”

Section: Motivationmentioning

confidence: 99%

“…It has the same ease of use as a standard PID control, but with more sophisticated capabilities like smoothing setpoint jumps via a transient profile generator as well as nonlinear feedback combination. 36 3. ADRC, like PID control, does not necessitate a model of the controlled process and hence has a wide range of applications.…”

Section: Advantages Of Proposed Controller Strategy Over Classical Co...mentioning

confidence: 99%

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Optimal control of electrical vehicle incorporated hybrid power system with second order fractional‐active disturbance rejection controller

Safiullah

Rahman

Lone

2021

Optim Control Appl Methods

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This article proposes a novel control methodology employing a fractional‐active‐disturbance‐rejection‐controller for the combined operation of load frequency control and automatic voltage regulator of a hybrid power system. A two area hybrid power system with diverse energy sources like solar‐thermal, conventional‐thermal and wind sources equipped with appropriate system nonlinearities is investigated. In order to ascertain the role of modern‐day electric‐vehicle (EV), the hybrid power system is incorporated with EVs in both the areas. To establish an effective frequency, voltage and tie line power control of the hybrid power system, a second order fractional‐active‐disturbance‐rejection‐controller with fractional‐extended state observer is modeled as secondary controller. Magnetotactic‐bacteria‐optimization (MBO) technique is applied to obtain optimal values of the controller gains and the hybrid system parameters. The robustness of the controller gains is tested under different system parameter changes from their nominal values. In addition, the effect of incorporating a power system stabilizer on the hybrid power system is evaluated. Further, the impact of integrating renewable sources and EVs in the hybrid power system is explored. Moreover, the stability of the hybrid power system is monitored with the inclusion of FACTS device. The developed controller operates encouragingly with reference to system stability, rapidity and accuracy in comparison to testified control strategies available in the literature. The robustness test under load‐perturbation, solar‐insolation, wind input variations also proves the efficiency of MBO optimized second order fractional‐active‐disturbance‐rejection‐controller gains.

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Section: Motivationmentioning

confidence: 99%

Section: Motivationmentioning

confidence: 99%

Section: Advantages Of Proposed Controller Strategy Over Classical Co...mentioning

confidence: 99%

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Optimal control of electrical vehicle incorporated hybrid power system with second order fractional‐active disturbance rejection controller

Safiullah

Rahman

Lone

2021

Optim Control Appl Methods

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“…This article adopts the method of linear active disturbance rejection controller (LADRC) to design track controller, and apply the tracking differentiator (TD) in the input terminal of reference to arrange the transition process of the expected course angle, and extract its differential signal [24,25] . The structure of LADRC is shown as Figure 3.…”

Section: The Design Of Path Tracking Controllermentioning

confidence: 99%

Automatic cruise system for water quality monitoring

Zhu

Liu

Chen

et al. 2018

International Journal of Agricultural and Biological Engineering

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The range of unit fixed-point measurement on water quality monitoring system is limited, and the cost for multipoint measurement is high. In order to solve these problems, the automatic cruise system for water quality monitoring was designed. Sage-Husa adaptive Kalman filtering algorithm was adopted to correct the error in GPS positioning. The boat was equipped with ship control module, water quality parameters acquisition module, power-supply module, GPS module and GPRS-DTU packet data transmission module. An Android application was developed so that individual users can use smartphone to communicate with the boat at all time and places. The results show that the boat can basically cruise in the set route to monitor the water quality. In a 4 m 2 aquatic plants areas, the dissolved oxygen monitored in different time were about 10.2%, 8.5% and 8.3%, respectively, higher than other areas, and the pH values were 4.1%, 3.8% and 3.7% higher than those in other waters, which shown that plants photosynthesis released oxygen consumption of carbon dioxide will affect the dissolved oxygen content and pH value. This system can widen the measurement range, and lower the measuring cost that can be widely used in the water quality monitoring in aquaculture and river management.

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“…The control algorithms presented in the literature for the course changing process are mainly based on a dynamic ship model. They include linear-quadratic techniques (Zwierzewicz, 2004), H ∞ control Messer and Grimble, 1993), feedback linearization control, disturbance rejection control (Han, 2002;Chen et al, 2008;Han et al, 2009), sliding mode control (Tomera, 2010;Ruan, 2006), and backstepping control.…”

Section: Introductionmentioning

confidence: 99%

Designing a ship course controller by applying the adaptive backstepping method

Witkowska¹,

Śmierzchalski²

2012

International Journal of Applied Mathematics and Computer Science

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The article discusses the problem of designing a proper and efficient adaptive course-keeping control system for a seagoing ship based on the adaptive backstepping method. The proposed controller in the design stage takes into account the dynamic properties of the steering gear and the full nonlinear static maneuvering characteristic. The adjustable parameters of the achieved nonlinear control structure were tuned up by using the genetic algorithm in order to optimize the system performance. A realistic full-scale simulation model of the B-481 type vessel including wave and wind effects was applied to simulate the control algorithm by using time domain analysis.

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Design and simulation of ship course controller based on auto disturbance rejection control technique

Cited by 12 publications

References 4 publications

Optimal control of electrical vehicle incorporated hybrid power system with second order fractional‐active disturbance rejection controller

Optimal control of electrical vehicle incorporated hybrid power system with second order fractional‐active disturbance rejection controller

Automatic cruise system for water quality monitoring

Designing a ship course controller by applying the adaptive backstepping method

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