A novel approximation of underwater robotic vehicle controller exploiting multi-point matching
These videos have been matched automatically. Contact us if they are not relevant.
These videos have been matched automatically. Contact us if they are not relevant.
View abstract on PubMed
Summary
This summary is machine-generated.This study approximates higher-order underwater robotic vehicle (URV) controllers to lower-order models using multi-point matching and grey wolf optimization. This yields an efficient and economical controller for URV systems.
Area Of Science
- Robotics
- Control Systems Engineering
- Optimization Algorithms
Background
- Underwater robotic vehicle (URV) performance is significantly impacted by complex internal and external dynamics.
- Effective control is crucial for achieving desired momentum and operational efficiency in URVs.
- Approximating higher-order controllers to lower-order models offers potential for more economical and efficient control solutions.
Purpose Of The Study
- To develop an efficient, effective, and economical lower-order (LO) controller for higher-order (HO) underwater robotic vehicle (URV) systems.
- To approximate HO URV controllers to LO URV controllers using a multi-point matching technique.
- To optimize the controller approximation using the grey wolf optimization algorithm (GWOA).
Main Methods
- Approximation of HO URV controller to a LO URV controller using expansion parameters.
- Minimization of errors between HO and LO controller expansion parameters via multi-point matching, formulated as an objective function (OF).
- Optimization of the OF using GWOA, subject to steady-state matching and Hurwitz stability criteria as constraints.
Main Results
- The proposed multi-point matching approach successfully generated a LO URV model.
- Validation through comparison with existing LO URV models demonstrated the effectiveness of the proposed method.
- Performance evaluation showed the applicability of the derived LO controller, supported by response analysis and statistical data on performance error values (PEVs).
Conclusions
- The presented method effectively approximates higher-order URV controllers to lower-order models.
- The integration of multi-point matching and GWOA provides an efficient and stable control strategy for URVs.
- The developed LO controller offers a practical and economical solution for URV applications.
These videos have been matched automatically. Contact us if they are not relevant.
These videos have been matched automatically. Contact us if they are not relevant.
Related Concept Videos
Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
Here, in order to determine the magnitude of velocity and acceleration for point...
A cruise control system in a car is designed to maintain a specified speed automatically by adjusting the gas pedal. The system continuously measures the vehicle's speed and makes fine adjustments to the pedal to achieve this goal. The root locus method is particularly useful for understanding how the cruise control system's behavior changes under varying conditions, such as when the car goes uphill, downhill, or faces strong wind resistance.
This system can be represented by a block...
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
In mechanical engineering, one-degree-of-freedom systems form the basis of a wide range of electrical and mechanical components. Using these models, engineers can predict the behavior of various parts in a larger system, which gives them insight into how different forces interact with each other.
A one-degree-of-freedom system is defined by an independent variable that determines its state and behavior. One example of a one-degree-of-freedom system is a simple harmonic oscillator, such as a...
Controller configurations are crucial in a car's cruise control system because they manage speed over time to maintain a consistent pace regardless of road conditions, thereby meeting design goals. In traditional control systems, fixed-configuration design involves predetermined controller placement. System performance modifications are known as compensation.
Control-system compensation involves various configurations, most commonly series or cascade compensation, in which the controller...
A three-dimensional force system refers to a scenario in which three forces act simultaneously in three different directions. This type of problem is commonly encountered in physics and engineering, where it is necessary to calculate the resultant force on the system, which can then be used to predict or analyze the behavior of the object or structure under consideration.
To solve a three-dimensional force system, first resolve each force into its respective scalar components. Do this using...