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3D design model of jack-up rig (1. Crane; 2. Operation area; 3. Flooring; 4. Drilling tower; 5. Rig legs; 6. Spudcans).
Source publication
Many offshore projects, such as offshore oil and gas exploration and offshore wind farm development, have required controlling the position of elevating the hull in a stable and balanced manner in recent years. The Jack-up Rig (JuR) jacking control systems are a revolutionary innovation that is already being employed in offshore drilling and other...
Contexts in source publication
Context 1
... JuR is a kind of movable platform consisting of a large floating hull outfitted with multiple detachable legs, usually three or four, capable of elevating the hull above the sea's surface [3], [4]. The 3-legs JUR 3D-designed model shown in Fig. 1, for example, are triangular barges that have been completely fitted during the drilling process and are motivated by three truss legs, the other shapes are utilized less frequently due to their complexity in structure and organization. JuR is also known for developing the jacking control system, which has three, four, six, and eight ...
Citations
... One limitation of these approaches is that, in some scenarios, the kinetic functions of motions must be linearized. From the preceding discussion, it can be seen that numerous approaches, including Fuzzy Logic Control (FLC), Neural Network Control (NNC), Genetic Algorithm [15], Particle Swarm Optimization Algorithm [16], Cerebellar Model Articulation Control (CMAC), Neural-Fuzzy Control (NFC), and Adaptive Neural-Fuzzy Robust Position Control Scheme [17] have been thoroughly studied from multiple viewpoints. Thus, it is clear that there are many different applications of modern control theory in marine vehicle control, all of which aim to improve the system's performance and stability. ...
Drillships and semi-submersible platforms (SSPs) are used for oil and gas offshore exploration and production activities in seas deeper than 300 meters. These SSPs must be maintained in a predefined position at a predetermined location to complete their offshore tasks. They must have a way to generate forces and angular momentum to balance out external factors (wind, currents, and waves). Actually, the offshore support vessel (OSV) is assisted by dynamic positioning systems (DPSs) in all of its operations, including transit, survival, and station maintenance, and there are significant differences in propulsion systems. In this paper, we suggested a path planning robust adaptive fuzzy-free fault-tolerant control (RAFFFTC) for SSPs with actuator constraints to improve the robust performance and quality of the control system. Accordingly, the adaptive fuzzy controller with adaptive law has been created to change the membership function of the fuzzy controller, and the Lyapunov theory for system stability analysis has been used to illustrate the
$H_\infty$
performance of robust tracking, reduced errors in the path planning control of the SSP in the cases of thrusters and disturbance-free fault-tolerant. Finally, a simulation experiment with two scenarios compared its performance to the other controller, with results verifying the proposed controller’s effectiveness and demonstrating that it achieved the control quality required in the SSP control process.
Optimal control (OCl) can be traced back to the 1960s when it was utilised for solving an optimisation problem (OP). In the OCl technique, a stable controller can be obtained by optimising a cost function (CF). This means that the performance provided by an OCl technique is the best in any instance. Nevertheless, this performance is not unique. Additionally, the parameters are not optimal. As such, there is a need to improve the technique so that the performance and the parameters are optimal. It has many applications in different fields of study such as engineering. There exist different OCl techniques such as Linear Quadratic Gaussian (LQG), Model Predictive Control (MPCl), Kalman Filter (KF), Linear Quadratic Regulator (LQR), etc. LQR and LQG are among the OCl techniques that are becoming popular in the industry and the research community. These controllers can provide robust stability because of their outstanding properties. Robustness to modelling uncertainty and noise (particular to LQG), closed-loop stability, direct input control, optimal input control, etc., are some of the properties that lead to this robust stability provided by these controllers. Nevertheless, one of their major problems is the tuning method. Conventionally, these controllers are tuned using classical methods such as Bryson’s, analytical, trial-and-error, etc., methods. But these methods are associated with a lot of issues, such as time consumption, requiring experience and understanding of the problem at hand, lack of guaranteeing optimal values, etc., which lead to performance, stability, robustness, etc., problems. To solve these issues, researchers tend to use computational (optimisation) methods which proved to be more efficient. Consequently, a comprehensive state-of-the-art review of the recent studies that used computational methods for the improvement of OCl is presented in this paper. Different MAs that have been used to implement optimised LQR and LQG controls have been presented where the most used MAs are discussed in detail. The extent to which these MAs improved the control techniques is critically analysed. Additionally, optimised hybrids of these control techniques have been considered. Furthermore, the most controlled systems are discussed in detail including system modelling and state space representation derivation. The implementation modes of the techniques have been analysed. Some future research directions are also outlined. In essence, the work’s major goal is to provide a comprehensive guide for researchers on computational methods usage for OCl improvement using LQR and LQG as case studies.
