Disturbance rejection of offshore drilling platforms: An equivalent-input-disturbance-based dynamic positioning control method.

Offshore drilling platforms are exposed to wind, waves, currents, and other unknown disturbances. Accurately estimating and rejecting these disturbances is the key to ensuring reliable station-keeping of the platforms. In this study, a novel dynamic positioning method using an improved equivalent-input-disturbance (EID) approach is proposed for offshore drilling platforms. An improved EID estimator is employed to estimate and suppress unknown disturbances, significantly enhancing the disturbance-rejection performance of the dynamic positioning system. The input channels are decoupled through linear transformation, and the parameter tuning process of the observer and controller is optimized, thus improving system performance. The bounded-input bounded-output stability of the closed-loop system is proved. This study provides insights into the design of dynamic positioning systems for offshore drilling platforms.

Deep optimization design of 2D repetitive control systems with saturating actuators: An adaptive multi-population PSO algorithm.

This paper presents an optimization design method for a two-dimensional (2D) modified repetitive control system (MRCS) with an anti-windup compensator. Using lifting technology, a 2D hybrid model of the MRCS considering actuator saturation is established to describe the control and learning of the repetitive control. A linear-matrix-inequality (LMI)-based sufficient condition is derived to ensure the stability of the MRCS. Two tuning parameters, the selection of which is critical to the system design, are used in the LMI to adjust the control and learning, and hence the reference-tracking performance. A new cost function, developed through time domain analysis, directly evaluates the control performance of the system without calculating control errors, thus reducing the optimization time. Based on this cost function, an adaptive multi-population particle swarm optimization algorithm is presented to select an optimal pair of tuning parameters in which multiple populations cooperatively search in non-intersecting search intervals. An anti-windup term is added between the low-pass filter and the time delay in the modified repetitive controller to mitigate the undesirable effect of actuator saturation on system performance and stability. Simulations and experiments on the speed control of a rotation control system demonstrate the validity of the approach.

A robust integrated control design for weight-on-bit fluctuation suppression in drilling process subject to fractured formations.

Fractured formations lead to insufficient contact or loss of contact between the drill bit and rocks, which subsequently causes notable fluctuation in weight-on-bit due to flexible drill strings. This paper presents a robust integrated control design to suppress weight-on-bit fluctuation. First, a finite element drill-string longitudinal model is employed as the basis of control design, which has been verified using actual run data. The integrated controller contains a proportional-integral (PI) controller and a dynamic output feedback controller, dealing with the system's first-order and high-order dynamics. The dynamic output feedback controller only needs to focus on the system's high-order dynamics since the controller synthesis is based on the pre-designed PI controller. The controller decreases the flexible mode amplitude from disturbance to the output channel through H-infinity loop shaping, making the closed-loop system less flexible. The numerical results demonstrate that the developed approach can effectively suppress weight-on-bit fluctuation due to drill-string flexibility when drilling in fractured formations.

Equivalent-input-disturbance-based robust control of drilling trajectory with weight-on-bit uncertainty in directional drilling.

Weight-on-Bit is of vital importance to the drilling trajectory orientation in directional drilling. This paper concerns robust control of drilling trajectory with weight-on-bit uncertainty for the directional drilling process. The objective is to develop an equivalent-input-disturbance-based trajectory control scheme such that the drilling trajectory is precisely controlled by suppressing the fluctuations of weight-on-bit. The motion orientation of both the drill bit and a series of stabilizers is used to describe the evolution process of the drilling trajectory. A state-space model with weight-on-bit uncertainty is derived from the evolution equation through a variable transformation. An equivalent-input-disturbance-based trajectory control system is designed, and two control loops are to track and control the trajectory inclination and azimuth, respectively. Two internal models track the trajectory inclination and azimuth respectively to elevate the control accuracy in the trajectory system. Two observer models combined with two low-pass filters estimate the trajectory inclination and azimuth by measuring the bottom hole assembly's inclination and azimuth. Some sufficient conditions are derived using linear-matrix-inequalities to obtain the control parameters by considering a reasonable fluctuation range of weight-on-bit. Finally, the control effects in the build-up and horizontal section of the drilling trajectory illustrate the proposed approach's validity.

A deviation correction strategy based on particle filtering and improved model predictive control for vertical drilling.

This paper is concerned with the correction of trajectory deviation in vertical drilling. Note that the accuracy of correction control will be reduced significantly by measurement and process noises, which finally leads to that the inclination angle exceeds beyond a tolerable limit. To deal with such noises and take into account practical constraints, a deviation correction strategy is developed for vertical drilling based on particle filtering and improved model predictive control in this paper. Firstly, the distributions and characters of the measurement and process noises in vertical drilling process are analyzed, and their approximate prior probability distributions are obtained. Based on the analysis, the structure of the deviation correction strategy is provided, including a particle filter and an improved model predictive controller which introduces a flexible constraint and an adjustable weight. The particle filter is effective to reject the measurement noises, and the improved model predictive controller plays an important role in achieving a small inclination of the drilling trajectory. Finally, two groups of simulations are carried out to illustrate the effectiveness of the proposed correction strategy.

Stubborn State Estimation for Delayed Neural Networks Using Saturating Output Errors.

This paper is concerned with the stubborn state estimation of delayed neural networks that subject to a general class of disturbances in measurements, including outliers and impulsive disturbances as its special cases. This class of disturbances may be unbounded, irregular, and assorted; therefore, they can hardly be suppressed by existing identification-based estimation approaches. In this paper, a stubborn state estimator is constructed by intentionally devising a saturation scheme on the injection of output estimation error. The embedded saturation can effectively resist the influences from these measurement disturbances by saturating them. Moreover, the saturation threshold in the designed scheme is not constant but governed by a dynamic equation with parameters to be designed. Benefiting from this adaptiveness, the estimator obtains more freedom in dealing with various disturbances. By combining a novel Lyapunov functional, the generalized sector condition and two latest integral inequalities, a delay-dependent criterion is derived in a less conservative way to check whether the estimation error system with this dynamic saturation is globally stable. A sufficient condition with two tuning scalars is further provided to codesign the gain of the state estimator and the evolution law of the saturation threshold. Finally, two numerical examples are used to illustrate the stubbornness of this state estimator in the presence of measurement outliers or impulsive disturbances.

Energy-to-Peak State Estimation for Static Neural Networks With Interval Time-Varying Delays.

This paper is concerned with energy-to-peak state estimation on static neural networks (SNNs) with interval time-varying delays. The objective is to design suitable delay-dependent state estimators such that the peak value of the estimation error state can be minimized for all disturbances with bounded energy. Note that the Lyapunov-Krasovskii functional (LKF) method plus proper integral inequalities provides a powerful tool in stability analysis and state estimation of delayed NNs. The main contribution of this paper lies in three points: 1) the relationship between two integral inequalities based on orthogonal and nonorthogonal polynomial sequences is disclosed. It is proven that the second-order Bessel-Legendre inequality (BLI), which is based on an orthogonal polynomial sequence, outperforms the second-order integral inequality recently established based on a nonorthogonal polynomial sequence; 2) the LKF method together with the second-order BLI is employed to derive some novel sufficient conditions such that the resulting estimation error system is globally asymptotically stable with desirable energy-to-peak performance, in which two types of time-varying delays are considered, allowing its derivative information is partly known or totally unknown; and 3) a linear-matrix-inequality-based approach is presented to design energy-to-peak state estimators for SNNs with two types of time-varying delays, whose efficiency is demonstrated via two widely studied numerical examples.