Net buoyancy changes affect hovering performance and increase the extra energy consumption of deep–sea intelligent floats (DIFs) at different submergence depths. To improve the adjustment efficiency of net buoyancy changes and solve the extra energy consumption problem at different submergence depths, this study proposes a combination model of ballasting weight (BW) and minimum oil content for DIFs based on the BW method. The proposed model is accurately designed by combining an exponential function model of actual density distribution in the oceans and the deformation of a DIF hull that resulted from compressibility and thermal expansibility. Given that the adjustment process of the net buoyancy changes should be approximated for actual deployment, the model establishes the compressibility balanced relationship between the DIF hull and actual seawater. The feasibility of the proposed model is validated through hyperbaric tank system and at–sea experiments. Experimental results show that the relationship between the net buoyancy changes of DIF and pressure is nonlinear. The BW changes fluctuate between 0.05 and 0.15 g by increasing the diving depth by 1 m. For the influence of the BW on the submergence depth error, the scope of the depth error ratio is 2.2% to 2.8% from the predefined depth of 100–4200 m. Analysis results provide a basis for the establishment of an optimal net buoyancy adjustment scheme for DIFs.

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Ballasting Weight on Net Buoyancy Changes and Submergence Depth for a Spherical Buoyancy–Driven Intelligent Float Based on the Ballasting Method

In this paper, we introduce a Coefficient Diagram Method (CDM) to design a conventional PID controller. This controller is used to decrease the frequency fluctuations of a microgrid system composed of two renewable energy sources (WTG and STPG) and four controlled elements (UC, FESS, BESS and DEG). The method compares two characteristic polynomials of the same order:, the coefficients of the first polynomial are a function of microgrid parameters and the unknown gains of the PID controller. The second is called the target polynomial; its coefficients are calculated by choosing the stability indices and the equivalent time constant to satisfy the desired performances of the closed-loop system. Mathematically, the order of the polynomial controller determines the type of linear system of equations to solve: undetermined or overdetermined. In our application, the least squares method is used to find an approximate solution to the overdetermined system resulting from this comparison. Digital simulation is performed to test the performance of the microgrid controlled by the CDM-PID controller. The obtained results are compared with two recently published works where the parameters of the PID controllers are tuned by DE and chaotic PSO algorithms. The results show that the CDM-PID controller gives better performance.

https://doi.org/10.1080/00051144.2023.2192380

Frequency control scheme based on the CDM-PID controller for the hybrid microgrid system with stochastic renewable generators

Although the 'Coefficient Diagram Method (CDM)' can generate a stable controller which is robust and give the desired performance, the optimality and cost function are not yet under consideration. Herein, we design and develop an optimal controller for the depth control of an 'Autonomous Underwater Vehicle (AUV)' by analyzing a 'Linear Quadratic Regulator (LQR)' based controller using the CDM. The coefficient diagrams of conventional LQR controller and CDM controller are analyzed and the parameters to design an optimal LQR controller are identified. This controller is implemented for the depth control of an AUV with a 'Variable Buoyancy Engine (VBE)' and is found to have better performance as compared to the LQR and CDM. The objective here is to reduce the control efforts needed for depth control. Finally, we report the detailed theoretical analysis and simulation results.

R. Sharma

Papers

Design and Development of a Depth Controller for an Autonomous Underwater Vehicle with Variable Buoyancy Engine using Coefficient Diagram Method