Research on Optimized Design of Predictive Controller for observational Underwater Robot

Research on Trajectory Planning and Control of Operational Underwater Robots

“…Strictly speaking, fractional calculus should be called noninteger calculus, and integer calculus is a special case of it, so using fractional calculus modeling will be more in line with the characteristics of real-world systems. We defne a D α t as the operator of fractional calculus, as shown in formula (7), where a and t are its upper and lower limits, and α is the order of calculus, which is the integral variable.…”

“…Chang et al [6] and others used the MATLAB simulation tool to convert the six-degreeof-freedom hydrodynamic equation of the underwater vehicle into a matrix equation, which is better applied to motion control and simulation. Zhu et al [7] proposed a simulation system, which considers all normal, general, and practical models of nonlinear hydrodynamics and uses the FLUENT software to perform numerical simulation and identifcation of the parameters of the underwater robot, which proves to have good accuracy. Tis paper uses the current relatively common underwater robot model, which is formed by the researchers' reasonable simulation and frame construction and then adjusts the parameters according to the characteristics of the designed operational underwater robot.…”

Improved Generalized Predictive Control for High-Speed Train Network Systems Based on EMD-AQPSO-LS-SVM Time Delay Prediction Model

“…However, the network application environment and nonlinear controlled object of the above method are completely different from the train network control system, so it is difficult to be applied to the high-speed train network system with high real-time performance requirements. As a model-based advanced control method, generalized predictive control (GPC) has the advantages of predictive model, control optimization, cyclic rolling, keeping output variable stable, etc., which is widely used for tracking control of complex nonlinear systems such as HSTs [20], spacecraft [21], and underwater robots [22]. Li and Yan [20] designed a GPC fast algorithm based on extreme learning machine for HSTs to achieve the speed tracking control, and the extreme learning machine was used to study the parameter mapping relationship between system model and controller, which greatly reduces the computational burden of the algorithm.…”

“…Chen et al [21] proposed a GPC method with extended state observer to solve the tracking control problem of the spacecraft attitude, and according to the hyperbolic tangent function, the extended state observer was designed to estimate and compensate the uncertainties and unknown disturbances of the system, which achieves high-precision tracking of spacecraft attitude. Zhu et al [22] designed an improved generalized predictive control (IGPC) method for the motion control of underwater robots, and the incremental proportion integration differentiation (PID) algorithm was used to optimize the generalized predictive controller in the initial stage, which improves the stability of the system.…”