A Second-Order Time-Difference Position Constrained Reduced-Dynamic Technique for the Precise Orbit Determination of LEOs Using GPS

In this paper, we propose a new reduced-dynamic (RD) method by introducing the second-order time-difference position (STP) as additional pseudo-observations (named the RD_STP method) for the precise orbit determination (POD) of low Earth orbiters (LEOs) from GPS observations. Theoretical and numerical analyses show that the accuracies of integrating the STPs of LEOs at 30 s intervals are better than 0.01 m when the forces (<10−5 ms−2) acting on the LEOs are ignored. Therefore, only using the Earth’s gravity model is good enough for the proposed RD_STP method. All unmodeled dynamic models (e.g., luni-solar gravitation, tide forces) are treated as the error sources of the STP pseudo-observation. In addition, there are no pseudo-stochastic orbit parameters to be estimated in the RD_STP method. Finally, we use the RD_STP method to process 15 days of GPS data from the GOCE mission. The results show that the accuracy of the RD_STP solution is more accurate and smoother than the kinematic solution in nearly polar and equatorial regions, and consistent with the RD solution. The 3D RMS of the differences between the RD_STP and RD solutions is 1.93 cm for 1 s sampling. This indicates that the proposed method has a performance comparable to the RD method, and could be an alternative for the POD of LEOs.

SLIDING MODE-PID FUZZY CONTROLLER WITH A NEW REACHING MODE FOR UNDERWATER ROBOTIC MANIPULATORS

Design of an accurate and robust controller is a challenging topic in an underwater manipulator control. This is due to hydrodynamic disturbances in underwater environment. In this paper a sliding mode control (SMC) included a PID sliding surface and fuzzy tunable gain is designed. In this proposed controller robustness property of SMC and fast response of PID are incorporated with fuzzy rules to reduce error tracking. In the control law, for removing of chattering, the exponential function is used. And also the system is analyzed in terms of stability by direct Lyapunov method. By tuning gains with fuzzy logic, the proposed controller does not require an accurate model of underwater manipulator dynamics. Hence the modeling and simulation studies are done for an underwater manipulator to verify the effectiveness of the proposed method in presence of unmodeled dynamic (variable masses of links) and external disturbance. Both new proposed controller and conventional SMC are simulated. The results of simulation show the high performance of proposed controller in comparison to conventional SMC.

GENERALIZED PREDICTIVE CONTROL FOR DIFFERENCE-ALGEBRAIC BIOLOGICAL ECONOMIC SYSTEMS

In this paper, a nonlinear difference-algebraic system is used to model some populations with stage structure when the harvest behavior and the economic interest are considered. The stability analysis is studied at the equilibrium points. After the nonlinear difference-algebraic system is changed into a linear system with the unmodeled dynamics, a generalized predictive controller with feedforward compensator is designed to stabilize the system. Adaptive-network-based fuzzy inference system (ANFIS) is used to make the unmodeled dynamic compensated. An example illustrates the effectiveness of the proposed control method.

A SHRFNN Control for a Switched Reluctance Motor Drive

Due to unmodeled dynamic behavior and uncertainties exist in the applications of switched reluctance motor (SRM) drive which seriously affected the drive performance, a supervisoy hybrid recurrent fuzzy neural network (SHRFNN) speed control system that combined supervisor control, recurrent RFNN and compensated control is proposed to increase the robustness of the SRM drive system. First, the asymmetrical structure of the power converter is applied to SRM drive. In order to process uncertainties, a SHRFNN control system is proposed to control SRM drive system. With proposed SHRFNN control system, the SRM drive possesses the advantages of good transient control performance and robustness to unmodeled dynamic behavior and uncertainties for speed control. The effectiveness of the proposed control scheme is verified by experimental results.