Abstract

An experimental attitude control algorithm design using prototypes can minimize space mission costs by reducing the number of errors transmitted to the next phase of the project. The Space Mechanics and Control Division (DMC) of INPE is constructing a 3D simulator to supply the conditions for implementing and testing satellite control hardware and software. Satellite large angle maneuver makes the plant highly nonlinear and if the parameters of the system are not well determined, the plant can also present some level of uncertainty. As a result, controller designed by a linear control technique can have its performance and robustness degraded. In this paper the standard LQR linear controller and the SDRE controller associated with an SDRE filter are applied to design a controller for a nonlinear plant. The plant is similar to the DMC 3D satellite simulator where the unstructured uncertainties of the system are represented by process and measurements noise. In the sequel the State-Dependent Riccati Equation (SDRE) method is used to design and test an attitude control algorithm based on gas jets and reaction wheel torques to perform large angle maneuver in three axes. The SDRE controller design takes into account the effects of the plant nonlinearities and system noise which represents uncertainty. The SDRE controller performance and robustness are tested during the transition phase from angular velocity reductions to normal mode of operation with stringent pointing accuracy using a switching control algorithm based on minimum system energy. This work serves to validate the numerical simulator model and to verify the functionality of the control algorithm designed by the SDRE method.

Highlights

  • The satellite attitude control algorithm design using the State-Dependent Riccati Equation (SDRE) technique and SDRE lter is able to deal with large angle maneuvers and plant uncertainties. e control strategy is based on reaction wheel and gas jets as actuators which allow the design of two control algorithms related to the transition from high angular velocity mode to the normal mode of operation with stringent pointing using an optimal switching control algorithm based on minimum system energy

  • Applying a direct parameterization to transform the nonlinear system into State Dependent Coefficients (SDC) representation, the dynamic equations of the system with control can be write in the form xẋ x xx (xx) xx x xx (xx) uuu with fffffffffffffff, where AAAAnnnnn is the state matrix

  • From the angular velocity reduction mode to the normal mode of operation the criterion could be associated with the amount of energy that the reaction wheel can support before being saturated or with the minimum and maximum values of the gas jets capacity. e criterion used here is based on the total potential and kinetic energy of the system, which means that when the system reaches a certain level of energy the control algorithm change the type of actuator

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Summary

Introduction

E design of a satellite Attitude Control System (ACS), that involves plant uncertainties [1] and large angle maneuvers followed by stringent pointing control, may require new nonlinear attitude control techniques in order to have adequate stability, good performance, and robustness. Applied by Souza in [8] for controlling a nonlinear satellite system with six-degrees of freedom It did not incorporate the SDRE lter as a state observer for the SDRE method, so that uncertainties could be accounted for in the ltering process. N this paper the SDRE technique [9] along with the associated alman lter [10] is applied to design a nonlinear controller for a nonlinear simulator plant where the unstructured uncertainties of the system are represented by process and measurement noise. The satellite attitude control algorithm design using the SDRE technique and SDRE lter is able to deal with large angle maneuvers and plant uncertainties. Several simulations have proven the computationally feasibility for real time execution of the SDRE control algorithm using the satellite’s onboard computer [11]

SDRE Control Methodology
Simulator Model
Simulation Results
Conclusions
Full Text
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