A model-based approach for jet aircraft lateral motion control with constraints satisfaction

Abstract

Design of a potentially robust autopilot to control lateral motion of a jet aircraft for maintaining the balance, turbulence rejection, handling asymmetric wind pressure, linearization and constraints on the inputs imposes technological and computational challenges for certain control algorithms. Especially, when the multiple states and inputs are strongly coupled to each other, it is imperative to evaluate the performance of most efficient control schemes which not only provide stable and error free response but also fulfill the system requirements with minimum computational cost. This paper demonstrates lateral motion control of a jet aircraft using state feedback controllers, proportional integral derivative controller and model predictive controller to evaluate and compare the control objectives. In a block diagram framework as a function of elementary tuning parameters, all strategies are implemented on a linearized state space model which is furnished by the set of fundamental equations of motion. The effects of disturbance, input and output constraints, sampling time and different controller gains are studied for the underlying multiple input multiple output system. State feedback algorithms provide minimum flexibility to achieve the control objectives in restraining the output within constraint boundaries. Proportional integral derivative controller is more flexible, yet not able to impose the limitation on both the input/output pair. Finally, model predictive controller presents the most efficient features by virtue of response time, robustness, stability, cost and constraints fulfillment with minimal computation and input cost.