A Dynamic Performance Evaluation Technique for Unmanned Aerial Vehicles

The performance of the control system of an Unmanned Aerial Vehicle (UAV) depends on two main parts; dynamic modeling of the UAV and its control law. In the evaluation of the response of the control system of a UAV, both transient response and steady-state response play important roles. In this paper, a new technique is developed to analyze the response of a control system to a command. This technique is based on the performance index and control energy of the closed-loop control system when following a reference trajectory. The dynamic modeling of an UAV forms the heart of its simulation and is critical for the design of the control system. Several versions of the equations of motion (forms of a UAV dynamic model), are introduced and compared and then the positive and the negative features of each of these dynamic models are demonstrated. This technique can be employed to evaluate and compare different control laws to accomplish a UAV mission. The advantage to this technique is that it provides a solid criterion against which to judge the goodness of any control system design. This technique was used to compare the performance of four control system designs, namely PID, LQR, robust linear and robust nonlinear controllers. These four techniques were applied individually to a UAV flying a complete mission. MATLAB/Simulink is used for the implementation of this simulation. I. Introduction Virtually all dynamic systems are nonlinear; yet an overwhelming majority of operational control laws have been designed as if their dynamic systems were linear and time-invariant. As long as the quantitative differences in response are minimal (or at least acceptable in some practical sense), the linear time invariant model facilitates the control system design process. This is because of the direct manner in which response attributes can be associated with model parameters. A small error in modeling, a small error in the control system design, or a small error in the simulation may each result in problems in flight, in the worst case might even result in the loss of an unmanned aerial vehicle. As long as the dynamic effects of parameter variations are slow in comparison to state variation, control design can be based on an ensemble of time-invariant dynamic models. Fast parameters may be indistinguishable from state components, in which case the parameters should be included in an augmented state vector for estimation. The unmanned aerial vehicle (UAV) field is one where extensive use is made of modeling and simulation technologies. The numerical simulation of the aircraft's dynamics is the most important tool in the development and verification of the flight control laws for an aircraft. The availability of special-purpose simulation languages, massive computing capabilities at decreased cost, and advances in simulation methodologies have made simulation one of the most widely used and accepted tools in flight operations research and aircraft systems analysis. The complete aircraft systems and dynamics model incorporates different subsystem models (e.g. aerodynamics, structures, propulsion, and control subsystems) that have interdependent responses to any input. These subsystems also interact with the other subsystems. The dynamic modeling of an aircraft is at the heart of its simulation. The response of an aerial vehicle system to any input, including commands or disturbances (e.g. wind gusts), can be modeled by a system of ordinary differential equations (i.e. the equations of motion). Dealing with the nonlinear, fully coupled differential equations of motion is not an easy task. The key component in a low-cost simulation software package is the aircraft dynamics model represented as a set of ordinary differential equations. The dynamics of an aircraft can be modeled in