General Guidance, Navigation, and Control (GNC) Architecture

Traditionally, guidance, navigation, and control (GNC) systems for aerospace vehicles have been designed around a hierarchical inner-outer loop architecture (as on X-43A, [6]-[7]) in which the (faster) inner loop is responsible for stabilization, robustness, command following, disturbance and noise attenuation [211]-[213], while the (slower) outer loop is responsible for determining appropriate commands to the inner loop. Such an architecture is depicted within Figure 3.

Figure 3: Visualization of General Hi-C Architecture

Perhaps the best example illustrating the effectiveness of such an architecture is encapsulated in today’s high performance missile GNC systems where the (faster) inner autopilot loop is designed to follow acceleration commands issued by the (slower) outer guidance loop [202]-[206]. In this work, it will be assumed that a GNC system architecture similar to that depicted in Figure 3 will be used - per the Hypersonics Reference Document [4]. The figure consists of a supervisory system, a guidance system, an autopilot (or control system), and the uncertain plant (or system) being controlled) - the latter two are the focus of the proposed research.

Key features of the autopilot (control system) are now highlighted [211]-[213].

• Coordination of Controls. The inner autopilot or control loop will process onboard sensory data, guidance commands, and supervisory system mode selection to generate properly coordinated control commands to aerodynamic control surface servo-actuators as well as to the propulsion system (e.g. scramjet [11], [18], [21], [36]-[42]).



• Nominal Performance. It will provide stability, reference (or guidance) command following capabilities, disturbance and noise attenuation capabilities, and robust performance in the presence of uncertainty in the plant (discussed below) over a wide range of hypersonic flight conditions [211]-[213].



• Mode Dependent Command Following. The autopilot will be designed to follow distinct command types during specific flight modes (e.g. ascent/climb pullups, trajectory following, altitude/attitude capture and hold, cruise, bank-to-turn, descend) selected by the supervisory system. We will use the X-43A flight path, documented within [6]-[7], and depicted in Figure 4 as a baseline trajectory to guide the modeling, analysis, design, and evaluation components of the project. This will be influenced by collaborations with NASA on the planned 2008, 2010 SOAREX flights [4].



• Constraint Enforcement and Envelope Protection. The autopilot will also ensure that critical constraints are maintained over certain regions within the flight envelope [6]-[7], [22]-[27], [43], [213], [223]-[228]; e.g. Mach number and angle-of-attack (AOA) constraints for air-breathing (scramjet) propulsion system [11], [18], [21], [36]-[42]. The autopilot will also limit guidance commands to ensure that certain limits are not exceeded; e.g. AOA, dynamic pressure/Mach, normal acceleration, actuator deflection. This is often done in a manner which is overly conservative. Given this, the constraint enforcement methods discussed below can prove to be very helpful (and systematic) [187]-[199].

Figure 4: Visualization of X-43A Mission Profile (NASA DFRC)

• Adaptive. Finally, the autopilot will adapt (or “schedule” itself) on the basis of key aerodynamic variables; e.g. Mach number, AOA, side-slip-angle (SSA). Here, the focus is on accommodating aero-thermo-elastic-propulsion nonlinearities, strong multivariable cross-coupling, and uncertainty (see control challenges and issues described earlier and below) attributed to aero-thermo-elastic-propulsion interaction over a wide Mach range [11], [18], [21]-[32], [36]-[43], [53], [61]-[66], [80], [101].



• Uncertain Plant. The plant is the system being controlled; i.e. the vehicle. It consists of the airframe, servo actuators, a propulsion system (e.g. scramjet [11], [18], [21], [36]-[42]), and sensors. Within the plant there are various critical sources of uncertainty that will be addressed throughout the course of the proposed research. These include (see control challenges and issues) [22]-[27], [28]-[29], [30]-[32], [43], [53]:
  
  
  • Aero-thermo-elastic-propulsion uncertainty (e.g. temperature dependent flexible and servo-elastic mode frequencies and damping)
  
  
  
  • Actuator and sensor dynamics uncertainty (e.g. effective rate limits on actuator)
  
  
  
  • Uncertain disturbances and sensor noise
  
  
  These sources of uncertainty, combined with nonlinear multivariable interactions between aero-thermo-elastic-propulsion phenomena, center of pressure and modal movement - all of which vary considerably across the operating envelop - make the hypersonic waverider/glider control problem very challenging [22]-[27], [28]-[42], [43]-[60].