A Robust MPC-based autopilot for mini UAVs

Sample-Based SMPC for Tracking Control of Fixed-Wing UAV

“…The SMPC algorithm described in the previous section is used to control a FW-UAV, whose systems-equation descriptions are given in [24]. In particular, we consider a linear case, in which the longitudinal and lateral-directional motions result to be decoupled, and each of them can be modeled in the standard discrete time-invariant state-space formulation as (6), where A, B represent the discrete-time state and input matrices respectively, obtained discretizing the corresponding continuous ones derived starting from the equations in [25]. The SMPC controller is adopted to control both the longitudinal dynamics, including airspeed longitudinal component u, angle of attack α, pitch angle θ, pitch rate q and altitude h, as well as the lateral-directional dynamics in terms of airspeed lateral component v, roll and yaw rates, p and r respectively, and roll angle φ.…”

Advances in Spacecraft Attitude Control

“…Future works will validate these results on laboratory spacecraft hardware simulators at the Naval Postgraduate School, and if successful flight in space is available on the international space station making the technology available to enhance the aforementioned applications of the technology [35][36][37][38][39][40][41][42][43][44][45].…”

“…Artificial intelligence and machine learning has evidenced the need for rapid calculations, so as motion mechanics incorporate adopt these new learning algorithms, the impact of this chapter become increasingly relevant in that options revealed in here illustrate simultaneous accuracy and favorable rapidity of calculation [62]. This chapter also complements other algorithmic advances [37][38][39][40][41][42][43][44][45] like system identification [55-59] including nonlinear adaptive forms and also control [46][47][48][49][50][51][52][53][54] for space guidance, navigation, and control (GNC) missions [35,36,[60][61][62][63][64][65] in a time when the United States is developing and relying upon more advanced Machine Learning and AI products than ever before.…”

“…Particle optimization includes particle swarm optimization [34], firefly algorithm [35], and cuckoo search algorithms [36] which focus on a large parameter space with a correspondingly large solution space that may be impossible to evaluate with traditionally exhaustive optimization routines. The artificial bee colony optimization [37], and ant colony optimization [38] algorithms are designed for a smaller investigation swarm but can successfully navigate a problem defined as an infinite set of combinations such as the commonly referred to problem of a traveling salesman visiting a large number of cities.…”

Precise Attitude Control Techniques: Performance Analysis From Classical to Variable Structure Control

“…Solving the LMI is possible to compute the matrix K which stabilizes the uncertain system and the terminal weighing matrix P which ensures satisfaction of the terminal constraints. This is a derivation of the edge theorem, as discussed in [38]. If no uncertainties are considered, the gain K can be computed using LQR design techniques.…”