GN&amp;C Technology Needed to Achieve Pinpoint Landing Accuracy at Mars

Brand, Tim; Fuhrman, Linda; Geller, David; Hattis, Philip D.; Paschall, Stephen; Tao, Yu

doi:10.2514/6.2004-4748

Cited by 18 publications

(4 citation statements)

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“…For the PID ABC optimization the gains c, k, T have been tuned for the following weight values: [q, r] ∈ [1, 1], [10,1], [100, 1], [1,0]. Table (3) shows the result of running the ABC tuning process for each of the above-specified weight values: Each value corresponds to the integral of the instantaneous values for a complete simulated trajectory e.g.…”

Section: A Optimization Of Pidmentioning

confidence: 99%

Quaternion Error Based Optimal Attitude Control Applied to Pinpoint Landing

Ghiglino

Lappas

2015

AIAA Guidance, Navigation, and Control Conference

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The problem of Unmanned Aerial Vehicles (UAV) trajectory tracking has been studied widely and in depth, including the application of optimal control techniques to this problem, which has also been covered frequently in literature, although mostly to simplified models of the UAV and therefore with limited range of operation. The use of more complex mathematical models for UAV control, on the other hand, is less common and practically all the available research makes use of sub-optimal control techniques. Furthermore, the application of optimal control techniques to more accurate UAV models has seldom been attempted before, and even less for quaternion attitude-based models in particular. In this paper we propose the use of two proper optimal control algorithms that rely on attitude quaternion error and its dynamics. These two control algorithms share a feature which is that although the underlying linearized model on which they are based is model-independent, the LQR cost matrices are dependent on both the particularities of the autonomous vehicle and on the UAV state, and more specifically, on the angular and linear velocities. Therefore, the tuning of these controllers becomes the tuning of cost matrices, which is a priori a simpler task than adapting a more complex controller algorithm to pinpoint landing. On the other hand, these control algorithms are based on exact linearization underlying models that, for close tracking conditions, behave effectively like linear time invariant systems. The advantage of this is two-fold. On one hand, the calculation requirements for these controllers are lighter and, more importantly, the adaption of nonlinearities specific to UAV pinpoint landing is relatively simpler. The authors of this research consider these two features to be highly useful in pinpoint landing control. NomenclatureB = Body frame [χ] BI = [φ, θ, ψ] = Euler angles from B to I δ c , δ r , δ p , δ y = Thrust, roll, pitch, yaw commands e = Error (generic) [F A ] B = [F Ax , F Ay , F Az ] = Aerodynamic forces (expressed in B) [F P ] B = [F P x , F P y , F P z ] = Propulsive forces (expressed in B) g = Gravitational acceleration due to the Earth I = Inertial frame I B Bcm B = diag {[I xx , I yy , I zz ]} = Inertial tensor of Bcm with respect to B (expressed in B) m = Mass of UAV ω = Linear control bandwidth I ω B B = [p, q, r] = Angular rate from B to I (expressed in B) [q] BI = [q 0 , q 1 , q 2 , q 3 ] = Quaternion vector from B to I r Bcm/Io I = [x, y, z] = Position of Bcm from Io (expressed in I) v I Bcm B = [u, v, w] = Velocity of Bcm with respect to I (expressed in B) [x c , y c , z c , ψ c ] = Position and yaw commanded setpoints ζ = Damping ratio 1 Control

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Section: A Optimization Of Pidmentioning

confidence: 99%

Quaternion Error Based Optimal Attitude Control Applied to Pinpoint Landing

Ghiglino

Lappas

2015

AIAA Guidance, Navigation, and Control Conference

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“…Human exploration missions and robotic sample return missions require the pinpoint Mars landing (safe landing with tens metres to 100 m of a preselected target site) compared to those of previous Mars missions [22–24]. The entry, descent and landing (EDL) sequence begins at Mars atmosphere interface (defined at a radius of 3522 km from the centre of Mars, and an altitude of 125 km over the surface) and ends with a preselected landing site, which includes hypersonic atmospheric entry phase, parachute descent phase (propulsive terminal descent) and powered descent phase [25, 26].…”

Section: Mars Entry Dynamic Equationsmentioning

confidence: 99%

Extension of robust three‐stage Kalman filter for state estimation during Mars entry

Xiao

et al. 2014

IET Radar, Sonar & Navigation

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In the context of unknown inputs (unknown dynamic biases) and unknown measurement systematic errors, this article studies the extension of robust three-stage Kalman filter (ERThSKF) that satisfies high precision entry navigation filter requirements for Mars pinpoint landing mission. The imprecise dynamic model with uncertain parameters could produce unknown inputs. Using radiometric beacons and/or inertial measurement unit (IMU) as output observation during the Mars atmospheric entry phase, the measurement data from the radio range and/or IMU have unknown measurement systematic errors. To solve these problems, we made an ERThSKF to obtain the accurate state estimation of the uncertain non-linear Mars system with unknown inputs and unknown measurement systematic errors which can be effectively estimated and compensated. Computer simulations show that the proposed navigation filter algorithm in this study can achieve high convergence speed and minor errors, which fulfils the need of future pinpoint Mars landing missions.

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“…Whereas previous investigations, such as those in [1,5], have examined technologies required to achieve pinpoint landing independent of the propulsive terminal descent guidance algorithm, this investigation couples the terminal descent guidance algorithm choice with a common baseline vehicle and dispersion set to comprehensively examine the GNC technology performance necessary to achieve pinpoint landing on Mars. Section II discusses the simulation and dispersion environment assumed for this investigation.…”

Section: Introductionmentioning

confidence: 99%

Guidance, Navigation, and Control System Performance Trades for Mars Pinpoint Landing

Steinfeldt

Grant

Matz

et al. 2010

Journal of Spacecraft and Rockets

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Landing site selection is a compromise between safety concerns associated with the site's terrain and scientific interest. Therefore, technologies enabling pinpoint landing performance (sub-100-m accuracies) on the surface of Mars are of interest to increase the number of accessible sites for in situ research, as well as allow placement of vehicles nearby prepositioned assets. A survey of the performance of guidance, navigation, and control technologies that could allow pinpoint landing to occur at Mars was performed. This assessment has shown that negligible propellant mass fraction benefits are seen for reducing the three-sigma position dispersion at the end of the hypersonic guidance phase (parachute deployment) below approximately 3 km. Four different propulsive terminal descent guidance algorithms were examined. Of these four, a near propellant-optimal analytic guidance law showed promise for the conceptual design of pinpoint landing vehicles. The existence of a propellant optimum with regard to the initiation time of the propulsive terminal descent was shown to exist for various flight conditions. Subsonic guided parachutes were shown to provide marginal performance benefits, due to the timeline associated with descent through the thin Mars atmosphere. This investigation also demonstrates that navigation is a limiting technology for Mars pinpoint landing, with landed performance being largely driven by navigation sensor and map tie accuracy. Nomenclature a = acceleration vector, a 1 a 2 a 3 T a i = acceleration along the ith direction b = scalar weighting parameter on the terminal constraint sensitivity C j i = jth constant coefficient used in the modified Apollo lunar module guidance algorithm dt f = terminal time increment f = set of first-order differential equations of motion g = local acceleration due to gravity g = acceleration vector due to gravity i = index I JJ = partition used in the optimal control solution I J = partition used in the optimal control solution I J = partition used in the optimal control solution I = partition used in the optimal control solution J = performance index Kn = Knudsen number L = scalar objective function describing path parameters M = Mach number m prop = mass of propellant m 0 = initial mass of the vehicle p = influence function vector R = matrix of influence functions r = position vector, r 1 r 2 r 3 T r i = position along the ith direction S j = matrix defining convex state constraints t = time t go = time to go until touchdown u = control vector v = velocity vector, v 1 v 2 v 3 T v i = velocity along the ith direction W = positive definite weighting matrix x = state vector, r T v T m T = mass consumption rate j = scalar defining convex state constraints = weighting on final time to go u = control vector increment "= tolerance level = slack variable bounding thrust magnitude 1 = thrust magnitude lower bound 2 = thrust magnitude upper bound = dust tau (opacity measure of the atmosphere) c = commanded thrust vector j = vector defining convex state constraints = scalar...

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GN&C Technology Needed to Achieve Pinpoint Landing Accuracy at Mars

Cited by 18 publications

References 0 publications

Quaternion Error Based Optimal Attitude Control Applied to Pinpoint Landing

Quaternion Error Based Optimal Attitude Control Applied to Pinpoint Landing

Extension of robust three‐stage Kalman filter for state estimation during Mars entry

Guidance, Navigation, and Control System Performance Trades for Mars Pinpoint Landing

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