The appeal of solid propulsion for a lunar lander lies in what engineers call architectural simplicity. A solid rocket motor is essentially a filled pressure vessel: propellant grain, casing, nozzle, igniter. There are no turbopumps, no pressurized feed lines, no valves throttling the flow of fuel and oxidizer, and no cryogenic fluids boiling away in the harsh thermal environment of space. That simplicity translates directly into reliability, and reliability is the currency that matters most when a single ignition opportunity determines whether a mission ends in triumph or in a plume of lunar dust. Mission planners have long been drawn to the idea of a lander that simply lights a solid motor and rides it down.

The catch is equally fundamental. Once a solid motor is ignited, it burns until the propellant is exhausted, and the thrust it produces cannot be throttled, pulsed, or shut down. The vehicle has exactly one chance to get the burn right. This means the ignition time and the resulting burn duration must be predetermined with extraordinary precision, because there is no pilot or autopilot that can ease off the throttle if the trajectory starts to look wrong. Conventional liquid-engine landers, including the Apollo lunar modules and most modern reusable rocket designs, rely on continuous thrust modulation to chase down errors as they develop. A solid-propellant lander forfeits that safety net entirely, which is why the concept has historically been viewed with skepticism for precision landing missions.

Compounding the problem is a subtler enemy: temperature. The performance of a solid propellant is sensitive to the temperature of the grain itself, and the lunar thermal environment—where a landed or orbiting vehicle can swing between searing sunlit heat and deep frigid darkness—induces propellant temperature variations that cause significant errors in the thrust magnitude the motor actually delivers. A motor characterized at one temperature may produce noticeably different thrust at another, and because the burn cannot be modulated, those errors propagate directly into the trajectory. The Inha team identified this thrust uncertainty as the critical failure mode that conventional open-loop guidance, which simply executes a precomputed trajectory, cannot overcome.

The researchers’ answer is to treat the powered descent as a sequential convex programming problem, a class of optimization techniques that has become the workhorse of modern aerospace trajectory design. Convex programming, popularized for planetary landing by the landmark Mars powered-descent guidance work of Behçet Açıkmeşe and colleagues, has the crucial property that the optimization problem can be solved rapidly and reliably to a global optimum, making it suitable for onboard, real-time computation. The lunar lander problem, however, is not naturally convex: fixed-thrust operation and vertical landing constraints introduce complications that must be carefully reformulated. The Inha framework incorporates these critical flight conditions directly, shaping the optimization so that the resulting trajectory respects the physical realities of a solid motor burn and the demanding attitude requirements of a vertical touchdown.

The truly novel contribution, though, is the closed-loop replanning scheme built on top of that optimizer. Rather than trusting the precomputed trajectory, the lander continuously estimates its actual thrust in flight, comparing how fast it is accelerating against how fast it should be. Because the motor’s thrust cannot be changed, the guidance cannot fix errors by adjusting the engine—but it can adjust everything else. The onboard algorithm periodically re-optimizes the entire remaining trajectory, shifting the ignition timing, reshaping the descent profile, and re-targeting the terminal conditions so that the vehicle, burning exactly as it must, still arrives at the surface with the right velocity and attitude for a soft landing. In effect, the software trades flexibility in thrust for flexibility in timing and geometry, re-solving the whole problem every time the estimate of the motor’s behavior changes.

The numerical simulations reported in the paper show that this approach works. Under thrust deviations of up to 1.5 percent—a level that would produce unacceptable landing errors under an open-loop baseline—the replanning algorithm successfully mitigated the severe thrust errors, ensuring precise terminal convergence and reliable soft-landing performance. The horizontal landing error was constrained to within a few meters of the target, a figure that would enable pinpoint landing rather than merely landing somewhere safe within a broad ellipse. For missions targeting specific scientific sites, lava tubes, polar ice deposits, or pre-positioned infrastructure, the difference between a kilometer-scale and a meter-scale landing footprint is the difference between a feasible and an infeasible mission.

The work sits within a rich lineage of lunar descent guidance research stretching back to the Apollo era, when the zero-effort-miss and zero-effort-velocity guidance concepts first put astronauts on the Moon, and to the foundational optimal thrust-programming analyses of the 1960s. What distinguishes the new framework is its marriage of that classical problem—soft landing under a fixed, unthrottleable thrust profile—with the modern machinery of convex optimization and in-flight estimation. Earlier studies had explored solid-propellant arrangements for the lunar soft-landing problem and hybrid rocket alternatives, but the closed-loop, estimation-driven replanning approach addresses the thrust uncertainty problem in a way those open-loop concepts could not.

The implications extend beyond any single lander design. Solid propulsion’s simplicity makes it attractive for small, low-cost lunar missions, including the growing wave of commercial and academic payloads riding on commercial lander platforms, where every valve, pump, and pressurant tank removed from the design is weight, cost, and failure modes eliminated. A guidance framework that can guarantee meter-class accuracy with a motor that behaves like a firework—light it once and it burns—could open precision landing to mission classes that could never afford the complexity of a throttleable liquid engine. The research was supported by funding from the Korea AeroSpace Administration, reflecting national investment in lunar landing capability.

There remain, of course, the usual caveats that separate simulation from flight. The results are numerical, and translating a real-time optimizer into flight software that runs on radiation-tolerant hardware, fuses real sensor data, and survives the communication blackouts and environmental surprises of an actual descent is a formidable engineering task in its own right. But the core insight is durable and, in its way, elegant: when the engine cannot adapt, the plan must. By making the trajectory itself the adaptive element—continuously re-optimized onboard as the true thrust of the motor reveals itself—the Inha team has shown that the simplest propulsion system in the rocketry catalog can be guided to a landing as precise as any throttled engine could achieve. For the next generation of lunar explorers, the smartest part of the spacecraft may matter more than the most sophisticated one.

Subject of Research: Real-time trajectory optimization and closed-loop replanning for a solid-propellant lunar lander during powered descent

Article Title: Real-Time Landing Trajectory Optimization of a Lunar Lander with Solid Propellant

Article References: Seo, J.-M., Kim, H.-J., & Ryoo, C.-K. (2026). Real-Time Landing Trajectory Optimization of a Lunar Lander with Solid Propellant. International Journal of Aeronautical and Space Sciences. https://doi.org/10.1007/s42405-026-01247-y

Image Credits: AI Generated

DOI: 10.1007/s42405-026-01247-y

Keywords: lunar landing, solid propellant, trajectory optimization, sequential convex programming, real-time replanning, powered descent guidance, thrust estimation, soft landing, spacecraft guidance, convex optimization, lunar exploration, aerospace engineering

The study, published in the International Journal of Aeronautical and Space Sciences, was led by Seunghyeon Byeon, Byungjun Kim, Jiwon Lee, Kyeongsun Lim, and Donghun Lee of the Korea Advanced Institute of Science and Technology, together with Gunho Park, Taehun Kim, and Yoonhyuk Choi of Korea Aerospace University. The work was supported by programs of the Korea AeroSpace Administration, and the resulting algorithm is explicitly designed with real flight software in mind rather than as a purely theoretical exercise. The researchers describe it as lightweight, with guaranteed convergence and a constant, predictable computational cost at every guidance cycle, which are precisely the properties mission operators look for when deciding whether a technique can safely run on limited onboard processors.

To understand why the algorithm matters, it helps to consider what an attitude guidance profile actually contains. At each command cycle, the profile specifies a quaternion, a four-parameter representation of the satellite’s orientation relative to the Earth-centered inertial frame, along with the angular velocity vector and the angular acceleration vector that describe how that orientation changes. For a mission lasting several minutes with commands updated every 0.125 seconds, transmitting the complete profile means sending tens of thousands of individual parameters. In the study’s low-dynamic Earth observation example, the conventional full-profile approach required 19,520 transmitted parameters, equivalent to 156,160 bytes in double-precision format; the high-dynamic case required 20,320 parameters, or 162,560 bytes. The proposed method needed only 97 parameters, or 776 bytes, in both cases.

The key insight behind the algorithm is that a typical Earth observation guidance profile has a repetitive, predictable structure: it alternates between maneuvering phases, in which the satellite slews rapidly toward a new target, and mission phases, in which it holds steady to image a ground region. Because each phase has distinct mathematical characteristics, the researchers tailored a different reconstruction technique to each. In the maneuvering phases, the angular acceleration profile is generated on the ground in closed form, following a smoothed shape with finite jerk constraints that ramps acceleration up, holds it, ramps it down, and includes a stabilization period before the mission begins. Since the analytical expression is known, the satellite only needs a handful of parameters, such as the phase start time, the durations of each acceleration segment, and the two peak acceleration vectors, to reconstruct the angular velocity commands analytically.

Reconstructing the quaternion commands during maneuvers is more delicate, because the quaternion kinematic equations are nonlinear and admit no simple analytical integral. The team therefore employed a discrete quaternion propagation model based on eigen-axis rotation, the standard approach described in classical texts on optimal estimation of dynamic systems. To minimize numerical integration error, the model uses the average of the angular velocity vectors at the current and next discrete time steps. A small positive threshold prevents numerical instability when the angular velocity norm approaches zero. Notably, during the stabilization period at the end of each maneuver, the algorithm propagates the quaternion backward from the known final conditions of the maneuvering phase, a clever choice that prevents numerical errors from accumulating before the mission phase begins.

The mission phases pose a different challenge, since no closed-form angular acceleration expression exists there. The researchers developed two alternative reconstruction methods. The quaternion-based method approximates the quaternion profile with a seventh-order polynomial whose coefficients are transmitted to the spacecraft; the angular velocity commands are then obtained analytically by differentiating the polynomial and applying the orthonormality property of the quaternion kinematic matrix. The angular-velocity-based method instead transmits a seventh-order polynomial approximation of the angular velocity profile, from which the quaternion commands are reconstructed through discrete propagation. The polynomial order was chosen through careful analysis: a fifth-order polynomial already satisfied the study’s maximum attitude error requirement of 0.1 degrees, but the seventh order was selected to provide an accuracy margin for more complex missions, while higher orders were rejected because their marginal accuracy gains did not justify the added coefficients, computational burden, and potential numerical instability.

Which of the two mission-phase methods performs better depends on the dynamics of the scenario, and the team’s answer is pragmatic: measure both on the ground and transmit whichever wins. In a comparison across three reference profiles ranging from low to high dynamics, the quaternion-based method achieved mean attitude errors on the order of 10^-5 degrees in the gentlest scenario but degraded sharply to a maximum error of roughly 3.4 degrees in the most aggressive one. The angular-velocity-based method also degraded with increasing dynamics, but far more gracefully, achieving approximately 3.4 times better mean attitude accuracy than the quaternion-based method in the high-dynamics case. This adaptive selection framework allows the reconstruction scheme to be matched to the dynamic character of each mission phase, rather than forcing operators to accept a one-size-fits-all compromise.

The full algorithm was validated in two complete Earth observation scenarios, each combining two maneuvering phases with two mission phases. In the low-dynamic case, the maximum attitude error in the mission phases was 2.9576 times 10^-6 degrees, with a maximum angular velocity error of 8.0200 times 10^-5 degrees per second, occurring only at the boundary instants of the mission phases. In the maneuvering phases, the maximum attitude error was 1.3021 times 10^-3 degrees, and the angular velocity commands showed no error at all because they were reconstructed analytically. The high-dynamic scenario, in which the spacecraft images along a ground track oriented opposite to its orbital motion, produced a maximum mission-phase attitude error of 9.0725 times 10^-5 degrees using the quaternion-based method, again comfortably within the 0.1-degree requirement, with a maximum angular velocity error of 1.0307 times 10^-3 degrees per second.

Beyond raw accuracy, the algorithm’s onboard computational profile is a central part of its appeal. Every operation in the reconstruction loop consists of a fixed number of arithmetic operations per time step: polynomial evaluation, differentiation, or a single quaternion propagation update. There are no iterative optimizations, no complex matrix decompositions, and no convergence loops whose runtime might vary unpredictably. This stands in sharp contrast to the numerical optimal control methods often used to generate three-axis slew maneuvers on the ground, which are computationally expensive and generally unsuitable for flight software, and to the analytical three-axis approaches that are efficient but rely on restrictive assumptions and yield only approximate solutions. By shifting the heavy computation to the ground and reserving only lightweight reconstruction for the spacecraft, the algorithm occupies a practical middle ground.

The implications extend across the growing fleet of agile imaging satellites, including smallsat and cubesat constellations that demand frequent retargeting maneuvers and operate under tight downlink budgets. Reducing attitude command transmissions from hundreds of kilobytes to under a kilobyte frees bandwidth for payload data, shortens command upload times, and could enable more responsive tasking of Earth observation assets. The authors note that their numerical examples validated the algorithm’s efficiency and accuracy specifically for Earth observation scenarios, and that the method is designed for guidance profiles with alternating maneuvering and mission phases. For satellites that must retarget often and precisely, the study suggests that the smartest command to send up may be not the maneuver itself, but the compact recipe for rebuilding it onboard.

Subject of Research: Onboard satellite attitude command reconstruction for reduced guidance data transmission in Earth observation missions

Article Title: Efficient Attitude Command Reconstruction Algorithm for Satellites Operations

Article References: Byeon, S., Kim, B., Park, G., Lee, J., Lim, K., Kim, T., Choi, Y., & Lee, D. (2026). Efficient Attitude Command Reconstruction Algorithm for Satellites Operations. International Journal of Aeronautical and Space Sciences. https://doi.org/10.1007/s42405-026-01245-0

Image Credits: AI Generated

DOI: 10.1007/s42405-026-01245-0

Keywords: satellite attitude control, attitude guidance profile, command reconstruction, Earth observation, quaternion kinematics, onboard algorithm, slew maneuvers, spacecraft guidance, data transmission efficiency, optimal control, flight software, Korea Aerospace Administration

The scenario the researchers studied is a three-body engagement involving the target, the pursuer, and the defender. Once the defender is deployed, two coupled pursuit–evasion relationships emerge simultaneously: the pursuer chases the target, while the defender chases the pursuer in an attempt to spoil the interception. Because the defender bends the pursuer’s trajectory away from the target, the two friendly vehicles must act in a coordinated fashion rather than as independent agents. The difficulty is compounded by the fact that the pursuer may employ any of several established interception laws, including optimal control guidance, proportional navigation, or differential game guidance. A defense scheme tuned to a single assumed attack strategy can be defeated the moment the adversary switches tactics. At the same time, neither the target nor the defender has access to complete state information; both must rely on noisy local measurements of range and line-of-sight angles, which makes the problem one of partial observability as well as multi-strategy adversarial behavior.

Existing guidance approaches fall short in this setting for distinct reasons. Unilateral optimal control methods assume a known, fixed adversary model and cannot adapt when the pursuer changes strategy mid-engagement. Differential game formulations deliver elegant theoretical solutions but typically presume perfect information on both sides, an assumption that collapses in the presence of sensor noise and hidden intentions. Conventional reinforcement learning, meanwhile, has shown promise in adversarial settings, but standard algorithms struggle to converge when observations are incomplete and the opponent’s policy varies across encounters. The Sun Yat-sen team identified this triple challenge—unknown pursuit strategies, information deficiency, and high-maneuverability confrontation—as the key technological bottleneck standing in the way of practical, cooperative active defense for spacecraft.

To overcome it, the researchers reframed the three-body engagement as a multi-agent partially observable Markov decision process, or Multi-POMDP. In this formalism, each agent receives only a noisy local observation rather than the true global state, and the opponent’s policy is treated as uncertain and potentially drawn from a set of diverse strategies. The solution architecture is a reinforcement learning guidance framework built on an adaptive dueling double deep Q-network, abbreviated AD3QN. The central idea is that a single neural network learns, from experience, how to issue coordinated maneuver commands to both the target and the defender, so that the pair adapts on the fly to whatever interception strategy the pursuer happens to be executing. The framework separates perception from decision-making: first the raw, incomplete observation history is transformed into a compact situational representation, and then that representation drives the selection of acceleration commands.

The perception pipeline is a fusion of two well-established neural components. Current and historical incomplete observations are stacked along the time dimension into a two-dimensional tensor, which a convolutional neural network processes to extract spatial features—the critical geometric signatures of an unfolding engagement, such as the configuration of lines of sight among the three vehicles. A gated recurrent unit then models the temporal structure of those features, using its internal update and reset gates to produce a history tensor that encapsulates the trajectory characteristics of the encounter. This history encoding is what allows the policy to infer which pursuit strategy the adversary appears to be following, something no single-frame observation can reveal. To make training on partial information effective, the team restructured the experience replay buffer so that it stores the stacked observation tensor, the action taken, the history tensor, and the resulting reward, enabling the network to exploit temporal correlations during learning rather than treating each decision as an isolated snapshot.

The decision-making core of AD3QN builds on the dueling double deep Q-network formulation, in which the state value function and the action advantage function are estimated separately. This decomposition reduces the variance of value estimates, a property that proves crucial in multi-strategy environments where the consequences of a maneuver differ sharply depending on the adversary’s current policy. Equally important is the reward design. Rather than rewarding success only at the final moment of the engagement, the researchers constructed a continuous reward function based on potential-field differences computed from the zero-effort miss distance—the miss distance that would result if both vehicles stopped maneuvering. Before the defender–pursuer encounter, the reward landscape encourages the defender to shrink its zero-effort miss distance relative to the pursuer, driving it toward interception. After that encounter, the shaping switches, guiding the target to enlarge its zero-effort miss distance from the pursuer and thereby accomplish evasion. The authors provide a theoretical proof that this reward formulation does not alter the optimal policy, which means the shaping improves training stability without sacrificing optimality.

The numerical validation compared AD3QN against a demanding field of baselines, including Deep Deterministic Policy Gradient, Twin Delayed Deep Deterministic Policy Gradient, Proximal Policy Optimization, and Deep Recurrent Q-Learning. Under the combined stresses of multi-strategy adversaries and incomplete information, these mainstream algorithms all struggled to achieve stable policy optimization, whereas AD3QN converged reliably thanks to its fusion architecture and its explicit handling of observation history. Computational efficiency is a decisive factor for flight implementation, and here the method posted a striking result: a decision frequency of 85 Hz in a simulated single-chip microprocessor environment, roughly 30 percent faster than the DRQN comparison and comfortably within the real-time requirements of spacecraft actuation systems. In representative sample engagements, the defender successfully deflected the pursuer’s trajectory by approximately 20 meters relative to the target—a deviation large enough to convert a lethal interception into a clean miss.

Statistical robustness was assessed through Monte Carlo analysis. Across 1,000 randomized simulations, the proposed method achieved an evasion success rate of 99.7 percent, dramatically outperforming the optimal switching cooperative guidance law at 51.8 percent and various reinforcement learning baselines, which fell below 0.2 percent. The robustness tests are perhaps the most compelling evidence of practical value: even when observation noise was inflated to 40 times the typical level—with range noise of 400 meters and line-of-sight angle noise of 40 milliradians—the method still maintained an evasion success rate of 73.5 percent. Parameter sensitivity studies added further engineering insight. When the pursuer’s maneuvering capability increased, the target’s achievable miss distance fell by roughly 30 percent, but boosting the defender’s agility effectively compensated, improving evasion performance. This trade-off quantifies a design principle for future defensive architectures: investment in the defender’s maneuverability can offset a faster adversary.

The broader significance of the study lies in demonstrating that cooperative, adaptive active defense is achievable under realistic sensing conditions rather than idealized perfect information. By combining a Multi-POMDP problem formulation, a CNN–GRU fusion network for perception under partial observability, dueling double Q-learning for stable value estimation in adversarial settings, and a theoretically sound potential-field reward, the Sun Yat-sen team has assembled a guidance framework that is simultaneously adaptive, robust, and computationally light enough for onboard implementation. For operators of high-value satellites in congested orbital regions, the work suggests a path toward survivability that does not depend on predicting the attacker’s playbook in advance. As orbital confrontation scenarios grow more complex, methods of this kind—able to learn coordinated counter-interception from noisy observations and to withstand sensor degradation far beyond nominal levels—are likely to become a cornerstone of spacecraft autonomy and space security engineering.

Subject of Research: Active defense guidance for spacecraft in three-body pursuit–evasion engagements with incomplete information and multi-strategy adversaries

Article Title: Active defense guidance for spacecraft in multi-strategy engagement with incomplete information

Article References: Active defense guidance for spacecraft in multi-strategy engagement with incomplete information. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: spacecraft guidance, active defense, pursuit-evasion, reinforcement learning, deep Q-network, partial observability, Multi-POMDP, space security, zero-effort miss distance, convolutional neural network, gated recurrent unit, spacecraft survivability