We propose the Polytopic Receding-Horizon Policy Gradient (P-RHPG) algorithm for synthesizing Parallel Distributed Compensation (PDC) controllers via Tensor Product (TP) model transformation. Standard LMI-based PDC synthesis grows increasingly conservative as model fidelity improves; P-RHPG instead solves a finite-horizon integrated cost via backward-stage decomposition. The key result is that each stage subproblem is a strongly convex quadratic in the vertex gains, a consequence of the linear independence of the HOSVD weighting functions, guaranteeing a unique global minimizer and linear convergence of gradient descent from any initialization. With zero terminal cost, the optimal cost increases monotonically to a finite limit and the gain sequence remains bounded; terminal costs satisfying a mild Lyapunov condition yield non-increasing convergence. Experiments on an aeroelastic wing benchmark confirm convergence to a unique infinite-horizon optimum across all tested terminal cost choices and near-optimal performance relative to the pointwise Riccati lower bound.

It is often imperative to test and validate the development of active flutter suppression technology in wind tunnels or in flight near and beyond the flutter speed. This is done not only to fine-tune mathematical models but also to examine the active control laws that were synthesized from such models. However, in-flight flutter tests are often dangerous, costly, and sometimes infeasible. Wind tunnel tests are usually more affordable and less risky, leading to higher productivity in evaluating alternative configurations and control law strategies. The University of Washington's MARGE-I aeroservoelastic wind tunnel model is a flexible half-wing-body-tail model with active ailerons and an elevator. The work here studies SISO mixed-sensitivity H infinity control for active flutter suppression, which is designed subject to requirements and hardware constraints. Such a controller is validated by wind tunnel tests, where the closed-loop margins in both simulation and experiments are quantified and compared. A previously published safe wind tunnel and flight test technique is used to generate flutter mechanisms. The set of control surfaces is partitioned into destabilizing and stabilizing ones, where a destabilizing controller reshapes the aeroservoelastic system to yield the desired unstable dynamic characteristics, and the stabilizing controller is used to study active flutter control strategies.

The reconfiguration of origami during the folding and unfolding process is governed through a sequence of panel deformations and hinge orientations. To develop an effective model for representing the reconfiguration process, this paper introduces planar straight-line graphs and a novel consensus protocol for reaching the target origami configuration. The convergence and stability properties of the proposed consensus protocol are subsequently analyzed. Furthermore, to account for aggregate material and structural effects in the proposed consensus-based reconfiguration model, effective parameters embedded in the consensus protocol are identified from trajectory data using a fitting algorithm. Lastly, the effectiveness of the proposed modeling approach is shown using simulations of the two-panel structure and the Kresling origami pattern reconfiguration process.

The work is motivated by a recently published formal proof establishing that the exploration of an tecedent–consequent structures is necessary for guaranteeing feasibility, reducing conservatism, and establishing optimality in Linear Matrix Inequalities (LMIs)-based Parallel Distributed Compensation (PDC) control design. Therefore, this paper presents a comparative effectiveness analysis of the original Takagi–Sugeno (TS) fuzzy model transformation and the recently proposed TS fuzzy model transition and input-space manipulation methods, all developed for the comprehensive exploration of antecedent–consequent structures. The investigation is further motivated by the fact that these recently proposed methods have so far been studied predominantly at a theoretical level, while comparative practical modeling and control-design insights remain limited. Therefore, the Translational Oscillator with a Rotational Actuator (TORA) benchmark system is employed as a representative case study. Although TORA is selected, the revealed tendencies originate from the underlying convex hull manipulation principles and are not specific to the TORA system itself. The analysis focuses on the manipulability of the convex hulls spanned by the consequent vertices of TS fuzzy models, including minimization or optimization of the number of vertices, manipulation of convex-hull tightness or looseness, parameter-dependent convex-hull tunability, and manipulation of the dimensionality of the TS fuzzy model input space, all of which directly influence the achievable antecedent–consequent structure. The most important conclusion of the paper is that the combined application of these approaches is very effective for systematically exploring the achievable control solutions to identify the controller that best satisfies the control-performance requirements.

Rank minimization is a fundamental optimization framework that underlies many modern control design problems including model reduction, sensor and actuator placement, and distributed controller design. This survey consolidates classical results and recent advances, with a special focus on their applications to networked control systems (NCS) where communication constraints, delays, and topology play a critical role. We review problem formulations, mathematical background, various applications, solution strategies, computational tools, and finally, illustrate the concepts through representative examples.

In this paper, an optimization-based framework for generating estimation-aware trajectories is presented. In this setup, measurement (output) uncertainties are state-dependent and set-valued. Enveloping ellipsoids are employed to characterize state-dependent uncertainties with unknown distributions. The concept of regularity for set-valued output maps is then introduced, facilitating the formulation of the estimation-aware trajectory generation problem. Specifically, it is demonstrated that for output-regular maps, one can utilize a set-valued observability measure that is concave with respect to finite-horizon state trajectories. By maximizing this measure, estimation-aware trajectories can then be synthesized for a broad class of systems. Trajectory planning routines are also examined in this work, by which the observability measure is optimized for systems with locally linearized dynamics. To illustrate the effectiveness of the proposed approach, representative examples in the context of trajectory planning with vision-based estimation are presented. Moreover, the paper presents estimation-aware planning for an uncooperative target rendezvous problem, where an Ego satellite employs an onboard machine-learning-based estimation module to realize the rendezvous trajectory.

We study policy optimization for gain-scheduled linear quadratic regulation, where one schedule of gains, interpolated through fixed weighting functions, is optimized against a family of plants. The resulting cost can develop spurious local minima, and existing convergence certificates are either local or severely conservative. We establish an exact identity: when the gradient of the cost is evaluated with the minimizer's closed-loop covariances, the scheduled cost is star-convex about the minimizer. The identity holds on the entire feasible set, for any parametrization of the schedule. Convergence is governed by a single dimensionless ratio. Wherever the ratio satisfies a threshold condition, gradient descent converges linearly to the optimum on entire sublevel regions at an explicit rate; at every spurious stationary point the condition necessarily fails. Experiments that maximize the ratio directly show the threshold to be an active boundary of the landscape. This extended version contains the complete proofs and additional numerical studies omitted from the letter for space.

This paper examines a robust data-driven approach for the safe deployment of systems with nonlinear dynamics using their imperfect digital twins. Our contribution involves proposing a method that fuses the digital twin's nominal trajectory with online, data-driven uncertainty quantification to synthesize robust tracking controllers. Specifically, we derive data-driven bounds to capture the deviations of the actual system from its prescribed nominal trajectory informed via its digital twin. Subsequently, the dataset is used in the synthesis of quadratic funnels -- robust positive invariant tubes around the nominal trajectory -- via linear matrix inequalities built on the time-series data. The resulting controller guarantees constraint satisfaction while adapting to the true system behavior through a segmented learning strategy, where each segment's controller is synthesized using uncertainty information from the previous segment. This work establishes a systematic framework for obtaining safety certificates in learning-based control of nonlinear systems with imperfect models.

We investigate the controllability of an origami system composed of Miura-ori cells. Extensive research has been conducted on the folding architecture, kinematic behavior, and actuation techniques of origami structures. However, understanding their transient dynamics and constructing control models remains a formidable task, primarily due to their innate flexibility and compliance. In light of this challenge, we discretize the origami system into a network composed of interconnected particle masses alongside bar and hinge elements. This yields a state-space representation of the system’s dynamics, facilitating the analysis of the system’s controllability properties. Informed by this computational framework, we explore the controllability Gramian-based method to find the most efficient crease lines for the deployment of single and tessellated Miura-ori cells using servo-motor actuators. We demonstrate that the deployment efficiency guided by this theoretical method shows good agreement with the empirical results derived from the control effort in deploying the origami prototypes. This investigation paves the way toward the efficient design and operation of complex actuation systems for origami-based deployable structures.

The dual quaternion guidance (DQG) algorithm was selected as the candidate 6-DoF powered-descent guidance algorithm for NASA's Safe and Precise Landing – Integrated Capabilities Evolution (SPLICE) project. DQG is capable of handling state-triggered constraints that are of utmost importance in terms of enabling technologies such as terrain relative navigation. In this work, we develop a custom solver for DQG to enable onboard implementation for future rocket landing missions. We describe the design and implementation of a real-time-capable optimization framework, called sequential conic optimization (SeCO), that blends together sequential convex programming and first-order conic optimization to solve difficult nonconvex trajectory optimization problems, such as DQG, in real-time. A key feature of SeCO is that it leverages a first-order primal-dual conic optimization solver, based on the proportional-integral projected gradient method (PIPG). We describe the implementation of this solver, develop customizable first-order methods, and leverage convergence-accelerating strategies such as warm-starting and extrapolation, to solve the nonconvex DQG optimal control problem in real-time. Finally, in preparation for an upcoming closed-loop flight test campaign, we test our custom solver onboard the NASA SPLICE Descent and Landing Computer in a hardware-in-the-loop setting. We observe that our algorithm is significantly faster than previously reported solve-times using the flight-tested interior point method-based subproblem solver, BSOCP. Furthermore, our custom solver meets (and exceeds) NASA's autonomous precision rocket-landing guidance update-rate requirements for the first time, thus demonstrating the viability of SeCO for real-time, mission-critical applications onboard computationally-constrained flight hardware.

Assignment problems are a classic combinatorial optimization problem in which a group of agents must be assigned to a group of tasks such that maximum utility is achieved while satisfying assignment constraints. Given the utility of each agent completing each task, polynomial-time algorithms exist to solve a single assignment problem in its simplest form. However, in many modern-day applications such as satellite constellations, power grids, and mobile robot scheduling, assignment problems unfold over time, with the utility for a given assignment depending heavily on the state of the system. We apply multi-agent reinforcement learning to this problem, learning the value of assignments by bootstrapping from the known polynomial-time greedy solver and then learning from further experience. We then choose assignments using a distributed optimal assignment mechanism rather than by selecting them directly. We demonstrate that this algorithm is theoretically justified and avoids pitfalls experienced by other RL algorithms in this setting. Finally, we show that our algorithm significantly outperforms other methods in the literature, even while scaling to realistic scenarios with hundreds of agents and tasks.

We examine an effective dynamic reconfiguration model for triangulated origami structures using planar straight-line graphs. In this setup, the origami panels are first represented as edges and vertices in an undirected triangular graph. A triangulated consensus protocol for the corresponding origami formation control problem is then developed, where state of the nodes reach agreement on target configuration while ensuring that the reconfiguration process is realizable by each panelthe setup is then extended to the entire triangulated origami structure. The proposed approach provides a general graph-theoretic framework for expressing the geometric evolution of diverse origami patterns during the folding/unfolding process.

As satellite constellations grow in size, there is an increasing need for autonomous, scalable, and real-time dynamic task assignment to address the unique operational challenges of such distributed systems. In particular, a time-varying task assignment (i.e., for observing various regions of Earth) often means that the corresponding satellite has to reorient itself or its sensors, costing time and energy. However, most assignment algorithms for area requests proposed in the literature do not account for the significant cost associated with task handover in satellite constellations. In this paper, we develop a framework for solving the seemingly non-deterministic polynomial-time (NP)-hard problem of optimal dynamic task allocation while minimizing task handover. In particular, we develop Handover-Aware Assignment with Lookahead (HAAL), an algorithm with centralized and distributed variants, and solutions that provably achieve 50% of the optimal value. We then proceed to show that HAAL significantly outperforms similar heuristic methods proposed in the literature for realistic constellation experiments with up to a thousand satellites. The algorithm scales polynomially in the number of satellites/tasks and offers a smooth tradeoff between computational efficiency and performance, allowing the designer to tune the algorithm based on available computing resources, communication bandwidth, and required performance.

Achieving average consensus without disclosing the initial agents' state is critical for secure multi- agent coordination. This paper proposes a novel privacy-preserving average consensus algorithm via a matrix-weighted inter-agent coupling mechanism. Specifically, the algorithm first lifts each agent state to a higher-dimensional space, then employs a dedicatedly designed matrix-valued state coupling mechanism to conceal the initial agents' state while guaranteeing that the multi-agent network achieves average consensus. The convergence analysis is transformed into the average consensus problem on matrix-weighted switching networks with low-rank, positive semi-definite coupling matrices. We show that the average consensus can be guaranteed and discuss its performance in the presence of honest-but-curious agents and external eavesdroppers. The algorithm, involving only basic matrix operations, is computationally more efficient than cryptography-based approaches and can be implemented without relying on a centralized third party. Numerical results are provided to illustrate the effectiveness of the algorithm. (c) 2024 Published by Elsevier Ltd.

This paper presents a numerical optimization algorithm for generating approach and landing trajectories for a six-degree-of-freedom (6-DoF) aircraft. We improve on the existing research on aircraft landing trajectory generation by formulating the trajectory optimization problem with additional real-world operational constraints, including 6-DoF aircraft dynamics, runway alignment, constant wind field, and obstacle avoidance, to obtain a continuous-time nonconvex optimal control problem. Particularly, the runway alignment constraint enforces the trajectory of the aircraft to be aligned with the runway only during the final approach phase. This is a novel feature that is essential for preventing an approach that is either too steep or too shallow. The proposed method models the runway alignment constraint through a multiphase trajectory planning scheme, imposing alignment conditions exclusively during the final approach phase. We compare this formulation with the existing state-triggered constraint formulation for runway alignment. To solve the formulated problem, we design a novel sequential convex programming algorithm called extrapolated penalized trust region that extends the penalized trust-region algorithm by incorporating an extrapolation step to expedite convergence. We validate the proposed method through extensive numerical simulations, including a Monte Carlo study, to evaluate the robustness of the algorithm to varying initial conditions.

A fundamental issue at the core of trajectory optimization on smooth manifolds is handling the implicit manifold constraint within the dynamics. The conventional approach is to enforce the dynamic model as a constraint. However, we show this approach leads to significantly redundant operations, as well as being heavily dependent on the state space representation. Specifically, we propose an intrinsic successive convexification methodology for optimal control on smooth manifolds. This so-called iSCvx is then applied to a representative example involving attitude trajectory optimization for a spacecraft subject to non-convex constraints.

We present Maratus-a proposed 12U cubesat far ultraviolet narrow-band imager, centered on 1350 angstrom to map the circumgalactic medium (CGM). We primarily target O vi emission, likely the brightest tracer of the 10(5) - 10(6) K gas surrounding galaxies, at z similar to 0.3. Combining flight-proven hardware with recent technological improvements, we tackle one of the most interesting questions bridging large scale structure and galaxy evolution in our current moment - "How does gas flow into and out of g alaxies?" Answering t his question i s c rucial f or understanding the regulation of star formation, and the flow o f m atter, e nergy, a nd m etals t ravelling b etween g alaxies and the intergalactic medium. Maratus is a proof of principle instrument that will pave the way for large-scale mapping of the intergalactic medium. Mapping the CGM is identified a s a k ey d iscovery a rea i n t he recent astrophysics decadal report. By using COS-Halos galaxies for our targeted survey, we present the first opportunity to characterize a key metal tracer of the CGM in both emission and absorption.

Control of networked systems, comprised of interacting agents, is often achieved through modeling the underlying interactions. Constructing accurate models of such interactions–in the meantime–can become prohibitive in applications. Data-driven control methods avoid such complications by directly synthesizing a controller from the observed data. In this paper, we propose an algorithm referred to as Data-driven Structured Policy Iteration (D2SPI), for synthesizing an efficient feedback mechanism that respects the sparsity pattern induced by the underlying interaction network. In particular, our algorithm uses temporary “auxiliary” communication links in order to enable the required information exchange on a (smaller) sub-network during the “learning phase”—links that will be removed subsequently for the final distributed feedback synthesis. We then proceed to show that the learned policy results in a stabilizing structured policy for the entire network. Our analysis is then followed by showing the stability and convergence of the proposed distributed policies throughout the learning phase, exploiting a construct referred to as the “Patterned monoid.” The performance of D2SPI is then demonstrated using representative simulation scenarios.