Micro-nano satellites offer compact size and cost benefits but are limited in orbital control due to constrained propulsion systems. This paper proposes a novel atmospheric differential drag control method for propulsion-free constellation on-orbit control. High-drag and low-drag operational modes are designed to modulate aerodynamic forces via frontal area adjustment. An analytical model predicts relative orbital phase changes, and a time-optimal optimization model using a command matrix is developed. A variable-length hidden-genes genetic algorithm is introduced to handle integer variables and enhance search efficiency. Simulations show that the method achieves uniform phase distribution for a 10-satellite constellation at 500 km altitude within 165 days, with superior computational efficiency compared to conventional genetic algorithms.

In this paper, we exclusively consider the simple case of a 2nd order real square matrix A and define a new set of indices labeled as Non-Linear Algebraic (NLA) eigenvalues in contrast to the current literature Linear Algebraic (LA) eigenvalues. In this new NLA viewpoint, we highlight the importance of Commutativity Condition (CC) satisfaction, which is ignored by current literature linear algebra, matrix theory textbook statements and theorems. Under this new NLA viewpoint, the current literature linear algebra concepts such as Rank, Condition Number and the Singular Value Decomposition are not invoked (or not needed) and instead, we use new indices introduced in this paper, labeled as Non-Linear Algebraic (NLA) eigenvalues. Using these new indices, we provide counterexamples to one LA eigenvalue property stated in the current literature linear algebra and matrix theory textbooks, namely the Linearity property that λ i (kA) = k * λ i (A). This counterexample is deemed to have significant impact on the way we assess the real state variable convergence of any real Linear Time Invariant State Space (LTISS) system.

Climbing is a physically demanding and limb coordination requirement task which is often required in special activities. Although many pieces of equipment have been developed to help people during climbing, most of them are passive without the ability to provide active assistance or lack flexibility resulting in restricted movement for people. This paper introduces an active upper-limb assistive suit aimed for climbing movement. In order to simultaneously maintain movement flexibility and provide Effective assistance, the actuation system is developed based on a cable driven approach. The actuator’s output is connected to a customized glove through a Bowden cable. This configuration takes the wearer’s hand serving as an anchor point, thereby enhancing the suit’s flexibility and simplifying control. A cascade control framework is implemented to fulfill the assistance control strategy. The upper-level is an assistive force profile generator that employs an impedance control algorithm. This algorithm uses position displacement as its input and generates the desired assistive force as output. The lower-level is a force profile tracker, which combines admittance control with a PI velocity control algorithm. The feasibility and Effectiveness of our developed device are verified through pull-up movement. Experimental results show that muscle activations for Trapezius, Latissimus Dorsi, Infraspinatus, Biceps Brachii and Pectoralis Major reduce by 17.1%, 13.4%, 18.5%, 7.3% and 11.6% respectively.

The problem of controlling nonlinear systems with unknown model is considered. A testing and design process that leads to the construction of robust state-feedback controllers for such systems is described. The framework presented in the note is based on the use of bang-bang controllers, and it applies to a wide range of nonlinear systems.

In this study, we address the challenge of detecting faults in the rotation of the spray arm in non-connected domestic dishwashers using highly noisy accelerometer data from a commercial MEMS sensor. We propose a Deep Neural Network model optimized to run in an entry-level, single-core microcontroller unit (MCU) aimed at discriminating between blocked and free states of the arm component. The proposed Embedded-AI classifier, also referred to as the 1DCNN-MLP model, is verified to be suitable for the selected hardware by a Processor-in-the-loop profiler. It is intended to execute multiple times during the washing cycle to adjust appliance control and notify the customer of necessary interventions, thereby preventing cycle repetition and saving electricity, water, and time.