Adaptive Neural Network Control of a Flexible Spacecraft Subject to Input Nonlinearity and Asymmetric Output Constraint

Vibration control of a class of flexible mechanical systems with output constraints based on partial differential equations

Vibration suppression in flexible mechanical systems (FMSs) significantly enhances the precision and stability of equipment, extending its operational life. This technology is extensively applied across various sectors, including aerospace, robotics, and precision manufacturing. This paper introduces a robust control scheme leveraging a Partial Differential Equations (PDEs) model and Barrier Lyapunov Function (BLF), applied through backstepping technology to manage a flexible mechanical system modeled as an Euler-Bernoulli beam with a centrally attached rigid body. The control laws we have formulated are designed to effectively dampen vibrations and rotations, thereby ensuring system stability despite the presence of environmental disturbances. Throughout the control process, the system output consistently stays within the predefined safety limits. Comparative simulations further validate the effectiveness of the proposed control strategy, showing that it can effectively counteract unforeseen disturbances while ensuring that the output remains within the specified constraints.

Dual Flexible Prescribed Performance Control of Input Saturated High-Order Nonlinear Systems

This article first presents a dual flexible prescribed performance control (DFPPC) approach of input saturated high-order nonlinear systems (IS-HONSs). Compared to the existing PPC approaches of IS-HONSs, under which the performance constraint boundaries (PCBs) are usually fixed and bounded, resulting in a restriction of the initial error in the algorithm implementation; in addition, the coupling relationship between performance constraints and input saturation is usually ignored, resulting in the methods are very fragile when input saturation occurs. By designing the novel tensile model-based PCBs that depend on output and input constraints, the proposed DFPPC method provides sufficient resilience for both the initial conditions and the input saturation, so that the proposed DFPPC method can not only be suitable for multiple types of initial errors by adjusting the parameters, including , , and , where , and denote the initial PCBs; but also can achieve a good balance between input saturation and performance constraints, i.e., when the control input reaches or exceeds the saturation threshold, the PCBs can adaptively extend to avoid the singularity, and when the control input returns to the saturation threshold range, the PCBs are then adaptively restored to the original PCBs. The results show that the proposed DFPPC algorithm guarantees semi-global boundedness for all closed-loop signals, while ensuring that the system output accurately tracks the desired signal, and it consistently maintains the tracking error within the PCBs. The developed algorithm is illustrated by means of simulation instances.

PDE-Based Boundary Adaptive Consensus Control of Multiagent Systems With Input Constraints

The leader-follower adaptive consensus control problem is addressed for partial differential equations (PDEs) multiagent systems (MASs), and these agents are composed of flexible manipulator systems with input nonlinearity, boundary uncertainties, and time-varying disturbances. Because of the spatial variables in the model, the design of adaptive protocols is more difficult than that of ordinary differential equation (ODE) MASs. By designing the Lyapunov function, a novel distributed boundary control (BC) protocol is constructed, which not only ensures the consensus of angular positions but also suppresses the boundary vibration of each agent. The hybrid effects of dead zones and input saturation on flexible manipulator systems are addressed using the approximation properties of neural networks (NNs). In addition, the disturbance adaptive laws are proposed to provide a control solution for bounded and time-varying disturbances. Furthermore, by applying the Lyapunov stability theory, the uniformly bounded stability of the multiflexible manipulator can be ensured. Finally, the feasibility of the presented control approach is verified using numerical examples.

Robust tracking control of a flexible manipulator with limited control input based on backstepping and the Nussbaum function

A flexible manipulator is a versatile automated device with a wide range of applications, capable of performing various tasks. However, these manipulators are often vulnerable to external disturbances and face limitations in their ability to control actuators. These factors significantly impact the precision of tracking control in such systems. This study delves into the problem of attitude tracking control for a flexible manipulator under the constraints of control input limitations and the influence of external disturbances. To address these challenges effectively, we first introduce the backstepping method, aiming to achieve precise state tracking and tackle the issue of external disturbances. Additionally, recognizing the constraints posed by control input limitations in the flexible manipulator's actuator control system, we employ a design approach based on the Nussbaum function. This method is designed to overcome these limitations, allowing for more robust control. To validate the effectiveness and disturbance rejection capabilities of the proposed control strategy, we conduct comparative numerical simulations using MATLAB/Simulink. These simulations provide further evidence of the robustness and reliability of the control strategy, even in the presence of external disturbances and control input limitations.

Asymmetric Input-Output Constraint Control of a Flexible Variable-Length Rotary Crane Arm

This article demonstrates the realization of angle tracking and deformation suppression by developing two boundary controllers for a flexible variable-length rotary crane arm with extraneous disturbances and asymmetric input-output constraints. The dynamic model description of this kind of crane arm system is several partial differential equations integrated into few ordinary differential equations. The S-curve acceleration and deceleration scheme is utilized to adjust the elongation rate of the arm. A kind of novel observer is put forward to tackle unknown extraneous disturbances. Auxiliary systems and barrier Lyapunov functions are introduced to meet the asymmetric input-output constraints. With the help of Lyapunov's theory, the global exponential stability and uniform boundedness are analyzed. The numerical simulations are finally provided to illuminate its availability of the designed control schemes.

The Dichotomy of Neural Networks and Cryptography: War and Peace

In recent years, neural networks and cryptographic schemes have come together in war and peace; a cross-impact that forms a dichotomy deserving a comprehensive review study. Neural networks can be used against cryptosystems; they can play roles in cryptanalysis and attacks against encryption algorithms and encrypted data. This side of the dichotomy can be interpreted as a war declared by neural networks. On the other hand, neural networks and cryptographic algorithms can mutually support each other. Neural networks can help improve the performance and the security of cryptosystems, and encryption techniques can support the confidentiality of neural networks. The latter side of the dichotomy can be referred to as the peace. There are, to the best of our knowledge, no current surveys that take a comprehensive look at the many ways neural networks are currently interacting with cryptography. This survey aims to fill that niche by providing an overview on the state of the cross-impact between neural networks and cryptography systems. To this end, this paper will highlight the current areas where progress is being made as well as the aspects where there is room for future research to be conducted.