Prescribed-time output feedback for stochastic nonholonomic systems

Security-conscious dynamic output feedback control for periodic piecewise nonlinear systems under output-reliant disturbance observer

This article investigates the exponential secure synchronization problem of Markovian jumping delayed inertial neural networks (INNs) under hybrid attacks, which is applied in signal encryption. The novel adaptive proportional-derivative (PD)-like event-triggered mechanism (APDETM), including the variational tendency of states, is proposed by adopting both proportional and derivative terms, aiming to further filter out redundant sampling data while preserving effective system performance. In addition, the INNs with generally uncertain semi-Markovian (GUSM) jumping parameters are established under denial-of-service (DoS) and deception attacks (DAs). Meanwhile, the event-triggered output feedback controllers are designed to achieve the secure synchronization control of the drive and response systems subject to hybrid attacks. Based on the chaotic behaviors of the INNs, the event-triggered synchronization conditions are applied to the signal encryption field. Finally, two examples, including a numerical simulation and an audio encryption process, are shown to demonstrate the effectiveness of the proposed methods.

This paper presents an output feedback discrete-time sliding mode control for an uncertain sampled-data system using a novel multi-rate estimator. In the existing works, the exact state estimation of an uncertain system is not possible due to the presence of disturbance in the multi-rate estimator. Hence, the desired performance is not achieved under multi-rate output feedback sliding mode control. In this paper, we propose a novel multi-rate estimation algorithm based on the decoupling approach that estimates the unknown plant states exactly despite the disturbance. Here, we transform the system into cascaded subsystems under the assumption that each of these subsystems is observable. This assumption on (stronger) observability is well known in the continuous-time framework, and it guarantees the estimation of the unknown (partial) state vector in one time step. Using these estimated values, an output feedback discrete-time sliding mode control is designed using Gao's reaching law to guarantee the same desired performance as that of the state-feedback controller. The simulation results are presented to demonstrate the system performance under the proposed algorithm.

Global exact prescribed-time output feedback stabilization of a class of nonlinear systems with measurement uncertainty by linear time-varying feedback.

Output feedback-based adaptive position control for hydraulic systems with preset performance.

This paper addresses a generic method for optimisation problems subject to bilinear matrix inequalities (BMIs), with application to dynamic output feedback (DOF) synthesis. Such problems are inherently nonconvex and NP-hard. To tackle this challenge, we develop an inner-approximation strategy that iteratively searches for a convex surrogate within a family of alternative constraints. The proposed algorithm features adaptive feasible sets and is equipped with a convergence guarantee. In a generic algorithmic framework to solve fixed-order DOF control problems for linear systems, we formulate an iterative procedure for calculating the control law. Numerical experiments on benchmarks from the COMPleib library demonstrate the effectiveness and superiority of the proposed approach.

This paper studies the problem of adaptive fuzzy output feedback funnel secure control for vehicle platoon systems under replay attacks, which contains nonlinear dynamics and unmodeled dynamic. Fuzzy system is adopted to identify unknown nonlinear dynamics, then fuzzy observer is designed to estimate the immeasurable states, and funnel functions are introduced to constrain the distance between each vehicle. By introducing Lipschitz conditions, it is possible to analyse the error changes of systems subjected to replay attacks and obtain the threshold for error changes. By using the changing supply function technique to address the unmodeled dynamics, an observer-based robust secure adaptive fuzzy funnel formation control scheme is developed. Based on Lyapunov stability theory, it can ensure all signals of the controlled system are bounded, and the desired spacing and avoid collision can be maintained. Finally, a simulation is considered to verify the effectiveness of the developed control algorithm.

Robust Koopman EMPC for optimal frequency regulation of VSC-MTDC systems

New results on observer-based output feedback dissipative control of neural networks by piecewise affine models

Observer-based output feedback stabilization of an ODE-heat coupled system with non-local term and boundary disturbance via modal decomposition

Fixed-time adaptive output feedback control of stochastic nonlinear systems with actuator faults and unmodeled dynamics

Composite learning adaptive event-triggered output feedback control of linear 2 × 2 hyperbolic PDE systems

This article investigates the problem of dissipativity-based output feedback control for networked sampled-data systems under actuator failures and consecutive denial-of-service (DoS) attacks. Two distinct sampling periods are considered, each governed by constant occurrence probabilities following a Bernoulli distribution. The communication channel from the sampler to the controller is vulnerable to malicious DoS attacks, with both the maximum number of consecutive occurrences and the attacking rate taken into account. On this basis, a mathematical model is built to characterize the interval between two sequential update instants at the controller side, whose randomness is captured by the probabilistic distributions of the stochastic sampling and consecutive attacks. By developing a sampled-data output feedback controller, an equivalent discrete-time closed-loop system is constructed. Then, two alternative control synthesis conditions are suggested for solving the controller, which can ensure the stochastic stability and strict dissipativity of the system. Finally, simulations are performed on two examples to illustrate the effectiveness of the proposed method.

This paper primarily investigates a dynamic positioning (DP) output feedback control law for ships with time-varying constraint functions on their positions. Initially, an innovative consideration is given to the collective disturbance formed by unknown time-varying low-frequency disturbances and additional disturbances caused by model parameter uncertainties. On one hand, an Extended State Observer (ESO) is utilised to estimate the state of the ship and the collective disturbance. On the other hand, a time-varying obstacle Lyapunov function is employed to impose time-varying regional constraints on the ship. Additionally, the propulsion dynamics equation and auxiliary system dynamics are introduced to address the physical characteristics and limitations of the ship's propellers. A DP control system for ships with output feedback, considering time-varying regional constraints and multi-source disturbances, is obtained. Finally, simulations are conducted to verify that the proposed control law can operate within the position constraint range, and it is characterised by fast convergence, strong stability, and high engineering applicability.

This study presents a virtual disturbance feedforward controller (VDFC) to mitigate motion sickness in vehicles equipped with active suspension systems. Because feedforward control is difficult to implement in practice owing to the limited availability of measurable or estimable road-disturbance information, a half-sine virtual disturbance (HSVD) corresponding to a bump input is introduced and incorporated into the feedforward controller design. The proposed VDFC is integrated with a feedback controller developed from quarter-car and half-car models using linear quadratic static output feedback (LQ SOF) control. Furthermore, to enhance the motion-sickness-mitigation performance of the VDFC, a simulation-based optimization framework is formulated and solved using a heuristic optimization technique. Simulations with bump inputs are carried out in a vehicle dynamics simulation environment using the LQ SOF controller together with the optimized VDFCs. A sensitivity analysis is also performed for the parameters of the optimized virtual disturbance. The results indicate that, under the bump-like excitation conditions considered, the proposed method can improve ride comfort and reduce motion-sickness-related response measures.

This article investigates data-driven output feedback control of unknown piecewise affine (PWA) systems. The objective is to design controllers that exponentially stabilize PWA systems without requiring explicit subsystem models. A data-dependent representation of the PWA system is first constructed using either input-state-output (ISO) or input-output (IO) data, depending on the availability of state measurements. Based on this representation, a piecewise output feedback controller is synthesized using the multiple Lyapunov function approach. Three key challenges are addressed. First, partition information is incorporated into the controller design to reduce conservativeness. Second, multiple datasets are employed to accommodate the switching nature of PWA systems, avoiding the need for a single long persistently exciting (PE) trajectory. Third, in the IO-data case, a left coprime condition is introduced to guarantee controllability of the constructed system. The effectiveness of the proposed method is demonstrated through three examples.

In this paper, an adaptive output feedback control scheme is proposed for a class of parametric strict feedback systems with asymmetric full-state constraints and unknown output gain. Firstly, an adaptive state observer is constructed to estimate the unmeasured system states. To compensate for the effect of the unknown output gain on the tracking performance, a new error signal incorporating an adaptive compensation coefficient is introduced into the backstepping design. Then, by combining the universal transformed function with a coordinate transformation, all system states are kept within time-varying asymmetric bounds, and the feasibility issues of conventional constrained control methods are avoided. Based on Lyapunov stability analysis, all signals in the closed-loop system are proven to be globally uniformly ultimately bounded. Finally, simulation results based on motor models demonstrate the effectiveness of the proposed scheme.

The deployment of Autonomous Underwater Vehicles (AUVs) for ocean observation is one of the important methods in marine scientific research. The stability of AUV attitude control determines the efficiency and safety of underwater operations. In practical systems, the actuators carried by AUVs have saturation, and the saturation of the actuators greatly affects the performance and stability of AUV attitude control. This article designs an anti-windup [Formula: see text] robust state feedback controller and an anti-windup [Formula: see text] robust dynamic output feedback controller for the pitch attitude control of AUVs to solve the pitch attitude control problem of AUVs subject to input constraints. Firstly, building a Linear-parameter varying (LPV) model for the AUV. Then, we design two anti-windup [Formula: see text] robust controllers based on LPV model. Simulation results verify the reasonable of LPV model and the effectiveness of proposed controllers. Proposed controllers can enable the system to quickly reach the desired attitude under input constraints.

Assistive mobility technologies have become increasingly important in improving the quality of life for individuals with disabilities. However, individuals with dual sensory loss, including visual and hearing impairments, continue to face major challenges in safe navigation and environmental awareness. Traditional mobility aids such as white canes and guide dogs provide only limited support because they cannot identify dynamic obstacles or hazardous environmental conditions in real time. This research proposes “SenseStep: AI-Integrated Smart Assistive Footwear for Dual Sensory Loss Individuals,” an intelligent wearable assistive system that combines artificial intelligence, embedded sensors, and wireless communication technologies to improve mobility assistance and user safety. The proposed system integrates ultrasonic sensors, flame sensors, and water sensors to detect nearby obstacles and environmental hazards. A camera module combined with a YOLO-based deep learning model performs real-time object recognition and distance estimation. The collected sensor and vision data are processed by a microcontroller to generate structured vibration-based feedback and optional Braille output, enabling communication without relying on visual or auditory cues. The system also includes a wireless communication module for sending emergency alerts and location updates to caregivers during hazardous situations. In addition, piezoelectric energy harvesting technology is integrated to improve battery efficiency by converting walking pressure into electrical energy. Experimental testing demonstrated an overall detection accuracy of 92%, with obstacle detection accuracy of 94%, hazard detection accuracy of 90%, and object recognition accuracy of 93%. The proposed system significantly improves safety, accessibility, and independence for individuals with dual sensory impairments by providing an intelligent, reliable, and energy-efficient assistive mobility solution Keywords— Artificial Intelligence; Smart Footwear; YOLO; Assistive Technology; Embedded Systems; Object Detection