This article analyzes the effects of communication delays on the stability of sampled data systems. In order to derive less conservative stability conditions for the system under consideration, certain zero equalities are typically introduced. These zero equalities are crucial for minimising conservativeness by establishing the link between communication delay and sampling time. However, the application of these zero equations can become problematic when the time delay exceeds the sampling period. To resolve this issue, the paper utilises a delay-decomposing approach, which validates the introduction of the zero equations. Leveraging this segmentation method, a new Lyapunov-Krasovskii functional has been developed, and these zero equations are used to establish an improved stability criterion. Numerical examples demonstrate the effectiveness and benefits of the proposed criterion.
This paper addresses the robust H infinity tracking control for a quadrotor unmanned aerial vehicle (UAV) subjected to external disturbance with unknown dynamics. It is worth emphasising that solving the optimal H infinity tracking control problem requires addressing a nonlinear Hamilton-Jacobi-Isaacs (HJI) equation, which is well known to be difficult to solve in practical applications. Furthermore, in this work, the system dynamics are assumed to be unknown; therefore, obtaining an optimal solution to the HJI equation using mathematical analysis methods is infeasible. To overcome this challenge, a learning algorithm based on Integral Reinforcement Learning (IRL) is developed for both translational and rotational controllers to achieve optimal control policies by using only the measured input-output quadrotor data. Numerical simulation results are presented to demonstrate the superior tracking performance and disturbance robustness of the proposed method.
For autonomous vehicles traveling in intersections, it is essential to plan trajectories that ensure safety and improve road efficiency. This paper proposes a trajectory planning and motion control scheme for intersections based on model predictive control and control barrier function (MPC-CBF). An expanded elliptical model is constructed to define a danger zone for CBF constraints, enhancing safety and reducing stop-and-go delays. To balance safety and path planning feasibility, the decay coefficient of CBF is treated as an optimisable variable in the MPC optimisation problem. The real-time adjusted decay coefficient dynamically changes the safety constraint range, preventing over-avoidance. Additionally, Bayesian optimisation trains the weights of the multi-objective problem in typical intersection conflict scenarios. The weights are selected based on the relative position between the autonomous and obstacle vehicles. Finally, the control scheme is evaluated using Carsim and Matlab/Simulink co-simulation.
This work focuses on control of a rigid satellite in the presence of turbulence and uncertainties. It applies actuators with first-order wheel dynamics. The fractional- and integer-order controllers have been tested under identical conditions to reach a proper comparison of the results. The satellite moment of inertia, actuator perfect, and the external disturbances are all considered as uncertain. Numerical analysis is performed using Euler's method, while FO integrals and derivatives are obtained by the Gr & uuml;nwald-Letnikov definition. The PSO method is employed to optimise the coefficients of both controllers during the process. The goal is to minimise the absolute average of the position error in the maneuvers. Performance criteria are analyzed and evaluated, including overshoot and settling times, while uncertainty is present. The findings demonstrate that the controllers with additional degrees of freedom, therefore, provide a much better pointing accuracy (especially in the case of uncertainty) than the IO ones.
Robotic exoskeletons hold significant potential for clinical rehabilitation as well as human performance augmentation. Nevertheless, identifying robust and adaptable control approaches to enable smooth human-machine interaction through dynamic environments with varying task requirements remains a primary technical challenge. This paper presents the design and implementation of interval type-2 fuzzy logic modified PID (PI-D) controller (IT2-FLPI-DC) for multi-joint lower limb exoskeleton (LLE). The purpose of LLE is to assist, augment or restore movement, and function in the human legs for those who suffers spinal cord injury, stroke, etc. For performance analysis, the proposed controller is compared with interval type-2 fuzzy logic PID controller (IT2-FLPIDC). Simulation results show that the proposed controller achieves faster settling time and less overshoot when compared to IT2-FLPID controller.
This paper investigates the problem of high-precision disturbance rejection tracking control for servo motion systems (SMS) with external disturbances and system uncertainties, where a control strategy combining the Fully Actuated System Approach (FASA), Generalised Extended State Observer (GESO), and Linear Quadratic Regulator (LQR) is proposed. First, a FASA model of the SMS is established with parameter matrices designed parametrically. Next, a GESO is developed to estimate system states and disturbances simultaneously. The LQR algorithm is then used to obtain disturbance compensation gains, enabling an FASA-based controller with estimation and compensation capabilities. Subsequently, Lyapunov stability theory is applied to verify closed-loop stability. Finally, simulation and experimental results show that the proposed method achieves higher precision than PD and uncompensated strategies under disturbances and uncertainties, can overcome limitations of traditional approaches, and improves control accuracy, stability, and reliability.
Zeroing neural network (ZNN) is an effective alternative to gradient neural networks for time-varying problems. However, when applied to complex-domain time-varying matrix inversion, existing ZNNs still employ fixed convergence factors and ignore noises, leading to slow response and potential divergence. To fill this gap, we propose an adaptive noise-tolerant ZNN (ANT-ZNN). Specifically, a noise-tolerant integral ZNN is embedded to reject disturbances, while a fuzzy controller dynamically adjusts an adaptive convergence factor, increasing proportionally with the instantaneous error magnitude and gradually diminishing upon convergence. These designs are synthesised into the ANT-ZNN model that guarantees global stability, finite-time convergence, and robustness against additive noises, as rigorously proved via Lyapunov theory. Comparative simulations further verify that the ANT-ZNN outperforms fixed-parameter models in both noise-free and noisy environments.
