This article presents a disturbance observer-based adaptive event-triggered underactuated control scheme for singularly perturbed coupled systems subject to space-time-varying disturbance. Initially, the considered nonlinear partial differential equation-ordinary differential equation (PDE-ODE) systems are linearised by Takagi–Sugeno fuzzy rules and transformed into an integrated augmented system to facilitate system's analysis. Then, a singular perturbation theory is considered to analyse the multi-time scales phenomenon that is often neglected in existing work. Subsequently, a space-time-varying disturbance is considered to model the external system disturbance, and a disturbance observer and a composite observer are designed to estimate the unknown disturbance and the system's state. In addition, a variable-threshold adaptive event-triggered mechanism is constructed to flexibly reduce the network communication burden, and an underactuated control strategy is proposed to save the system control cost. Finally, simulations for a hypersonic rocket car demonstrate that the proposed controller and the disturbance observer are effective.

This paper presents a robust constrained control strategy aimed at enhancing the trajectory tracking performance of wheel hub motors, based on their dynamic model. We introduce an innovative state transformation method that utilises the monotonic and unbounded properties of the tangent function to convert constrained output variables of the motors into unconstrained ones. This transformation ensures that the motor's angular displacement output consistently remains within specified limits. By reconfiguring the control variables, we derive a new dynamic model, leading to the proposal of a model-based and error-based control method. The theoretical analysis, using the Lyapunov method, demonstrates that the controller guarantees both uniform boundedness and uniform ultimate boundedness of the system. The effectiveness of the robust controller is validated through simulations and experiments, showing that this strategy effectively mitigates uncertainties and significantly enhances the servo capability and reliability of wheel hub motors.

This article deals with the output regulation problem with output constraint for uncertain nonlinear systems with unknown control directions. An output feedback backstepping control and an internal model integrating root-type barrier Lyapunov function are designed. The root-type barrier Lyapunov function is used for the first time in designing an adaptive controller to deal with the output constraint of the output regulation problem, which ensures that the system output cannot violate the given boundary. Compared with other barrier Lyapunov functions, the key advantage lies in its polynomial-form derivatives, which avoid singularity issues near constraint boundaries. Nussbaum functions are utilised to address the problem of unknown directions. It is proven that under the adaptive controller via a novel algorithm, all signals of the closed-loop system are ultimately uniformly bounded(UUB), and the tracking error remains strictly within pre-given constraint intervals. Finally, the effectiveness of the proposed method is shown by two simulation examples.

This paper mainly deals with the distributed group bipartite consensus control problem for heterogeneous multi-agent systems(MASs) in signed graphs. The systems under consideration consist of first- and second-order agents, and can be divided into multiple groups in which agents can either cooperate or compete. Particularly, competition can exist not only between different groups, but also within the same group. Due to actuator saturation, a control protocol employing hyperbolic tangent function is developed by utilising the nearest neighbour role. Using the graph theory and the Lyapunov stability theory, it is shown that under the structurally balanced and unbalanced topologies, the group bipartite consensus and the group consensus can be reached respectively. Finally, the performance of the proposed protocol is illustrated by a simulation example.

This paper investigates distributed leaderless formation maneuver control over a directed sensing topology. Leaderless formation maneuvers have no designated leaders, unlike traditional leader-follower approaches. The leaderless approaches proposed recently in the literature considered undirected sensing graphs. When an agent's sensing capabilities are limited, directed sensing topology becomes crucial. First, we propose control laws for leaderless formation maneuvers in 2-D by manipulating the weights of the complex-laplacian for single integrators with directed sensing graphs, where translational, rotational, and scale formation maneuvers were possible, and GAS (Global Asymptotic Stability) is established for it. Then, we propose control laws for leaderless affine formation maneuvering for single integrators with directed sensing graphs for 2-D and 3-D. Also, an extension for the higher-order integrators is provided using a back-stepping-based design and using a command filter to take account of computational and noise amplification issues. Finally, simulations are provided to validate the results.

This paper proposes a sliding mode controller and a new reaching law for automated guided vehicle (AGV) trajectory tracking. Firstly, the kinematic model of the AGV is established under a global coordinate system. Subsequently, a sliding mode switching function is designed by integrating the kinematic model with backstepping theory. Secondly, to enable the AGV system to reach the sliding mode switching surface more rapidly, variable coefficients are incorporated into the double-power reaching law. The conventional sign function is replaced with a tanh function to effectively suppress chattering. A novel double power-exponential reaching law is developed by combining the double power reaching law with an exponential reaching law. Furthermore, sufficient conditions for ensuring the stability of the AGV system are derived. Finally, the effectiveness of the proposed control law is verified through some simulations.

We present a data-driven robust reference governor (DRRG) framework that handles output constraints in nonlinear systems with unknown dynamics and uncertainties. By incorporating the Koopman operator, the DRRG first maps nonlinear system dynamics into a higher-dimensional linear space, allowing for more effective data-driven prediction. Subsequently, we develop a data-driven maximal admissible set (MAS) and a min-max optimisation problem using the high-dimensional data-driven predictors to compute optimal reference signals that are resilient to all possible realizations of uncertainties. The proposed DRRG method provides a practical solution to improving robustness against uncertainties while retaining the existing control architectures. Numerical simulations are presented to demonstrate the effectiveness of the approach.

This article investigates the problem of resilient control for Cyber-Physical-Systems (CPSs) equipped with multiple sensors, where both sides of the control structure are subjected to Denial-of-Service (DoS). To address both sides of the communication channels under DoS to multi-sensors framework, the DoS are divideded into 2-types: Multi-Channel DoS (MCDoS) and Full-Scale DoS (FSDoS). While previous work focussed on characterising MCDoS, this study emphasises the characterisation of FSDoS. First, a switched observer technique is proposed to address the MCDoS condition. Then, a Switched-Event-Triggered-Mechanism (SETM) is designed to handle FSDoS. Finally, the frequency and duration of FSDoS are analysed to ensure the Input-to-State-Stability (ISS) of the closed-loop system. Switched observer and SETM parameters are designed using Linear-Matrix-Inequalities (LMIs) based multi-objective-optimisation to ensure resilience against the maximum MCDoS frequency, FSDoS frequency, and duration. Finally, an asynchronous triggering policy is developed to address asynchronous FSDoS on both the measurement and controller sides.

This paper investigates the dynamic modelling and vibration control problems of a PDE-based moving vehicle-mounted boom crane system with input magnitude and rate constraints (IMRCs). The system consists of a moving vehicle, a rigid boom, a flexible string, and a payload. This configuration addresses the critical need for mobility in long-distance industrial operations that stationary boom cranes cannot fulfill. First, to preserve the infinite-dimensional modes of the system, the dynamic model is established as a set of partial differential equations (PDEs) via Hamilton's principle. Second, based on the backstepping method with smooth hyperbolic tangent function, a boundary controller is designed to suppress the vibration of the flexible string and regulate the positions of the vehicle and boom, while strictly constraining both input magnitudes and rates within the set bounds. The asymptotic stability of the closed-loop system is proved by Lyapunov's direct method and LaSalle's invariance principle. Finally, the effectiveness of the suggested theoretical framework is verified via numerical simulations.

In this work, we present an Actor-Critic like neural network in a Receding Horizon Control framework to solve a trajectory optimal control problem while accounting for numerical uncertainties that are inherent in discretisation and derivative approximations. We derive the asymptotic stability conditions considering bounded numerical uncertainties for a class of 1st and 2nd order systems with invertible dynamics. The Actor network generates nominal control inputs by optimising over a dimension-reduced motion manifold, while the Critic network evaluates and mitigates the effect of numerical uncertainties on the system propagated dynamics. The Critic is trained to ensure that the nominal control policy drives the system along a desired trajectory with a specified level of accuracy. The proposed methods are validated in two simulation examples: a 1st order skid-steered mobile robot and a 2nd order quadrotor moving in a vertical plane.