Actuator Saturation Research Articles

Adaptive Flying Assistance Controller Design to Suppress Nonlinear Pilot-Induced Oscillations

Nonlinear pilot-induced oscillations (PIOs) pose a threat to the safety of piloted aircraft in flight. This study aimed to address this safety concern. A novel method for designing an assistance controller based on incremental model predictive control is proposed to aid pilots in suppressing categories 2 and 3 PIOs. In the assistance controller design, the characteristics of the full pilot–aircraft system are considered through a predictive model that integrates the dynamics of the pilot, actuator, and aircraft. This ensures that the assistance controller coordinates with both the pilot and aircraft. Additionally, an adaptive logic for the pilot–controller authority assignment is proposed to automatically adjust the role of the assistance controller. This adaptive logic adopts a model predictive technique to determine whether the pilot’s independent control will cause actuator saturation over the receding prediction horizon. By doing so, the assistance controller is restricted to work only when the actuator saturation constraints are activated. The pilot model-based simulations and the human-in-the-loop experiments demonstrated that the proposed assistance controller can effectively assist pilots in dealing with factors causing nonlinear PIOs while maintaining a harmonious cooperative relationship with the pilot.

Anti-disturbance cooperative formation containment control for multiple autonomous underwater vehicles with collision-free and actuator saturation constraints

This article investigates the formation containment control (FCC) problem of multiple autonomous underwater vehicles system (MAUVS) in the simultaneous presence of disturbances, collision avoidance between autonomous underwater vehicles (AUVs) and actuator saturation constraints. Firstly, for the purpose of enhancing the resisting disturbance capability of the MAUVS, the extended state observers are designed to estimate the total disturbance comprised of internal uncertainties and external interferences in real time. In order to hinder the occurrence of complexity explosion during the backstepping design, tracking differentiators are adopted to provide estimates for the derivative of the virtual control signals. Furthermore, the auxiliary system is constructed for reducing the influence of the actuator saturation on the MAUVS. Potential functions are integrated into the designed FCC strategy, so that the collisions between AUVs can be effectively avoided while the MAUVS achieving formation containment. Afterwards, via resorting to Lyapunov stability theory, it is demonstrated that the formation tracking errors and containment errors are uniformly ultimately bounded. Lastly, the comparative simulation results are illustrated to validate the feasibility of the presented anti-disturbance FCC scheme with collision-free and anti-saturation.

Stability and stabilization for the hydraulic automatic gauge control system of cold rolling mill with time delay and saturation

This paper studies the stability and stabilization of hydraulic automatic gauge control (HAGC) systems with time-varying delay and actuator saturation. First, a mathematical model of HAGC systems is established considering the measurement delay and saturation factors. With the help of state transformation, the measurement delay is transformed into input delay and the generalized sector condition is adopted to deal with the saturation problem in HAGC systems. Then, a new augmented L–K functional is constructed to contain more delay information to reduce the conservativeness of the stability criteria. Furthermore, by combining the proposed L–K functional with some advanced inequalities, the stability conditions and controller are derived with the aid of linear matrix inequalities (LMIs). Finally, the effectiveness of the proposed control algorithm is verified by simulation results.

Fully distributed observer-based scaled consensus of multi-agent systems with actuator saturation and edge-based event-triggered communication

This work uses an edge-based event-triggered technique to study the adaptive scaled consensus of general linear multi-agent systems with actuator saturation and unmeasurable internal states. By proposing an edge-based event-triggered scaled algorithm, constructing an improved Lyapunov function, and introducing a low-gain feedback technique, the difficulties generated by actuator saturation and scaling property are overcome, and a triggering condition based on the triggering function is obtained, under which the scaled consensus is achieved, and the Zeno behavior is excluded. This work’s results are more flexible than existing results; in other words, triggering numbers and convergence rates are adjusted in this work. Finally, several examples are proposed to verify the results and their interesting characteristics.

Lyapunov-based model predictive control for trajectory tracking of hovercraft with actuator constraints and external disturbances

In this study, a Lyapunov-based model predictive control (LMPC) method is developed to address the trajectory tracking problem of autonomous hovercrafts subjected to unknown complex disturbances from ocean environments and practical constraints of marine surface vessels, such as actuator increment limitations and saturation. Initially, the novel LMPC algorithm is applied to enhance tracking performance through rolling optimization and online optimization. Subsequently, a nonlinear disturbance observer is designed to estimate external disturbances caused by wind and dynamics uncertainties. Furthermore, to theoretically ensure control stability, contraction constraints are established using the nonlinear backstepping control method. This approach provides the essential conditions for the system's robustness and stability. Finally, the superiority and robustness of the designed LMPC trajectory tracking method are demonstrated through numerical simulations conducted in a marine environment.

Anti-windup design for supercavitating vehicle based on sliding mode control combined with RBF network

During the longitudinal motion of a supercavitating vehicle, the stability control problem is complicated because of the nonlinear planing force on the tail part. The dynamic model of a supercavitating vehicle in longitude plane is nonlinear, simultaneously, the control instructions of a supercavitating vehicle may exceed the physical limits of an actuator. Therefore, designing a longitudinal stability control system for a supercavitating vehicle, not only the treatment of nonlinear planing force, but also the physical constraints of the actuator should be considered. For the longitudinal motion model of supercavitating vehicle, a cascade model is proposed, which decomposes the longitudinal motion of supercavitating vehicle into two subsystems. Sliding mode control based on RBF neural network compensation is adopted in the controller design process, and RBF neural network is exploited to approach the deviation caused by actuator saturation. The proposed control method can effectively compensate the performance degradation caused by control variable saturation, and has strong robustness.

Dynamic surface control for satellite attitude of the chained three-body tethered system during deployment

A fixed-time attitude stabilization scheme based on dynamic surface control (DSC) is proposed for the attitude of the three-body chained tethered system during tether deployment. Considering that the angular velocity of the satellite is difficult to measure, an extended state observer (ESO) is introduced to simultaneously compensate for the adverse effects of flexible panel vibrations and uncertain inertia tensors. On this basis, a fixed-time convergence attitude control scheme using DSC is designed, accounting for filtering error compensation. To offset the limitations of traditional fixed-time control strategies that ignore actuator saturation, an auxiliary system is introduced to incorporate control input constraints directly into controller design. The stability of the closed system is analyzed based on Lyapunov theory. Finally, the effectiveness and superiority of the proposed algorithm are verified by numerical simulation.

Anti‐windup compensation for model reference adaptive control schemes

AbstractActuator constraints, particularly saturation limits, are an intrinsic and long‐standing problem in the implementation of most control systems. Model reference adaptive control (MRAC) is no exception and it may suffer considerably when actuator saturation is encountered. With this in mind, this paper proposes an anti‐windup strategy for model reference adaptive control schemes subject to actuator saturation. A prominent feature of the proposed compensator is that it has the same architecture as well‐known nonadaptive schemes, namely model recovery anti‐windup, which rely on the assumption that the system model is known accurately. Since, in the adaptive case, the model is largely unknown, the proposed approach uses an “estimate” of the system matrices for the anti‐windup formulation and modifies the adaptation laws that update the controller gains; if the (unknown) ideal control gains are reached, the model recovery anti‐windup formulation is recovered. The main results provide conditions under which, if the ideal control signal eventually lies within the control constraints, then the system states will converge to those of the reference model, that is, the tracking error will converge to zero asymptotically. The article deals with open‐loop stable linear systems and highlights the main challenges involved in the design of anti‐windup compensators for model‐reference adaptive control systems, demonstrating its success via a flight control application.

A passivity based sliding mode controller design for microgrid considering delay and actuator saturation

A passivity based sliding mode controller design for microgrid considering delay and actuator saturation

Nonlinear adaptive pose motion control of a servicer spacecraft in approximation with an accelerated tumbling target

Removing a limited number of large debris can significantly reduce space debris risks. These bodies are generally exposed to extreme environmental disturbance torques or consecutive accidents due to their large wet area, which causes them to experience accelerated high-rate tumbling motion. The existing literature has adequately explored the approximation operations with non-cooperative targets exhibiting 3-axis tumbling motion. However, the research gap lies in the lack of attention given to addressing this approximation for targets undergoing accelerated motion. Agile, accurate, and large-angle maneuvers are three common necessities for safely capturing such targets. Changes in the moment of inertia brought on by fuel slushing cannot be disregarded during such a maneuver. To deal with nonlinearities, adverse coupling effects, actuator saturation constraints, time-varying moment of inertia, and external disturbances that worsen during accelerated agile large-angle maneuvers, a novel adaptive control approach is developed in this paper. The controller's main advantage is its adjustable desired acceleration, which maintains its performance even when dealing with accelerated motion. The control law is directly synthesized from the nonlinear relative equations of motion, without any linearization or simplification of the system dynamics, making it robust to a variety of orbital elements and target behaviors. Adaptation laws are extracted from the Lyapunov stability theorem in a way that guarantees asymptotic stability. Moreover, control actuator roles (delay, saturation, and allocation) are accounted for in modeling and simulation. Finally, a comprehensive numerical simulation based on three different realistic and strict scenarios is carried out to demonstrate the effectiveness and performance of the proposed control approach. The controller's robustness against time-varying dynamic parameters (sharp and sudden change, smooth and slow change, and periodic change) is extensively demonstrated through simulation.

Connectivity preservation control for multiple unmanned aerial vehicles in the presence of bounded actuation

This paper proposes a novel multi-unmanned aerial vehicle (UAV) connectivity preservation controller, suitable for scenarios with bounded actuation and limited communication range. According to the hierarchical control strategy, controllers are designed separately for the position and attitude subsystems. A distributed position controller is developed, integrating an indirect coupling control mechanism. The innovative mechanism associates each UAV with a virtual proxy, facilitating connections among adjacent UAVs through these proxies. This structuring assists in managing the actuator saturation constraints effectively. The artificial potential function is utilized to preserve network connectivity and fulfill coordination among all virtual proxies. Additionally, an attitude controller designed for finite-time convergence guarantees that the attitude subsystem adheres precisely to the attitude specified by the distributed position controller. Simulation results validate the efficacy of this distributed formation controller with connectivity preservation under bounded actuation conditions. The simulation results confirm the effectiveness of the distributed connectivity preservation controller with bounded actuation.

Non‐fragile reliable guaranteed cost control and L2‐gain analysis for saturated switched nonlinear systems

AbstractFor the nonlinear continuous‐time saturated switched system, this article investigates the problem of non‐fragile reliable guaranteed cost control of the system. Due to the simultaneous occurrence of controller perturbation, actuator saturation and actuator failure, when designing the cost function, this article considers three scenarios affecting the system performance, controller perturbation, actuator saturation and actuator failure, simultaneously in the cost function, which has not been investigated in the existing literature. For nonlinear switched systems, we derive some sufficient conditions to satisfy simultaneously the stabilization of non‐fragile reliable guaranteed cost control and the performance index. In order to ensure the exponential stabilization of the nonlinear switched systems and minimize the upper bound of cost function, a switching law and the non‐fragile reliable guaranteed cost state feedback controllers are designed by using the minimum dwell‐time method. Furthermore, by solving optimization problems with linear matrix inequality constraints, the minimum upper bound of the cost function and the performance of the system are determined. At the end of the article, we give a numerical example to verify the effectiveness of the proposed approach.

Rules-reduced fuzzy neural network-based learning control for multiple constraints robots using online identification and compensation methods

This paper presents a novel fuzzy-learning adaptive control approach for uncertain robotic systems with input dead zone and output saturation constraints. To address the input dead zone and output saturation and improve the control stability of the robot, an internal model compensation method was proposed, which utilizes online identification of an arctanh function to approximate the output saturation of actuators. The paper also introduces an adaptive learning control system based on a rules-reduced fuzzy neural network (RRFNN) algorithm, which considers the dead zone and output saturation function to enhance control performance, and an adaptive law driven by approximation mistakes is employed to handle multiple constraints. Furthermore, the controller utilizes the integral Lyapunov stability theorem and RRFNN to design the fuzzy-learning adaptive control law, ensuring global convergence and stability of the control system. Extensive simulations and experiments on a robotic manipulator are conducted to verify the feasibility of the proposed control method. Without the proposed method, the robot’s joint tracking error exceeds 0.5 rad, while with the proposed controller, it is less than 0.05 rad and does not violate output saturation constraints. Thus, the proposed method can realize the desired performance and overcome multiple constraints.

Robust adaptive predictive digital controller with integral action for set-point regulation of saturated uncertain systems

A robust adaptive digital controller is systematically investigated on set-point regulation in uncertain systems under input saturation. So, an optimization strategy will be derived to solve the reference regulation robustly in the discrete-time systems. To this purpose, a discrete-time integral controller is considered through a fixed state-feedback structure. Then, its coefficients would be numerically quantified at each sample time by means of a minimization. The formulation and merit of the addressed predictive control method are theoretically proved in the paper. Then the control technique is simulated in a discrete-time equation. Additionally, some numerical results are executed to declare the remarkability and vigor of the recommended algorithm against uncertainties, actuator saturation, and disturbance in contrast to the existing control ways.

Adaptive fuzzy neural network-based finite time prescribed performance control for uncertain robotic systems with actuator saturation

Adaptive fuzzy neural network-based finite time prescribed performance control for uncertain robotic systems with actuator saturation

A further step to transient performance enhancement of discrete-time singular systems with time-varying delay

This paper is the first attempt to enhance the transient performance while simultaneously addressing the output regulation challenges for discrete singular time-delay systems subject to actuator saturation. The proposed controller consists of two linear and nonlinear parts, without any switching elements. The linear part is designed to solve the main regulation problem, while the nonlinear part is designed to enhance the performance of the closed-loop system in the face of input saturation. The delay-range dependent stability conditions of the closed-loop system are analysed using the Lyapunov-Krasovskii (LK) approach, which involves solving an LMI problem to determine the parameters of the proposed controller. Furthermore, the admissibility of the closed-loop system is assessed to ensure the system’s stability, regularity, and causality. Finally, the theoretical achievements are endorsed by simulations through numerical and practical examples.

Distributed Global Consensus of LTI Mass with Heterogeneous Actuator Saturation and Communication Noises

Distributed Global Consensus of LTI Mass with Heterogeneous Actuator Saturation and Communication Noises

Trajectory Planning and Tracking Using Decoupled CBF-QPs for Safe Navigation of Quadrotors

Abstract Control barrier function-based quadratic program (CBF-QP) provides an avenue for agile and numerically efficient obstacle avoidance algorithms. However, the CBF-QP methods may lead to lengthy detours and undesirable avoidance maneuvers. This paper proposes a bi-level CBF-QP for the safe navigation of quadrotors. A Planning-QP is proposed to create a safe reference trajectory that shadows the actual reference trajectory with prescribed avoidance acceleration, velocity, distance, and direction during the avoidance maneuver. A control Lyapunov function (CLF) ensures that modified reference closely matches the actual reference outside the avoidance regions and multiple control barrier functions (CBFs) ensure the safety and smoothness of the avoidance trajectory. Model uncertainties can undermine the safety of the quadrotor while tracking the modified reference. Hence, a Tracking-QP is designed to achieve accurate tracking with ensured safe maneuvering around obstacles, where a new CBF is constructed to prevent actuator saturation. Prescribed attitude bounds are ensured via additional acceleration constraints in the Tracking-QP. The proposed method is validated using numerous experiments involving various static obstacles where the quadrotor carries different payloads.

Robust security‐based model predictive control for nonlinear cyber‐physical system under random occurring deception attacks and actuator saturation

AbstractThis article presents a robust security‐based model predictive control (MPC) framework for a class of discrete‐time nonlinear cyber‐physical systems (CPSs) suffering random deception attacks and persistent actuator saturation. In CPSs, the attacks can significantly degrade the control performance. Naturally, a larger degree of control is required, prompting the consideration of actuator saturation in CPS's structure. In our work, the cyberattacks on the controller‐actuator (C‐A) channel are described as a stochastic process with specific probability and bounded energy. The security‐related constraint is introduced to improve the resistance and robustness against attacks. Mean‐square quadratic boundedness is derived to estimate the robustly invariant set. By placing the saturated input into a convex hull, a robust security‐based MPC optimization problem is addressed less conservatively with linear matrix inequality technique. Furthermore, the feasibility of the proposed method is proved recursively, and the stability and security of the closed‐loop system are established in mean‐square sense. In the end, the laboratory tank and reactor‐separator process are applied to validate the effectiveness of the proposed algorithm.

Leader-Following Consensus for Incremental Quadratic Constrained Nonlinear Multiagent Systems With Actuator Saturation

Leader-Following Consensus for Incremental Quadratic Constrained Nonlinear Multiagent Systems With Actuator Saturation