Actuator Delay Research Articles

A novel robust adaptive control for nonlinear uncertain quarter-vehicle suspension system in presence of unknown time delay actuation

This paper presents a novel robust adaptive control approach for nonlinear uncertain vehicle suspension system with time delayed actuation and bounded disturbances. The uncertainty and disturbance as well as the input delay on the system are all limited and unknown. This paper explores a control-oriented nonlinear model to accurately describe the dynamics of the vehicle suspension which incorporates uncertainty, disturbance and actuator delay. The controller is designed based on robust and adaptive approaches, which along with guaranteeing general goals for the suspension system is able to assure the stability of the closed-loop system in the Lyapunov concept. Also, due to the use of smooth functions in the robust controller structure, sudden changes in the behavior of system states are prevented. The simulation and comparison results in MATLAB environment show the efficiency of the proposed robust adaptive method in covering the effects of uncertainty, disturbance and time-variant actuator delay.

Fixed-Time H∞ Consensus Control of Uncertain Vehicular Platooning Systems With Homogeneous Time-Varying Actuator Delay

Fixed-Time H_∞ Consensus Control of Uncertain Vehicular Platooning Systems With Homogeneous Time-Varying Actuator Delay

Hyper-Exponential Stabilization of Neural Networks by Event-Triggered Impulsive Control With Actuation Delay.

This brief studies the hyper-exponential stabilization of neural networks (NNs) by event-triggered impulsive control, where the impulse instants are determined by the event-triggered conditions. In the presence of actuation delay, an event-triggered impulsive control scheme is devised. For reducing the sampling task of continuous detection, a periodic-detection scheme is also introduced. Within these frameworks, the occurrence of Zeno behavior is rigorously precluded, and some criteria are formulated to achieve the stabilization of the system with a hyper-exponential convergence rate. Moreover, a numerical simulation is provided to elucidate the validity of the theoretical findings.

Robust cooperative control strategy for a platoon of connected and autonomous vehicles against sensor errors and control errors simultaneously in a real-world driving environment

In a real-world driving environment, a platoon of connected and autonomous vehicles (CAVs) is subject to many internal and external disturbances, resulting in uncertain vehicle dynamics. In general, the disturbances can be categorized into two types: disturbances due to vehicle sensor errors (e.g., GPS error) and disturbances due to vehicle control errors (e.g., actuator delay). In the literature, many control strategies have been proposed to improve the robustness of the CAV platoon against uncertain vehicle dynamics induced by these disturbances. However, most of these strategies only consider one type of disturbance and cannot tackle both types of disturbances simultaneously. Furthermore, they are designed to maximize the benefits of each vehicle in the platoon independently, which can deteriorate the performance of the platoon. To address these problems, this study proposes a robust cooperative control (RCC) strategy to maneuver the vehicles in the platoon cooperatively to counteract the impacts of both types of disturbances. The RCC strategy is developed based on a minimax problem, where the maximization subproblem seeks to find the worst inputs for the uncertainty terms in the vehicle dynamics equation to minimize the platoon performance, while the minimization subproblem seeks to find the optimal control decisions for all subsequent vehicles to maximize the platoon performance in the worst case. To solve the minimax problem, this study proposes a globally convergent solution algorithm. It can solve the minimax problem very efficiently to enable real time deployment of the RCC strategy. Numerical application indicates that compared to the existing methods, the RCC strategy can dramatically improve the robustness of the CAV platoon against the uncertain vehicle dynamics induced by both vehicle state detection errors and vehicle control errors. Therefore, it can maneuver the CAV platoon safely and efficiently in a real-world driving environment.

Non-square internal model control for mode transition of hybrid electric vehicles with multiple time delays

Mode transitions of multi-mode hybrid electric vehicles need careful coordination among the engine torque, motor torque, and clutch transmitted torque. However, the three torques have different actuation delays which make it difficult to ensure mode transition performance. A non-square internal model controller (IMC) is proposed in this paper for mode transition during which the input number is greater than the output number and each input has a different actuation delay. At first, the Moore-Penrose generalized inverse matrix is introduced to solve the inverse of the non-square matrix and make the IMC applicable. Secondly, the all-pole method is adopted to approximate the three delay models. Based on these specialized techniques, the tracking controller and anti-disturbance controller of the IMC are derived. Experiment results verify the effectiveness of the proposed controller.

Delay-adaptive compensation for 3-D formation control of leader-actuated multi-agent systems

This paper focuses on the control of collective dynamics in large-scale multi-agent systems (MAS) operating in a 3-D space, with a specific emphasis on compensating for the influence of an unknown delay affecting the actuated leaders. The communication graph of the agents is defined on a mesh-grid 2-D cylindrical surface. We model the agents’ collective dynamics by a complex- and a real-valued reaction–advection–diffusion 2-D partial differential equations (PDEs) whose states represent the 3-D position coordinates of the agents. The leader agents on the boundary suffer unknown actuator delay due to the cumulative computation and information transmission time. We design a delay-adaptive controller for the 2-D PDE by using PDE backstepping combined with a Lyapunov functional method, where the latter is employed to design an update law that generates real-time estimates of the unknown delay. Capitalizing on our recent result on the control of 1-D parabolic PDEs with unknown input delay, we use Fourier series expansion to bridge the control of 1-D PDEs to that of 2-D PDEs. To design the update law for the 2-D system, a new target system is defined to establish the closed-loop local boundedness of the system trajectories in H2 norm and the regulation of the states to zero assuming a measurement of the spatially distributed plant’s state. We illustrate the performance of the delay-adaptive controller by numerical simulations.

Deep reinforcement learning based active surge control for aeroengine compressors

This study proposes an active surge control method based on deep reinforcement learning to ensure the stability of compressors when adhering to the pressure rise command across the wide operating range of an aeroengine. Initially, the study establishes the compressor dynamic model with uncertainties, disturbances, and Close-Coupled Valve (CCV) actuator delay. Building upon this foundation, a Partially Observable Markov Decision Process (POMDP) is defined to facilitate active surge control. To address the issue of unobservability, a nonlinear state observer is designed using a finite-time high-order sliding mode. Furthermore, an Improved Soft Actor-Critic (ISAC) algorithm is developed, incorporating prioritized experience replay and adaptive temperature parameter techniques, to strike a balance between exploration and convergence during training. In addition, reasonable observation variables, error-segmented reward functions, and random initialization of model parameters are employed to enhance the robustness and generalization capability. Finally, to assess the effectiveness of the proposed method, numerical simulations are conducted, and it is compared with the fuzzy adaptive backstepping method and Second-Order Sliding Mode Control (SOSMC) method. The simulation results demonstrate that the deep reinforcement learning based controller outperforms other methods in both tracking accuracy and robustness. Consequently, the proposed active surge controller can effectively ensure stable operation of compressors in the high-pressure-ratio and high-efficiency region.

Security synchronization problem for stochastic complex networks via event-triggered impulsive control with actuation delays

This study focused on the security synchronization problem for stochastic complex networks (SCNs) via event-triggered impulsive control (ETIC) with actuation delays. Firstly, incorporating the network topology and the Lyapunov function theory, a novel event-triggered mechanism (ETM) is devised, which accounts for actuation delays; Secondly, an ETM-based quantizer is introduced to optimize network resources, meantime, building upon this foundation, the Lipschitz–Razumikhin method is integrated with graph theory to establish a positive lower bound on the minimum event interval time, thereby preventing Zeno behavior; Additionally, some sufficient conditions are obtained to achieve global asymptotic synchronization of SCNs under quantization and deception attacks; Finally, a Chua’s circuit numerical simulation is employed to demonstrate the effectiveness of our theoretical findings.

Data-Efficient Deep Reinforcement Learning for Attitude Control of Fixed-Wing UAVs: Field Experiments.

Attitude control of fixed-wing unmanned aerial vehicles (UAVs) is a difficult control problem in part due to uncertain nonlinear dynamics, actuator constraints, and coupled longitudinal and lateral motions. Current state-of-the-art autopilots are based on linear control and are thus limited in their effectiveness and performance. Gls drl is a machine learning method to automatically discover optimal control laws through interaction with the controlled system that can handle complex nonlinear dynamics. We show in this article that deep reinforcement learning (DRL) can successfully learn to perform attitude control of a fixed-wing UAV operating directly on the original nonlinear dynamics, requiring as little as 3 min of flight data. We initially train our model in a simulation environment and then deploy the learned controller on the UAV in flight tests, demonstrating comparable performance to the state-of-the-art ArduPlane proportional-integral-derivative (PID) attitude controller with no further online learning required. Learning with significant actuation delay and diversified simulated dynamics were found to be crucial for successful transfer to control of the real UAV. In addition to a qualitative comparison with the ArduPlane autopilot, we present a quantitative assessment based on linear analysis to better understand the learning controller's behavior.

A Two-Stage Cooperative Adaptive Cruise Control for Connected Automated Vehicles in Multislope Roads considering Communication Delay and Actuator Delay

A Two-Stage Cooperative Adaptive Cruise Control for Connected Automated Vehicles in Multislope Roads considering Communication Delay and Actuator Delay

Nonlinear predictor‐feedback cooperative adaptive cruise control of vehicles with nonlinear dynamics and input delay

SummaryWe construct a nonlinear predictor‐feedback cooperative adaptive cruise control (CACC) design for homogeneous vehicular platoons subject to actuators delays, which achieves: (i) positivity of vehicles' speed and spacing states, (ii) string stability of the platoon, (iii) stability of each individual vehicular system, and (iv) regulation to the desired reference speed (dictated by the leading vehicle) and spacing. The design relies on a nominal, nonlinear adaptive cruise control (ACC) law that we construct, which guarantees (i)–(iv) in the absence of actuator delay, and nonlinear predictor feedback. We consider a classical (for ACC/CACC design) third‐order, nonlinear model subject to input delay, for the vehicles' dynamics. The proofs of the theoretical guarantees (i)–(iv) rely on derivation of explicit estimates on solutions (both during open‐loop and closed‐loop operation), capitalizing on the ability of predictor feedback to guarantee complete delay compensation after the dead‐time interval has elapsed, and derivation of explicit conditions on initial conditions and parameters of the nominal control law. We also present consistent simulation results, considering a platoon of ten vehicles, which validate the design developed.

Adaptive backstepping control for a class of uncertain systems with actuator delay and faults

Unknown actuator failures are inevitable in practical systems. At the same time, time delay exists in many physical actuators, and the system performance will be affected by such actuator delay and faults. However, the results of studies that attempt to compensate for unknown failures of actuators with time delay are still very limited. In this paper, such a problem is studied, and an adaptive control scheme is proposed based on backstepping approaches. First, the input delay of actuator faults and output disturbances are transformed into unknown effects on the output signal. In the backstepping recursive design, these unknown effects will accumulate to the last step of the controller design. Then, a new Lyapunov function is constructed by introducing auxiliary signals to prove the stability of the system. It is shown that the proposed control scheme can compensate for the effects caused by unknown actuator failures and input delays. The stability of the closed-loop system can be guaranteed by this adaptive controller. Finally, simulation studies are used to verify the effectiveness of the proposed scheme.

Asynchronous sampled-data control for connected vehicles with heterogeneous delays

This study investigates an asynchronous sampled-data control problem of vehicular platoons, with heterogeneous sampling, subjected to actuator delays. Without a synchronized clock, a completely asynchronous sampled-data controller is designed for each follower, where the state of the ith follower itself and its neighboring vehicles are sampled at their own sampling time instants. The caused closed-loop tracking error dynamics for the entire platoon considering the effect of the nonuniform sampling time intervals, heterogeneous vehicle dynamics, inter-vehicle topology and heterogeneous time delays. To simplify the stability analysis and controller design, the tracking-error dynamics of the entire platoon are decomposed into individual subsystems with reduced-order dynamics. Based on Lyapunov stability theory, the optimal conditions are explored to design an asynchronous sampled-data controller to guarantee the desired stability performance. Moreover, the exact values for the maximum allowable sampling interval and time delay are calculated for each follower using the designed feedback controller gain. The proposed asynchronous sampled-data control method is extended to a vehicular platoon using an event-based sampling scheme. Numerical examples are used to verify the effectiveness of the proposed sampled-data control method.

Safe Stabilizing Control of Traffic Systems With Simultaneous State and Actuator Delays

Safe Stabilizing Control of Traffic Systems With Simultaneous State and Actuator Delays

Input‐to‐state stability of a time‐invariant system with control delay and additive disturbances

AbstractWe consider a class of linear time invariant systems with control delay and additive disturbances. A state predictive feedback method is first applied to compensate the actuator delay. In this way, a closed loop system free of delay is achieved. It allows to ensure input‐to‐state‐stability of the closed loop system. Applications are given for the lateral‐directional stability of an airplane with two controls, on the aileron and on the rudder, in correlation with compliance with some regulatory flight conditions.

Model Predictive Direct Self-Control for Six-Step Operation of Permanent-Magnet Synchronous Machines

The optimization of voltage utilization can boost electric drives' power and torque capabilities, which is of particular interest in transportation applications. However, this is a weak point of most control approaches, like linear field-oriented control (FOC) methods using standard modulation schemes. This paper introduces the model predictive direct self-control (MPDSC) strategy, a modification of direct self-control (DSC) for its application in a digital implementation to achieve maximum voltage utilization (i.e., six-step operation) for permanent magnet synchronous machines. This solution is suitable for highly utilized machines with heavy magnetic (cross-)saturation and low sampling to fundamental frequency ratio. The modifications include using load angle regulation to control the selected operating point and model prediction to compensate for the actuation delay. The proposed system can achieve six-step operation with accurate torque control and robustness against disturbances and parameter estimation inaccuracies. Comprehensive simulation and experimental results demonstrate the performance of the proposed MPDSC while operating at the voltage constraint. In particular, a transient rise time of 2.3 ms to maximum torque, current reduction for equal torque-speed operating point of up to 18 %, and maximum torque increase for equal current amplitude of up to 15 % compared to conventional FOC have been empirically observed.

Event-triggered impulsive control for multi-agent systems with actuation delay and continuous/periodic sampling

The paper explores the event-triggered impulsive control for consensus problems in multi-agent systems (MASs) with actuation delay. Two types of event-triggered delayed impulsive control (ETDIC) schemes, predicated on continuous and periodic sampling, are proposed respectively. Several exponential consensus criteria for MASs are derived by using Lyapunov method. A correlation function is established between event parameters, actuation delay, and control strength to promote the co-design of the event-triggering policy and impulsive controller. It is shown that under the proposed ETDIC schemes, the interaction between neighboring agents is executed discontinuously, the upper bound information on the interval of two consecutive event impulses is not directly required. This results in reduced resource consumption of communication and control actions, as well as enhancing flexibility of the generated sequences of event impulses. Moreover, two representative examples with simulation results are given to illustrate the theoretical results.

Consistency vs. Availability in Distributed Cyber-Physical Systems

In distributed applications, Brewer’s CAP theorem tells us that when networks become partitioned (P), one must give up either consistency (C) or availability (A). Consistency is agreement on the values of shared variables; availability is the ability to respond to reads and writes accessing those shared variables. Availability is a real-time property whereas consistency is a logical property. We extend consistency and availability to refer to cyber-physical properties such as the state of the physical system and delays in actuation. We have further extended the CAP theorem to relate quantitative measures of these two properties to quantitative measures of communication and computation latency (L), obtaining a relation called the CAL theorem that is linear in a max-plus algebra. This paper shows how to use the CAL theorem in various ways to help design cyber-physical systems. We develop a methodology for systematically trading off availability and consistency in application-specific ways and to guide the system designer when putting functionality in end devices, in edge computers, or in the cloud. We build on the Lingua Franca coordination language to provide system designers with concrete analysis and design tools to make the required tradeoffs in deployable embedded software.

Active Suspension Robust Preview Control by Considering Actuator Delay

Active suspension systems (ASSs) are crucial for realizing the improvement demand of ride comfort in autonomous vehicles (AVs). This paper presents a novel integrated robust preview control (PC) algorithm to further improve the ride comfort by effectively handling actuator delay. An augmented ASS model is first established to consider actuator delay. Then, to obtain the time-domain feedforward disturbance, updating the local spatial road dataset from sensing systems by low-pass filtering and introducing speed for conversion. A predictive control framework that utilizes preview road disturbances is developed to achieve better ride comfort. And assuming parameterized disturbances, it is transformed into a consistent quadratic programming (QP) problem for real-time application in ASS. Furthermore, an innovative design of combined observer provides accurate and robust observations under multiple uncertainties, with which the algorithm can effectively compensate for actuator delay under different road conditions. Finally, the bounded-input bounded-output (BIBO) stability of the algorithm is well demonstrated. Simulation and bench test results indicate that the proposed algorithm can not only significantly improve road holding and ride comfort, but also realize a robust observation of the augmented ASS state. In addition, the solving time of the bench test satisfies the real-time requirements of the algorithm.

Robust Predictor-Based Control for Multirotor UAV With Various Time Delays

This paper presents a robust predictor-based sliding mode control (RPSMC) for multirotor unmanned aerial vehicles (UAVs) to ensure desired tracking control under time delays which appear in practice by communications, complex computation, and actuator delays. Many UAV applications have difficulty in control and operation due to various types of time delays, resulting in repeated commands, severe control instability, and then mission failure. However, existing controllers have limitations in solving time delay problems. In this paper, the RPSMC with the prediction of future disturbance and reference trajectory is developed for the multirotor UAV to minimize the effects of time delay, robustly deal with external disturbances, and further achieve desired tracking control. The performance of RPSMC for the multirotor UAV is verified under various time delays and disturbances in numerical simulations. The results show the robustness and fast control convergence compared with proportional-integral-differential and conventional predictor-based controllers. Experimental results with step response and tracking of circular trajectory demonstrate the feasibility and performance of the RPSMC for UAVs in the presence of various time delays and disturbances.