Input Delay Research Articles

Hysteresis Inverse-Based Tracking Control of a Class of Hysteretic Nonlinear Systems With Input and Output Delays

Hysteresis Inverse-Based Tracking Control of a Class of Hysteretic Nonlinear Systems With Input and Output Delays

Memoryless Dual-Observer-Based Output Feedback Stabilization of Linear Systems With Input and Output Delays.

This article focuses on the dual-observer-based stabilization of linear systems with delays in both the inputs and outputs. By a model reduction approach the system can be converted to an equivalent linear system without delays, for which a dual-observer-based stabilizing controller can be designed. However, such a controller is infinite dimensional or memory-based. To solve such a problem, a modified memoryless dual-observer-based stabilizing controller is designed, and the closed-loop stability is proven under some additional conditions. Compared with the reduced-order observer-based controller, the dimension of the dual-observer-based controller is smaller if the system has more inputs than outputs. At the same time, the design approach is more challenging in proving closed-loop stability, as for example a more intricate Lyapunov-Krasovskii functional has to be constructed associated with the proposed approach. The proposed approach is applicable to both continuous-time and discrete-time systems. Numerical simulations validate the effectiveness of the proposed method.

Adaptive tracking control of high-order nonlinear systems with unknown time-varying input delay and unmodeled dynamics

Adaptive tracking control of high-order nonlinear systems with unknown time-varying input delay and unmodeled dynamics

LQG control for networked control systems with packet dropouts and input delay: UDP case

LQG control for networked control systems with packet dropouts and input delay: UDP case

Reachable set control of singular semi-Markov jump multi-agent systems with input delay via state decomposition method

This paper addresses the challenge of reachable set estimation in singular multi-agent systems with time-delay and semi-Markov switching topologies. The primary goal is to construct an ellipsoid that encompasses all states originating from the origin. Furthermore, criteria for reachable sets with reduced conservatism are developed. The singular multi-agent systems are reduced by the Laplace matrix, then the Lyapunov–Krasovskii functional is constructed by using decomposed state vector. In addition, the sufficient conditions for the reachable set of singular multi-agent systems bounded by ellipsoids are given by matrix theory and linear matrix inequality. Numerical examples show that our results is effective.

Manual steering and robust adaptive constrained control of ship autopilot

This study proposes two schemes for ship autopilot: the first is a follow-up mode, applying manual dynamics to the state-space-based underactuated large merchant ship (ULMS) model, using a steering wheel and joystick to emulate manual operation; the second is a Nav mode developed by path-following control. In this mode, a novel path-following control strategy is developed for ULMS with input saturation and delay, two novel auxiliary systems are employed to stabilize the plant with input constraints. The input delay auxiliary system (IDAS) effectively tackles the design challenges posed by input delay, while the input saturation auxiliary system (ISAS) is dedicated to generating actual control inputs under the constraint of saturation. Furthermore, the dynamic surface control (DSC) technique and the roust neural damping technique are introduced to mitigate the computational burden and simplify the controller structure, thereby enhancing the efficiency and performance of the entire control system. Lyapunov stability analysis proves the ship motion system is semi-globally uniformly ultimately bounded (SGUUB). Finally, the effectiveness of these two schemes is verified through turning experiment, comparative simulations and a semi-physical simulation platform that integrates physical models with computer simulation technology.

Robust super‐twisting sliding mode control of input‐delayed nonlinear systems using disturbance observers and predictor feedback

AbstractThis paper investigates prediction‐based robust super‐twisting sliding mode controller design for nonlinear systems with input delay and external disturbances. A novel predictor feedback is introduced based on state predictions of the original and transformed models. To cope with the external disturbances, a set of disturbance observers are utilized which estimate the future values of disturbances asymptotically. Super‐twisting sliding mode control is used to avoid chattering phenomenon which is also able to handle external disturbances. Proposed design can handle all external disturbances that their derivatives vanish at infinity. Stability proof of the closed‐loop is given in details ensuring the asymptotic stability of the tracking errors and finite time vanishing of the sliding surface. To illustrate effectiveness and applicability, the proposed design is applied to a two‐link robot and a serial variable stiffness actuator. Simulation results show that input delays of the robot and actuator are compensated, and external disturbances are correctly estimated and attenuated.

Optimal quantized feedback control for linear quandratic Gaussian systems with input delay

AbstractThis paper is concerned with the optimal quantized feedback linear quadratic Gaussian (LQG) control problem for a discrete‐time stochastic system with input delay as well as the measurements to be quantized before transmitted to the controller. In this scenario, the system is presented with several choices of quantizers, along with the cost of using each quantizer. The objective is to jointly select the quantizers and synthesize the controller to maintain an optimal balance between control performance and quantization cost. It is shown that this problem can be decoupled into two optimization problems when the innovation signal is quantized instead of state: one for optimal controller synthesis and the other for optimal quantizer selection. More specifically, a necessary and sufficient condition is derived for the optimal control problem based on Pontryagin's maximum principle. On the other hand, the optimal quantizer selection policy is established by dealing with a certain Markov decision process (MDP).

A novel second-order predictive disturbance observer for systems with input and output delays

This paper proposes a novel second-order predictive disturbance observer for systems experiencing external disturbance with both input and output measurement delays. The proposed observer uses the delayed system state and its derivative information to predict and compensate for future disturbance in real time, despite the delayed control input. Compared with the past studies about second-order disturbance observer, this paper analyses the impact of the disturbance derivatives on the estimation accuracy. This observer is designed using the Lyapunov–Razumikhin theorem and its exponential stability is proven. Comparative analysis and simulations demonstrate its advantages over second-order disturbance observers that do not consider delays.

Fixed-time prescribed performance tracking for nonlinear systems with unknown time-varying input delay

Fixed-time prescribed performance tracking for nonlinear systems with unknown time-varying input delay

Prescribed Performance Sliding Mode Control of Vehicular Platoons With Input Delays

Prescribed Performance Sliding Mode Control of Vehicular Platoons With Input Delays

Input delay compensation of nonlinear systems with uncertain time-varying parameters

A robust adaptive control law is designed for the class of nonlinear systems with uncertain time-varying parametric dynamics and input delays. The Constitution of control law design is based on filtered tracking errors, previous values of control input and estimate of uncertain parameters which is updated by the update law. An arbitrary large input delay compensation is guaranteed by deriving sufficient control gain conditions using Lyapunov-Krassovskii based stability analysis. The efficacy of the design is verified by Simulation results of two-link robot manipulator dynamical system.

Solving optimal predictor-feedback control using approximate dynamic programming

This paper is concerned with approximately solving the optimal predictor-feedback control problem of multiplicative-noise systems with input delay in infinite horizon. The optimal predictor-feedback control, provided by the analytical method, is determined by Riccati–ZXL equations and is hard to obtain in the case of unknown system dynamics. We aim to propose a policy iteration (PI) algorithm for solving the optimal solution by approximate dynamic programming. For convergence analysis of the algorithm, we first develop a necessary and sufficient stabilizing condition, in the form of several new Lyapunov-type equations, which parameterizes all predictor-feedback controllers and can be seen as an important addition to Lyapunov stability theory. We then propose an iterative scheme for the Riccati–ZXL equations computations, along with convergence analysis, based on the condition. Inspired by this scheme, a data-driven online PI algorithm, convergence implied in that of the iterative scheme, is proposed for the optimal predictor-feedback control problem without full system dynamics. Finally, a numerical example is used to evaluate the proposed PI algorithm.

Simultaneous compensation of input delay and state/input quantization for linear systems via switched predictor feedback

We develop a switched predictor-feedback law, which achieves global asymptotic stabilization of linear systems with input delay and with the plant and actuator states available only in (almost) quantized form. The control design relies on a quantized version of the nominal predictor-feedback law for linear systems, in which quantized measurements of the plant and actuator states enter the predictor state formula. A switching strategy is constructed to dynamically adjust the tunable parameter of the quantizer (in a piecewise constant manner), in order to initially increase the range and subsequently decrease the error of the quantizers. The key element in the proof of global asymptotic stability in the supremum norm of the actuator state is derivation of solutions’ estimates combining a backstepping transformation with small-gain and input-to-state stability arguments, for addressing the error due to quantization. We extend this result to the input quantization case and illustrate our theory with a numerical example.

Precision Control for Room Temperature of Variable Air Volume Air-Conditioning Systems with Large Input Delay

A large input delay, parametric uncertainties, matched disturbances and mismatched disturbances exist extensively in variable air volume air-conditioning systems, which can deteriorate the control performance of the room temperature and even destabilize the system. To address this problem, an adaptive-gain command filter control framework for the room temperature of variable air volume air-conditioning systems is exploited. Through skillfully designing an auxiliary system, both the filtered error and the input delay can be compensated concurrently, which can attenuate the effect of the filtered error and the input delay on the control performance of the room temperature. Then, a smooth nonlinear term with an adjusted gain is introduced into the control framework to compensate for parametric uncertainties, matched disturbances and mismatched disturbances, which relieves the conservatism of the controller gain selection. With the help of the Lyapunov theory, both the boundedness of all the system signals and the asymptotic tracking performance for the room temperature can be assured with the presented controller. Finally, the contrastive simulation results demonstrate the validity of the developed method.

Secure control design for cooperative adaptive cruise control with a time-varying input delays under false data injection attacks

ABSTRACT Connected and Autonomous Vehicles (CAVs) are shaping the future of transportation systems; however, their future development requires consideration of the upcoming challenges with cyber-physical attacks. Cooperative Adaptive Cruise Control (CACC) is one of the features used in CAVs. Using CACC increases the vulnerability of such systems to False Data Injection (FDI) attacks. To address this, this paper proposes a novel mitigation approach to compensate for FDI attacks and noise in real-time. In addition to FDI attacks, the current CACC algorithm suffers from input delay and disturbance. Here, we develop a nonlinear controller and an FDI attacks estimation technique to mitigate the effects of FDI attacks, noise, input delay and additive disturbance. Lyapunov–Krasovskii functionals are used in the Lyapunov-based stability analysis to ensure semi-global uniformly ultimately bounded tracking. In order to demonstrate the performance of the proposed approach, we validate it through simulation analysis.

Adaptive Finite-Time Constrained Attitude Stabilization for an Unmanned Helicopter System under Input Delay and Saturation

Adaptive Finite-Time Constrained Attitude Stabilization for an Unmanned Helicopter System under Input Delay and Saturation

Human-in-the-loop control of dynamics and robotics using augmented reality

Human-machine interaction (HMI) and human-robot interaction (HRI) offer innovative approaches for the analysis of structural dynamics both in laboratory and real-world scenarios. In vibratory experimentation, an external force is generated to evaluate dynamic responses of structures. One mode of generation is to use electrodynamic shakers. Sensors are attached to the structure to measure dynamic responses and can be deployed by robots. In post-disaster inspections robotic deployment ensures the safety of inspectors and requires control. If the interface between operators and the controls was augmented, then operators can visualize experiments, exciter levels, and define robot input while maintaining awareness of the area of interest. Robots can provide better aid to humans if control of the robot is: (1) quantified and presented to the human; and (2) conducted in real-time for human feedback. This research proposes using Augmented Reality (AR) to provide operators with sensor feedback and control for analysis of dynamic structures with robots. This method improves cognition by allowing the operator to maintain awareness of structures while adjusting conditions accordingly with the assistance of the new real-time interface. One interface application is developed to plot sensor data in addition to voltage, frequency, and duration controls for vibration simulations. Two more applications are developed following the same framework, one to control the position of a mediating robot and one to control the frequency of the robot's movement. The applications serve as a first-step approach investigating potential robotic sensor deployment in disaster assessment of structures. This paper presents the proposed model for the new vibratory control loop and then compares the new approach with a traditional method by measuring time delay in control input. The study also demonstrates that AR enhances time efficiency and accuracy in controlling robotic arm positions, matching actuator frequencies, and deploying sensors with minimal time delay.

PDE-based observation and predictor-based control for linear systems with distributed infinite input and output delays

This paper considers output feedback stabilization of linear systems with both distributed infinite input and output delays. We first propose a PDE-based observer to deal with distributed infinite output delays. The main idea is to transform infinite-delayed output into a transport PDE with a semi-infinite domain. It is proved that the resulted error systems are exponentially stable and thus the observer is effective. Furthermore, relying on the PDE-based observer, a predictor-based output feedback stabilization controller is provided. This work removes a restrictive condition on eigenvalues of open-loop system in the previous work, which is a distinctive advantage of this work. A simulation example is given to illustrate the effectiveness of our proposed observer and controller.

Protocol-based secure guaranteed cost control of sampled-data T-S fuzzy system with denial-of-service attack and input saturation

This paper proposes a protocol-based secure guaranteed cost sampled-data controller design problem of Takagi-Sugeno (T-S) fuzzy system under denial-of-service (DoS) attack and input saturation. First, in the absence of an effective description for the characteristics of received signals on controller, a novel scheduling protocol is proposed according to the characteristics of the round-robin protocol and DoS attack, which can guarantee the specific sequence characteristic for control updates. Then, via introducing input delay approach and switched system modeling method, a time-delay switched system with fixed switching sequence is introduced to describe the considered system with DoS phenomenon. Moreover, some novel control criteria are presented to ensure the resulting closed-loop system reaches a required cost level under the obtained control law. And an optimization problem is proposed to compute the optimal upper of the specified cost level. Finally, a simulation example is presented to demonstrate the effectiveness of the algorithm.