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This paper aims to provide a precise tracking control scheme for multi-input multi-output “MIMO” nonlinear systems with unknown input time-delay in industrial process.

The predictive control scheme based on multi-dimensional Taylor network (MTN) model is proposed. First, for the unknown input time-delay, the cross-correlation function is used to identify the input time-delay through just the input and output data. And then, the scheme of predictive control is designed based on the MTN model. It goes as follows: a recursive d-step-ahead MTN predictive model is developed to compensate the influence of time-delay, and the extended Kalman filter (EKF) algorithm is applied for its learning; the multistep predictive objective function is designed, and the optimal controlled output is determined by iterative refinement; and the convergence of MTN predictive model and the stability of closed-loop system are proved.

Simulation results show that the proposed scheme is of desirable generality and capable of performing the tracking control for MIMO nonlinear systems with unknown input time-delay in industrial process effectively, such as the continuous stirred tank reactor (CSTR) process, which provides a considerably improved performance and effectiveness. The proposed scheme promises strong robustness, low complexity and easy implementation.

For the limitations of proposed scheme, the time-invariant time-delay is only considered in time-delay identification and control schemes. And the CSTR process is only introduced to prove that the proposed scheme can adapt to practical industrial scenario.

The originality of the paper is that the proposed MTN control scheme has good tracking performance, which solves the influence of time-delay, coupling and nonlinearity and the real-time performance for MIMO nonlinear systems with unknown input time-delay.

The purpose of this paper is to present a separable identification algorithm for a multiple-input single-output (MISO) continuous-time (CT) system.

This paper proposes an optimal method for the identification of MISO CT systems with unknown time delays by using the Simplified Refined Instrumental Variable method.

Simulations results are presented to show the performance of the proposed approach in the presence of additive output measurement noise.

This paper presents an optimal and robust method to separable delays and parameter identification of a MISO CT system with unknown time delays from sampled input/output data.

This paper aims to address the speed and flux tracking problem of an induction motor (IM) drive that propels an electric vehicle (EV). A new continuous control law is developed for an IM drive by using the backstepping design associated with the Robust Integral Sign of the Error (RISE) technique.

First, the rotor field-oriented IM dynamic model is derived. Then, a RISE-backstepping approach is proposed to compensate for the load torque disturbance under the assumptions that the disturbances are C² class functions with bounded time derivatives.

The numerical validation results have presented good control performances in terms of speed and flux reference tracking. It is also robust against load disturbances rejection and IM parameters variation compared to the conventional Field-Oriented Control design. Besides, the asymptotic stability and the boundedness of the closed-loop signals is guaranteed in the context of Lyapunov.

A very relevant strategy based on a conjunction of the backstepping design with the RISE technique is proposed for an IM drive. The approach remains simple and can be scaled to different applications.

This paper aims to study the issues of output reachable set estimation for the linear singular Markovian jump systems (SMJSs) with time-varying delay based on a proportional plus derivative (PD) bumpless transfer (BT) output feedback (OF) control scheme.

To begin with, a sufficient criterion is given in the form of a linear matrix inequality based on the Lyapunov stability theory. Then, a PD-BT OF controller is designed to keep all the output signs of the system are maintain within a predetermined ellipsoid. Finally, numerical and practical examples are used to demonstrate the efficiency of the approach.

Based on PD control and BT control method, an OF control strategy for the linear SMJSs with time-varying delay is proposed.

The output reachable set synthesis of linear SMJSs with time-varying delay can be solved by using the proposed approach. Besides, to obtain more general results, the restrictive assumptions of some parameters are removed. Furthermore, a sufficiently small ellipsoid can be obtained by the design scheme adopted in this paper, which reduces the conservatism of the existing results.

This paper aims to investigate an identification strategy for the nonlinear state-space model (SSM) in the presence of an unknown output time-delay. The equations to estimate the unknown model parameters and output time-delay are derived simultaneously in the proposed strategy.

The unknown integer-valued time-delay is processed as a latent variable which is uniformly distributed in a priori known range. The estimations of the unknown time-delay and model parameters are both realized using the Expectation-Maximization (EM) algorithm, which has a good performance in dealing with latent variable issues. Moreover, the particle filter (PF) with an unknown time-delay is introduced to calculated the Q-function of the EM algorithm.

Although amounts of effective approaches for nonlinear SSM identification have been developed in the literature, the problem of time-delay is not considered in most of them. The time-delay is commonly existed in industrial scenario and it could cause extra difficulties for industrial process modeling. The problem of unknown output time-delay is considered in this paper, and the validity of the proposed approach is demonstrated through the numerical example and a two-link manipulator system.

The novel approach to identify the nonlinear SSM in the presence of an unknown output time-delay with EM algorithm is put forward in this work.

This study/paper aims to present a separable identification algorithm for a multiple input single output (MISO) continuous time (CT) hybrid “Box–Jenkins”.

This paper proposes an optimal method for the identification of MISO CT hybrid “Box–Jenkins” systems with unknown time delays by using the two-stage recursive least-square (TS-RLS) identification algorithm.

The effectiveness of the proposed scheme is shown with application to a simulation example.

A two-stage recursive least-square identification method is developed for multiple input single output continuous time hybrid “Box–Jenkins” system with multiple unknown time delays from sampled data. The proposed technique allows the division of the global CT hybrid “Box–Jenkins” system into two fictitious subsystems: the first one contains the parameters of the system model, including the multiple unknown time delays, and the second contains the parameters of the noise model. Then the TS-RLS identification algorithm can be applied easily to estimate all the parameters of the studied system.

This paper aims to use a new design approach based on a Lagrange mean value theorem for the stabilization of multivariable input‐delayed system by linear controller.

The delay‐dependent asymptotical stability conditions are derived by using augmented Lyapunov‐Krasovskii functionals and formulated in terms of conventional Lyapunov matrix equations and some simple matrix inequalities. Proposed design approach is extended to robust stabilization of multi‐variable input‐delayed systems with unmatched parameter uncertainties. The maximum upper bound of delay size is computed by using a simple optimization algorithm.

A liquid monopropellant rocket motor with a pressure feeding system is considered as a numerical design example. Design example shows the effectiveness of the proposed design approach.

The proposed approach can be used in the analysis and design of the uncertain multivariable time‐delay systems.

The paper has a great potential in the stability analysis of time‐delay systems and design of time‐delay controllers and may openup a new direction in this area.

Underwater shuttle is widely used in scenarios of deep sea transportation and observation. As messages are transmitted via the limited network, high transmission time-delay often leads to information congestion, worse control performance and even system crash. Moreover, due to the nonlinear issues with respect to shuttle’s heading motion, the delayed transmission also brings extra challenges. Hence, this paper aims to propose a co-designed method, for the purpose of network scheduling and motion controlling.

First, the message transmission scheduling is modeled as an optimization problem via adaptive genetic algorithm. The initial transmission time and the genetic operators are jointly encoded and adjusted to balance the payload in network. Then, the heading dynamic model is compensated for the delayed transmission, in which the parameters are unknown. Therefore, the adaptive sliding mode controller is designed to online estimate the parameters, for enhancing control precision and anti-interference ability. Finally, the method is evaluated by simulation.

The messages in network are well scheduled and the time delay is thus reduced, which increases the quality of service in network. The unknown parameters are estimated online, and the quality of control is enhanced. The control performance of the shuttle control system is thus increased.

The paper is the first to apply co-design method of message scheduling and attitude controlling for the underwater unmanned vehicle, which enhaces the control performance of the network control system.

Acoustic emission analysis (AE) is used for characterization and location of developing defects in materials. AE sources often generate a mixture of various statistically independent signals. One difficult problem of AE analysis is the separation and characterization of signal components when the signals from various sources and the way in which the signals were mixed are unknown. Recently, blind source separation (BSS) by independent component analysis (ICA) has been used to solve these problems. The main purpose of this paper is to demonstrate the applicability of ICA to time‐delay estimation of two independent continuous AE sources on an aluminum beam. It is shown that it is possible to estimate time delays by ICA, and thus to locate two independent simultaneously emitted sources.

This paper aims to present a novel modular design framework for the haptic teleoperation of single-master/multiple-slave (SM/MS) systems with cooperating manipulators.

The user commands the remote-leader robot and the slave remote robot follows the leader in a leader–follower formation. The remote-slave is purely force-controlled. A virtual model of the remote environment is introduced between the local and remote environments through simulation software. Locally generated motion inputs are transmitted to the remote environment through the virtual model. A haptic coupling is designed in the virtual environment and the haptic feedback is transmitted to the user along with the forces measured in the remote environment. The controllers proposed in this work are experimentally evaluated with experienced and inexperienced users.

The proposed haptic interaction model contributes to the total force feedback and smoothens the high-frequency signals occurring at the physical interaction in the remote environment. Experimental results show that the implemented controllers including the proposed haptic interaction improve the teleoperation performances in terms of trajectory tracking. Furthermore, pure force control of the remote-slave is shown to enhance the robustness of the teleoperation against external disturbances. Satisfactory teleoperation performances are observed with both experienced and inexperienced users.

The proposed SM/MS teleoperation system involves a multi-purpose virtual simulator and a purely force-controlled remote-slave manipulator in a modular cooperative configuration. The uniquely defined structure of the proposed haptic coupling is used in modeling the interaction between the local and remote manipulators on the one hand, and between cooperating remote manipulators on the other.