Transactions of the Institute of Measurement and Control

Munje, Ravindra; Patre, Balasaheb

doi: 10.1177/01423312231187445pmid: N/A

Design of feedback control, for a system with slow and fast eigenvalue modes, using single sampling rate results in either information loss (for a larger sampling period) or increased computations (for a smaller sampling period). This paper contributes to designing multirate state feedback controllers for such systems. In this, it is shown that the feedback control for a linear time-invariant system, having slow and fast eigenvalue modes, can be successfully designed by multirate sampling of states in two stages. Multirate sampling refers to sampling slow- and fast-varying states at different rates, that is sampling slow states at a lower rate than the fast states. Here, two approaches for multirate sampling of states are presented, depending on the sampling sequence. In the first approach, fast subsystem states are sampled initially, and then, slow subsystem states are sampled, whereas in the second approach, slow subsystem states are sampled before sampling the fast subsystem states. As far as the two-stage design is concerned, the first stage of the design of feedback control is initiated just after sampling the first subsystem. Then, the left subsystem is sampled, and the second stage of the design of feedback control is accomplished. It is proved that the feedback controls derived with the multirate sampling of states stabilize the full-order system in both approaches. The design and implementation aspects of both approaches are compared. Finally, the applicability of the proposed control is demonstrated by simulating two examples. Simulations are also compared with other methods proposed in the literature.

Li, Chen-Long; Yan, Hong-Sen; Zhang, Chao

doi: 10.1177/01423312231180946pmid: N/A

In this paper, a recursive d-step-ahead predictive control scheme based on multi-dimensional Taylor network (MTN) is proposed for the real-time tracking control of multiple-input multiple-output (MIMO) nonlinear systems with input time-delay. The MTN predictive model is designed using a recursive approach to compensate the influence of time-delay, and an extended Kalman filter (EKF) is applied as its learning algorithm. An MTN controller is developed based on a proportional–integral–derivative (PID) controller where the closed-loop errors between the reference input and the system output are set as the MTN controller’s inputs. Then, a back propagation (BP) algorithm, designed to update its weights according to errors caused by system uncertainty, is used as a learning algorithm for the MTN controller. Meanwhile, the convergence of the MTN predictive model and the stability of the closed-loop system are evaluated. Two numerical examples and a practical example – continuous stirred tank reactor (CSTR) process are presented to verify the superiority of the proposed scheme. The experimental results and the computational complexity analysis show that the proposed scheme is effective, promising its desirable robustness, anti-disturbance, tracking and real-time performance.

Hernandez-Gonzalez, M; Alanis, AY; Rios, JD; Hernadez-Vargas, EA

doi: 10.1177/01423312231188774pmid: N/A

This paper proposes a control algorithm based on the sliding mode (SM) control law with an exponential term which improves the convergence to the origin. Such convergence to the origin is compared with other existing results such as the super-twisting (ST) and an SM controller with exponential terms. To compensate for unknown but bounded disturbances, a discontinuous function through an integral action has been employed. The presented algorithm with the exponential term, or exponential SM control algorithm, has been employed to a nonlinear system that can be represented into a block controllable form to follow an admissible trajectory. Finally, a real-time experiment and a numerical simulation have been carried out on a direct current motor to show effectiveness of the proposed control law. Numerical simulations have been compared with the ST control law and with a predefined-time controller with expon0ential terms.

Li, He; Liu, Cheng-Lin; Zhang, Ya; Chen, Yang-Yang

doi: 10.1177/01423312231185377pmid: N/A

This paper tries to solve the quantized practical fixed-time consensus tracking problem for networked Euler–Lagrange systems under the predetermined workspace. To realize the information interaction under the limited bandwidth, a set of encoder, decoder, and average quantizers are constructed to process the interaction data. On this basis, a fixed-time observer is proposed so that each follower can estimate the leader’s information within the quantized communication environment. Afterward, the local tracking control algorithm is designed by using backstepping strategy and adaptive technology, and the state constraint function is introduced to cope with the asymmetric time-varying constraint problem. With the Lyapunov stability criterion, all error signals are guaranteed to remain in the compact sets near the origin within the fixed time. Ultimately, a numerical example is carried out to testify the validity of the proposed scheme.

Zhang, Tu; Zhang, Guobao; Huang, Yongming

doi: 10.1177/01423312231189107pmid: N/A

This paper studies a secure iterative interval estimation approach for cyber-physical systems subject to stealthy deception attacks. Under the hypothesis that the system is accessed by a stealthy attack, an iteration scheme integrating the T-N-L observer framework is employed to reconstruct the system state. With the help of a structure separation method, a sufficient condition in terms of linear matrix inequality is provided to obtain convergent observation errors under deception attacks. Resorting to the reachability analysis, a secure state interval is built by means of the analyzed attack bounds and the observation error interval. Simulation studies verify the effectiveness of the proposed method for attack and attack-free cases.

Xi, Rui-Dong; Ma, Tie-Nan; Xiao, Xiao; Yang, Zhi-Xin

doi: 10.1177/01423312231190169pmid: N/A

Robot manipulators as an indispensable part of automatic operation are becoming increasingly important in intelligent manufacturing systems, such as grinding and assembly. Although control methods of robot manipulators have been extensively studied, high-precision robots still face new challenges from uncertainties caused by changes in the environment and enhancement of interference. As a consequence, the neural network-based observer is utilized to reduce the influence of uncertainties and external disturbances. In this work, a new kind of nonlinear disturbance observer is designed which could well function with observed states. To improve the control efficiency and response characteristic, a kind of new integral sliding manifold is devised and the control input is generated via backstepping techniques. The stability is proved with Lyapunov theory, and the experimental results are given to demonstrate the effectiveness of the proposed controller.

Shan, Rui; Sun, Ping; Wang, Shuoyu; Chang, Hongbin

doi: 10.1177/01423312231190446pmid: N/A

An each step time-limited iterative learning control model was developed for a cushion robot with velocity constraints. A predictive modeling method was proposed to solve the velocity mutation problem by employing a kinematic model to constrain the velocity inputs, which can then constrain the robot’s actual velocity. Furthermore, a tracking error system was established that used constrained motion velocity and a dynamic model. A new iterative controller with each step time-limited learning was built to reduce the transient adjustment time. Simulation results and comparative analyses revealed that the proposed control method quickly stabilizes the system and ensures that the human–robot system operates at a safe velocity.

de la Cruz, Nain; Basin, Michael

doi: 10.1177/01423312231187019pmid: N/A

This paper presents the predefined-time convergent robust controller design for a brushed direct current (DC) motor system affected by matched and unmatched deterministic disturbances and stochastic noises, considering both fully measurable and incompletely measurable states. The control algorithm allows the control designer to set the convergence time a priori. The convergence time is independent of initial conditions and disturbances and noises affecting the system. Numerical simulations are conducted to demonstrate efficiency of the designed control algorithm. The obtained results show that the control algorithm counteracts the matched and unmatched disturbances, and noises in case of fully measurable states and mitigates their influence in case of incompletely measurable ones.

Liang, Qian-Kun; Cai, Yan; Song, Jin-Chun; Wang, Bing-Long

doi: 10.1177/01423312231189770pmid: N/A

This paper is focused on asymptotic tracking control of electro-hydraulic actuator (EHA) systems subject to matched and mismatched time-varying disturbances. To counteract the matched disturbance, a novel extended state observer (ESO) is proposed to achieve asymptotic convergence of the estimation error, by incorporating the strictly positive real (SPR) Lyapunov design method and a Nussbaum function. To further suppress the mismatched disturbance, an adaptive robust integral of the sign of the error (RISE) controller is formulated in the backstepping framework based on the proposed ESO. Asymptotic tracking performance is theoretically achieved via closed-loop system stability analysis. The efficacy of the proposed control scheme is verified through comparative experiments executed on an EHA test rig. In this study, a priori bounds of the disturbances and their higher-order derivatives are no longer needed, and only one auxiliary error signal is introduced. This approach loosens the restrictions on the disturbances and reduces the design conservativeness, thus making it promising in practice.

Zhang, Wenhan; Cui, Wei; Li, Xingguang; Xu, Mingzhi; Wang, Chensong

doi: 10.1177/01423312231189809pmid: N/A

In an indoor environment where global positioning system (GPS) signals are severely attenuated, ultra-wideband (UWB) and 2D lidar are widely used in the autonomous positioning of mobile platforms. However, the presence of nonline-of-sight (NLOS) environments can lead to large errors in UWB positioning, and 2D lidar will increase the cumulative error due to the loss of accuracy in sparsely textured scenes. In order to reduce the positioning error, a UWB and 2D lidar fusion positioning algorithm based on the assistance of a few landmarks is proposed in this paper. Considering the colored noise of lidar location data, a Kalman filter algorithm based on cumulative error analysis is proposed. First, the lidar error curve is fitted by the least-square method, and then the relationship between the noise covariance matrix and the lidar cumulative error function is established by introducing the scale factor, which is substituted into the Kalman prediction equation. Experimental results show that the proposed multi-sensor fusion localization algorithm is feasible, and compared with the single localization method, the proposed fusion algorithm can significantly improve the localization accuracy; matching landmarks can achieve a positioning accuracy of 0.15 m, which is about 24.4% lower than the root mean square error of traditional Kalman filter.