Transactions of the Institute of Measurement and Control

Guo, Bingbing; Sun, Yuan; Chen, Yiyang

doi: 10.1177/01423312241264860pmid: N/A

Path planning is a fundamental aspect of mobile robot navigation, playing a crucial role in enabling robots to autonomously navigate while avoiding obstacles. Nevertheless, traditional path planning algorithms face navigation challenges, including obstacle avoidance and the potential for getting stuck in local minima or deadlocks along the path. To tackle these challenges, the study proposes an enhanced path planning method based on control barrier function (CBF). This approach introduces a safety velocity adjustment mechanism based on CBF and combines it with the particle swarm optimization (PSO), adjusting the safe speed in global planning and addressing the issue of local minima. Experimental simulations are conducted to validate the flexibility and global optimization performance of the proposed path planning method across various obstacle scenarios.

Yu, Ce; Shi, Yingjing; Li, Rui

doi: 10.1177/01423312241263136pmid: N/A

This paper investigates the problem of localization and circumnavigation of an unknown stationary target in 3D space using distance measurements. An estimator is first designed in 2D space to locate the target, with which a control protocol is proposed to force the agent to travel along a circular trajectory around the target. Then the algorithm is extended to 3D space to circumnavigate the target under the condition of obtaining gravity direction additionally. In particular, the agent moves alternately in the X-Y horizontal planes and the X-Z vertical planes in different patterns under different switching conditions, which is beneficial to monitor the target from a broader perspective. Meanwhile, the rigorous stability analyses of two cases are given in terms of the Lyapunov method. Finally, the effectiveness of the proposed algorithms is verified through numerical simulations and a real experiment.

Pan, Yingnan; Ding, Bingjie; Zhang, Linchuang

doi: 10.1177/01423312241265531pmid: N/A

This paper investigates the consensus control problem for strict-feedback nonlinear multiagent systems (MASs) under false data injection attacks (FDIAs) and unknown control directions. Different from the existing results, an attack detection mechanism that only uses the information of adjacent agents is designed to judge the occurrence times of the FDIAs, and the negative effects of FDIAs are eliminated by proposing a switching topologies strategy. By introducing the multiple Nussbaum functions, the multiple unknown control directions are addressed, while the effects of these functions enhance each other rather than offset each other. It is shown that the consensus performance and the boundedness of all the signals in the closed-loop systems can be ensured. Finally, the validity of the proposed control scheme is verified through a simulation example.

Liu, Yuyu; Liu, Ding; Song, Zezhong

doi: 10.1177/01423312241265281pmid: N/A

The optimal operation of the semiconductor silicon single crystal (SSC) growth in an industrial single crystal furnace is contingent upon the precise measurement of both the crystal diameter and melt temperature. The Czochralski (CZ) silicon single crystal growth process (CZ-SSCGP) is a phase transition process from solid to liquid that takes place in a high-temperature, enclosed furnace designed for single crystal growth. The direct measurement of crystal diameter and melt temperature using sensors can pose a challenge due to the physical limitations of the furnace. This study introduces a novel approach for modeling clustering based on data that integrate fuzzy C-means (FCM) and deep belief network (DBN) techniques. The proposed method aims to estimate the online crystal diameter and melt temperature. The objective of this approach is to efficiently and precisely derive the CZ-SSCGP model. In order to address the negative impact of nonequilibrium properties of data on the model, it is advisable to employ FCM to produce numerous subsets of data that exhibit comparable operating conditions. This is especially important because data change over time and are different in different working situations. In order to enhance the precision of the fitting process for crystal diameter and melt temperature, we amalgamate the output outcomes of DBN that have been constructed for each individual subset. It is imperative to address the nonlinear and time-varying features manifested by the engineering data. Industrial data are utilized for conducting diverse data experiments and comparative analyses. The process takes into account the data’s susceptibility to contamination by outliers. The findings suggest that the method put forth exhibits superior precision and enhanced resilience in predicting crystal diameter and melt temperature compared to alternative modeling approaches.

Wang, Xiaoheng; Xiao, Zhihe; Ren, Ziming; Dong, Chunzhu; Tian, Xuan Dan

doi: 10.1177/01423312241267038pmid: N/A

In this paper, a formation cooperative tracking control problem for unmanned aerial vehicles is investigated via non–zero-sum game theory and reinforcement learning. The formation cooperative tracking control problem is converted to a multi-player non–zero-sum game problem to attain the team optimality of multiple unmanned aerial vehicles. The desired trajectory can be achieved via a game-based feedback controller to minimize the global cost function involving all control policies. The Nash equilibrium control laws are learned based on only the collected system data, without knowledge of the vehicle dynamics. The asymptotic convergence of the constructed global closed-loop control system is proven via theoretical analysis. Simulation results validate the effectiveness of the proposed control method.

Chen, Dianjun; Wang, Xueshen; Sun, Ran; Zhang, Ruizhi; Zhu, Shaoshu; Chen, Jingyuan; Ban, Mingfei

doi: 10.1177/01423312241266007pmid: N/A

This paper considers the distributed secondary control for direct current (DC) microgrid to regulate the DC bus voltage containing local loads. An adaptive event-triggered protocol is employed to update the control input to save transmission energy and ensure the control performance. Depending on the switch of the converter, a switching dynamical equation is proposed to describe a microgrid with a DC–DC boost converter. Moreover, the Lyapunov technique is developed to assure the stability of the system, which effectively handles real-time precision of input and model uncertainties. Combining this method with proportionate load sharing enables global average voltage management and the system performance preservation in DC microgrids for a variety of disturbances. Finally, the effectiveness of the proposed method is verified by simulation.

Lu, Yuan; Meng, Bo; Jin, Xuan

doi: 10.1177/01423312241267048pmid: N/A

For the uncertain nonlinear systems with prescribed performance, the command filter–based fixed-time fault estimation and compensation control strategy is investigated in this study. The radial basis function neural networks (RBFNNs) are utilized to approximate the uncertain nonlinear terms. Simultaneously, the composite disturbance observer is established to quickly estimate external disturbances, approximation errors, and additive actuation fault. Moreover, the actuation effectiveness of the actuator is quickly estimated online by constructing the cubic absolute-value Lyapunov function. Therefore, based on the fast estimation of the actuator fault parameters, the fixed-time fault-tolerant control method is proposed by adopting the command filter backstepping technology and prescribed performance function, which can compensate for the adverse effect of actuator fault and keep the tracking error stable in a short time interval. Finally, a simulation example is given to prove the performance of the designed controller.

Benharkou, Ibtihal; Gherbi, Sofiane; Sedraoui, Moussa; Bechouat, Mohcene

doi: 10.1177/01423312241266011pmid: N/A

This paper introduces a novel design of a proportional filtered-integral (P-FI) controller whose parameters are auto-tuned using the backpropagation neural networks (BPNN) algorithm. The proposed controller is designed to address the main challenges posed by a class of complex systems characterized by uncertain high-gain and pure integrator dynamics, including high-frequency noise amplification and poor robustness against parameter variations. Designing such a controller involves three main steps: First, a proportional–integral–derivative (PID) controller is designed, with its parameters auto-tuned online using the BPNN algorithm, resulting in the primary (BPNN-PID) controller. Second, the obtained parameters of the previous controller are utilized to compute those of a low-pass filter offline. This filter is then cascaded with the integral parts of a PID controller, forming a P-FI controller structure. This configuration introduces a phase lead within a specific frequency range without amplifying high-frequency noise, overcoming the primary disadvantage of the derivative term. Finally, the parameters of the resulting P-FI controller are again auto-tuned online using the BPNN algorithm, resulting in the final robust (BPNN-P-FI) controller. This novel controller structure and its parameters tuning procedure, based on a two-stage BPNN learning approach, constitute the main contributions of this paper. Simulation results demonstrate the superiority of the proposed controller in terms of time-domain performance, sensor noise attenuation, and closed-loop robustness compared to those obtained with the BPNN-PID and optimally tuned PID controllers.

Guo, Hu; Zhang, Xinquan

doi: 10.1177/01423312241273763pmid: N/A

For nonlinear discrete time-delay saturated switched systems with memory feedback, the problem of actuator failure with external disturbances is studied. First, we propose a sufficient condition for the existence of a fault-tolerant state feedback law with memory by using the switched Lyapunov function (SLF) strategy, which ensures that the closed-loop system has a maximum tolerant disturbance capacity by solving the optimization problem. Then, the minimum upper bound on the L2-gain is estimated by solving the optimization problem. Finally, the reliability of the proposed method is verified by examples.

Wan, Min; Du, Jiadai; Yi, Hao

doi: 10.1177/01423312241273766pmid: N/A

Under different working conditions and control objectives, fuzzy model predictive control is used to optimize the control effect by adjusting the weight to achieve stable and accurate control for the dynamic positioning control of offshore platforms. Also, aiming at the situation where the measurement noise of dynamic positioning control may be time-varying and non-Gaussian and the statistics of the noise are not exact, this paper proposes a variational Bayesian extended Kalman filter observer. In order to model non-Gaussian noise more reasonably, the joint posterior distribution of non-Gaussian noise with state and time-varying covariance is described as the product of Gaussian and independent inverse gamma distributions. Then, the variational Bayesian (VB) method was used to simultaneously estimate the system state, the intermediate variable values of the new distribution, and the noise parameters through fixed-point iteration. The fuzzy algorithm is applied to the model predictive control, and the weight parameters of the model predictive control are adaptively changed to better adapt to the changing working conditions in the control process. The simulation results show that the variational Bayesian extended Kalman filter observer has better performance than the existing algorithms when dealing with the time-varying non-Gaussian observation noise, whose statistics are not exact. The variational Bayesian extended Kalman filter observer can significantly improve the accuracy of the control when it is used in fuzzy model predictive control.