IEEE/CAA Journal of Automatica Sinica

A Survey of Output Feedback Robust MPC for Linear Parameter Varying Systems

Xubin Ping, Jianchen Hu, Tingyu Lin, Baocang Ding, Peng Wang, Zhiwu Li

2022, 9(10): 1717-1751. doi: 10.1109/JAS.2022.105605

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Abstract:
For constrained linear parameter varying (LPV) systems, this survey comprehensively reviews the literatures on output feedback robust model predictive control (OFRMPC) over the past two decades from the aspects on motivations, main contributions, and the related techniques. According to the types of state observer systems and scheduling parameters of LPV systems, different kinds of OFRMPC approaches are summarized and compared. The extensions of OFRMPC for LPV systems to other related uncertain systems are also investigated. The methods of dealing with system uncertainties and constraints in different kinds of OFRMPC optimizations are given. Key issues on OFRMPC optimizations for LPV systems are discussed. Furthermore, the future research directions on OFRMPC for LPV systems are suggested.

Interaction-Aware Cut-In Trajectory Prediction and Risk Assessment in Mixed Traffic

Xianglei Zhu, Wen Hu, Zejian Deng, Jinwei Zhang, Fengqing Hu, Rui Zhou, Keqiu Li, Fei-Yue Wang

2022, 9(10): 1752-1762. doi: 10.1109/JAS.2022.105866

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Abstract:
Accurately predicting the trajectories of surrounding vehicles and assessing the collision risks are essential to avoid side and rear-end collisions caused by cut-in. To improve the safety of autonomous vehicles in the mixed traffic, this study proposes a cut-in prediction and risk assessment method with considering the interactions of multiple traffic participants. The integration of the support vector machine and Gaussian mixture model (SVM-GMM) is developed to simultaneously predict cut-in behavior and trajectory. The dimension of the input features is reduced through Chebyshev fitting to improve the training efficiency as well as the online inference performance. Based on the predicted trajectory of the cut-in vehicle and the responsive actions of the autonomous vehicles, two risk measurements are introduced to formulate the comprehensive interaction risk through the combination of Sigmoid function and Softmax function. Finally, the comparative analysis is performed to validate the proposed method using the naturalistic driving data. The results show that the proposed method can predict the trajectory with higher precision and effectively evaluate the risk level of a cut-in maneuver compared to the methods without considering interaction.

Designing Discrete Predictor-Based Controllers for Networked Control Systems with Time-varying Delays: Application to A Visual Servo Inverted Pendulum System

Yang Deng, Vincent Léchappé, Changda Zhang, Emmanuel Moulay, Dajun Du, Franck Plestan, Qing-Long Han

2022, 9(10): 1763-1777. doi: 10.1109/JAS.2021.1004249

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Abstract:
A discrete predictor-based control method is developed for a class of linear time-invariant networked control systems with a sensor-to-controller time-varying delay and a controller-to-actuator uncertain constant delay, which can be potentially applied to vision-based control systems. The control scheme is composed of a state prediction and a discrete predictor-based controller. The state prediction is used to compensate for the effect of the sensor-to-controller delay, and the system can be stabilized by the discrete predictor-based controller. Moreover, it is shown that the control scheme is also robust with respect to slight message rejections. Finally, the main theoretical results are illustrated by simulation results and experimental results based on a networked visual servo inverted pendulum system.

A New Noise-Tolerant Dual-Neural-Network Scheme for Robust Kinematic Control of Robotic Arms With Unknown Models

Ning Tan, Peng Yu, Zhiyan Zhong, Fenglei Ni

2022, 9(10): 1778-1791. doi: 10.1109/JAS.2022.105869

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Abstract:
Taking advantage of their inherent dexterity, robotic arms are competent in completing many tasks efficiently. As a result of the modeling complexity and kinematic uncertainty of robotic arms, model-free control paradigm has been proposed and investigated extensively. However, robust model-free control of robotic arms in the presence of noise interference remains a problem worth studying. In this paper, we first propose a new kind of zeroing neural network (ZNN), i.e., integration-enhanced noise-tolerant ZNN (IENT-ZNN) with integration-enhanced noise-tolerant capability. Then, a unified dual IENT-ZNN scheme based on the proposed IENT-ZNN is presented for the kinematic control problem of both rigid-link and continuum robotic arms, which improves the performance of robotic arms with the disturbance of noise, without knowing the structural parameters of the robotic arms. The finite-time convergence and robustness of the proposed control scheme are proven by theoretical analysis. Finally, simulation studies and experimental demonstrations verify that the proposed control scheme is feasible in the kinematic control of different robotic arms and can achieve better results in terms of accuracy and robustness.

A Fully Distributed Hybrid Control Framework For Non-Differentiable Multi-Agent Optimization

Xia Jiang, Xianlin Zeng, Jian Sun, Jie Chen, Yue Wei

2022, 9(10): 1792-1800. doi: 10.1109/JAS.2022.105872

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Abstract:
This paper develops a fully distributed hybrid control framework for distributed constrained optimization problems. The individual cost functions are non-differentiable and convex. Based on hybrid dynamical systems, we present a distributed state-dependent hybrid design to improve the transient performance of distributed primal-dual first-order optimization methods. The proposed framework consists of a distributed constrained continuous-time mapping in the form of a differential inclusion and a distributed discrete-time mapping triggered by the satisfaction of local jump set. With the semistability theory of hybrid dynamical systems, the paper proves that the hybrid control algorithm converges to one optimal solution instead of oscillating among different solutions. Numerical simulations illustrate better transient performance of the proposed hybrid algorithm compared with the results of the existing continuous-time algorithms.

Integrating Conjugate Gradients Into Evolutionary Algorithms for Large-Scale Continuous Multi-Objective Optimization

Ye Tian, Haowen Chen, Haiping Ma, Xingyi Zhang, Kay Chen Tan, Yaochu Jin

2022, 9(10): 1801-1817. doi: 10.1109/JAS.2022.105875

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Abstract:
Large-scale multi-objective optimization problems (LSMOPs) pose challenges to existing optimizers since a set of well-converged and diverse solutions should be found in huge search spaces. While evolutionary algorithms are good at solving small-scale multi-objective optimization problems, they are criticized for low efficiency in converging to the optimums of LSMOPs. By contrast, mathematical programming methods offer fast convergence speed on large-scale single-objective optimization problems, but they have difficulties in finding diverse solutions for LSMOPs. Currently, how to integrate evolutionary algorithms with mathematical programming methods to solve LSMOPs remains unexplored. In this paper, a hybrid algorithm is tailored for LSMOPs by coupling differential evolution and a conjugate gradient method. On the one hand, conjugate gradients and differential evolution are used to update different decision variables of a set of solutions, where the former drives the solutions to quickly converge towards the Pareto front and the latter promotes the diversity of the solutions to cover the whole Pareto front. On the other hand, objective decomposition strategy of evolutionary multi-objective optimization is used to differentiate the conjugate gradients of solutions, and the line search strategy of mathematical programming is used to ensure the higher quality of each offspring than its parent. In comparison with state-of-the-art evolutionary algorithms, mathematical programming methods, and hybrid algorithms, the proposed algorithm exhibits better convergence and diversity performance on a variety of benchmark and real-world LSMOPs.

Structured Sparse Coding With the Group Log-regularizer for Key Frame Extraction

Zhenni Li, Yujie Li, Benying Tan, Shuxue Ding, Shengli Xie

2022, 9(10): 1818-1830. doi: 10.1109/JAS.2022.105602

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Abstract:
Key frame extraction based on sparse coding can reduce the redundancy of continuous frames and concisely express the entire video. However, how to develop a key frame extraction algorithm that can automatically extract a few frames with a low reconstruction error remains a challenge. In this paper, we propose a novel model of structured sparse-coding-based key frame extraction, wherein a nonconvex group log-regularizer is used with strong sparsity and a low reconstruction error. To automatically extract key frames, a decomposition scheme is designed to separate the sparse coefficient matrix by rows. The rows enforced by the nonconvex group log-regularizer become zero or nonzero, leading to the learning of the structured sparse coefficient matrix. To solve the nonconvex problems due to the log-regularizer, the difference of convex algorithm (DCA) is employed to decompose the log-regularizer into the difference of two convex functions related to the l₁ norm, which can be directly obtained through the proximal operator. Therefore, an efficient structured sparse coding algorithm with the group log-regularizer for key frame extraction is developed, which can automatically extract a few frames directly from the video to represent the entire video with a low reconstruction error. Experimental results demonstrate that the proposed algorithm can extract more accurate key frames from most SumMe videos compared to the state-of-the-art methods. Furthermore, the proposed algorithm can obtain a higher compression with a nearly 18% increase compared to sparse modeling representation selection (SMRS) and an 8% increase compared to SC-det on the VSUMM dataset.

Distributed Cooperative Learning for Discrete-Time Strict-Feedback Multi Agent Systems Over Directed Graphs

Min Wang, Haotian Shi, Cong Wang

2022, 9(10): 1831-1844. doi: 10.1109/JAS.2022.105542

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Abstract:
This paper focuses on the distributed cooperative learning (DCL) problem for a class of discrete-time strict-feedback multi-agent systems under directed graphs. Compared with the previous DCL works based on undirected graphs, two main challenges lie in that the Laplacian matrix of directed graphs is nonsymmetric, and the derived weight error systems exist n-step delays. Two novel lemmas are developed in this paper to show the exponential convergence for two kinds of linear time-varying (LTV) systems with different phenomena including the nonsymmetric Laplacian matrix and time delays. Subsequently, an adaptive neural network (NN) control scheme is proposed by establishing a directed communication graph along with n-step delays weight updating law. Then, by using two novel lemmas on the extended exponential convergence of LTV systems, estimated NN weights of all agents are verified to exponentially converge to small neighbourhoods of their common optimal values if directed communication graphs are strongly connected and balanced. The stored NN weights are reused to structure learning controllers for the improved control performance of similar control tasks by the “mod” function and proper time series. A simulation comparison is shown to demonstrate the validity of the proposed DCL method.

An Adaptive Padding Correlation Filter With Group Feature Fusion for Robust Visual Tracking

Zihang Feng, Liping Yan, Yuanqing Xia, Bo Xiao

2022, 9(10): 1845-1860. doi: 10.1109/JAS.2022.105878

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Abstract:
In recent visual tracking research, correlation filter (CF) based trackers become popular because of their high speed and considerable accuracy. Previous methods mainly work on the extension of features and the solution of the boundary effect to learn a better correlation filter. However, the related studies are insufficient. By exploring the potential of trackers in these two aspects, a novel adaptive padding correlation filter (APCF) with feature group fusion is proposed for robust visual tracking in this paper based on the popular context-aware tracking framework. In the tracker, three feature groups are fused by use of the weighted sum of the normalized response maps, to alleviate the risk of drift caused by the extreme change of single feature. Moreover, to improve the adaptive ability of padding for the filter training of different object shapes, the best padding is selected from the preset pool according to tracking precision over the whole video, where tracking precision is predicted according to the prediction model trained by use of the sequence features of the first several frames. The sequence features include three traditional features and eight newly constructed features. Extensive experiments demonstrate that the proposed tracker is superior to most state-of-the-art correlation filter based trackers and has a stable improvement compared to the basic trackers.

RANSACs for 3D Rigid Registration: A Comparative Evaluation

Jiaqi Yang, Zhiqiang Huang, Siwen Quan, Zhiguo Cao, Yanning Zhang

2022, 9(10): 1861-1878. doi: 10.1109/JAS.2022.105500

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Abstract:
Estimating an accurate six-degree-of-freedom (6-DoF) pose from correspondences with outliers remains a critical issue to 3D rigid registration. Random sample consensus (RANSAC) and its variants are popular solutions to this problem. Although there have been a number of RANSAC-fashion estimators, two issues remain unsolved. First, it is unclear which estimator is more appropriate to a particular application. Second, the impacts of different sampling strategies, hypothesis generation methods, hypothesis evaluation metrics, and stop criteria on the overall estimators remain ambiguous. This work fills these gaps by first considering six existing RANSAC-fashion methods and then proposing eight variants for a comprehensive evaluation. The objective is to thoroughly compare estimators in the RANSAC family, and evaluate the effects of each key stage on the eventual 6-DoF pose estimation performance. Experiments have been carried out on four standard datasets with different application scenarios, data modalities, and nuisances. They provide us with input correspondence sets with a variety of inlier ratios, spatial distributions, and scales. Based on the experimental results, we summarize remarkable outcomes and valuable findings, so as to give practical instructions to real-world applications, and highlight current bottlenecks and potential solutions in this research realm.