Recent approaches for few-shot 3D point cloud semantic segmentation typically require a two-stage learning process, i.e., a pre-training stage followed by a few-shot training stage. While effective, these methods face overreliance on pre-training, which hinders model flexibility and adaptability. Some models tried to avoid pre-training yet failed to capture ample information. In addition, current approaches focus on visual information in the support set and neglect or do not fully exploit other useful data, such as textual annotations. This inadequate utilization of support information impairs the performance of the model and restricts its zero-shot ability. To address these limitations, we present a novel pre-training-free network, named Efficient Point Cloud Semantic Segmentation for Few- and Zero-shot scenarios. Our EPSegFZ incorporates three key components. A Prototype-Enhanced Registers Attention (ProERA) module and a Dual Relative Positional Encoding (DRPE)-based cross-attention mechanism for improved feature extraction and accurate query-prototype correspondence construction without pre-training. A Language-Guided Prototype Embedding (LGPE) module that effectively leverages textual information from the support set to improve few-shot performance and enable zero-shot inference.Extensive experiments show that our method outperforms the state-of-the-art method by 5.68% and 3.82% on the S3DIS and ScanNet benchmarks, respectively.

Unsupervised domain adaptation (UDA) is a machine learning approach designed to minimize reliance on labeled data by aligning features between a labeled source domain and an unlabeled target domain, thereby reducing feature discrepancies, which is efficient for multivariate time series (MTS) prediction. However, most MTS UDA methods focus solely on aligning intra-series temporal features, overlooking the valuable information in inter-series dependencies. Research has highlighted that analyzing decomposed frequency dependencies in time series can reveal significant trends, noise patterns, and intricate temporal details. To address these unexplored frequency dependencies, we introduce the Frequency Graph Discovery Module (FGD), which uncovers and aligns shared frequency information and correlations across domains. Additionally, we propose a Frequency-Contextual Contrastive Learning (FCCL) framework to better capture and align frequency-contextual representations in multivariate time series, ensuring the extraction of label-invariant information for prediction. Furthermore, considering existing models overlooking the valuable and abundant information outside source and target dataset, we enhance the MTS UDA prediction model with a Language-guided Adversary Alignment (LAA) module, which leverages the advancement and capabilities of Large Language Models (LLMs) to get text-encoded labeled embeddings and align the classification features, thereby improving prediction accuracy. Our model achieves state-of-the-art results on three public multivariate time-series datasets for unsupervised domain adaptation, as demonstrated by empirical evidence.

As one of the most fundamental control modes in robotics, force/torque (F/T) control plays an essential role in a wide range of applications. However, classical F/T control fails to offer effective means to regulate the convergence sequence of the controlled states, which is beneficial in many real-world tasks, e.g., unknown surface contact, where the force should preferably converge later than the alignment angles to ensure sufficient contact and avoid dangerous misalignment. In this work, a novel nested fast terminal sliding mode control approach is proposed. This approach establishes a hierarchical structure for the controlled states, such that the Lyapunov stabilities of controlled states can be achieved in both a sequential and a time-synchronized manner within finite time, which is named as ‘Sequen-Sync’. Extensive experiments are conducted for various tasks in two different environments. The experimental results show that the proposed approach successfully achieves Sequen-Sync stability, which leads to improved contact quality and enhanced safety.

Impedance control can be achieved within a model predictive control (MPC) framework for optimization and constraint compliance. However, user-defined or optimization-derived impedance models can be too conservative to achieve a timely convergence, or too aggressive to ensure safety. To address this, an MPC-based impedance control framework with learning-based tuning for predefined-time (PdT) convergence is proposed. On the low level, the framework dynamically selects between a task-oriented and a safety-oriented impedance model based on real-time interaction force modeling and safety assessments, ensuring optimal performance and maintaining safety while interacting with unknown and complex environments. On the high level, the framework achieves PdT convergence via reinforcement learning for meta-parameter tuning, allowing users to specify the desired convergence time upper bound. Lastly, the superiority of the proposed framework is validated on interaction safety and PdT convergence via experiments.

Recent 6D pose estimation methods demonstrate notable performance but still face some practical limitations. For instance, many of them rely heavily on sensor depth, which may fail with challenging surface conditions, such as transparent or highly reflective materials. In the meantime, RGB-based solutions provide less robust matching performance in low-light and texture-less scenes due to the lack of geometry information. Motivated by these, we propose **SingRef6D**, a lightweight pipeline requiring only a **single RGB** image as a reference, eliminating the need for costly depth sensors, multi-view image acquisition, or training view synthesis models and neural fields. This enables SingRef6D to remain robust and capable even under resource-limited settings where depth or dense templates are unavailable. Our framework incorporates two key innovations. First, we propose a token-scaler-based fine-tuning mechanism with a novel optimization loss on top of Depth-Anything v2 to enhance its ability to predict accurate depth, even for challenging surfaces. Our results show a 14.41% improvement (in $\delta_{1.05}$) on REAL275 depth prediction compared to Depth-Anything v2 (with fine-tuned head). Second, benefiting from depth availability, we introduce a depth-aware matching process that effectively integrates spatial relationships within LoFTR, enabling our system to handle matching for challenging materials and lighting conditions. Evaluations of pose estimation on the REAL275, ClearPose, and Toyota-Light datasets show that our approach surpasses state-of-the-art methods, achieving a 6.1% improvement in average recall.

Three-dimensional point cloud semantic segmentation is a fundamental task in computer vision. As the fully supervised approaches suffer from the generalization issue with limited data, few-shot point cloud segmentation models have been proposed to address the flexible adaptation. Nevertheless, due to the class-agnostic nature of the few-shot pretraining, its pretrained feature extractor is hard to capture the class-related intrinsic and abstract information. Therefore, we introduce the new concept of shallow and deep similarities and propose a shallow-deep similarity learning network (SDSimPoint) that aims to learn both shallow (superficial geometry, color, etc.) and deep similarities (intrinsic context and semantics, etc.) between the support and query samples, thereby boosting the performance. Moreover, we design a beyond-episode attention module (BEAM) to enlarge the region of the attention mechanism from a single episode to the entire dataset by utilizing the memory units, which enhances the extraction ability to better capture the shallow and deep similarities. Furthermore, our distance metric function is learnable in the proposed framework, which can better adapt to complex data distributions. Our proposed SDSimPoint consistently demonstrates substantial improvements compared to baseline approaches across various datasets in diverse few-shot point cloud semantic segmentation settings.

Visual anomaly detection is an essential component in modern industrial manufacturing. Existing studies using notions of pairwise similarity distance between a test feature and nominal features have achieved great breakthroughs. However, the absolute similarity distance lacks certain generalizations, making it challenging to extend the comparison beyond the available samples. This limitation could potentially hamper anomaly detection performance in scenarios with limited samples. This article presents a novel sparse feature representation anomaly detection (SFRAD) framework, which formulates the anomaly detection as a sparse feature representation problem; and notably proposes an anomaly score by orthogonal matching pursuit (ASOMP) as a novel detection metric. Specifically, SFRAD calculates the Gaussian kernel distance between the test feature and its sparse representation in the nominal feature space for anomaly detection. Here, the orthogonal matching pursuit (OMP) algorithm is adopted to achieve the sparse feature representation. Moreover, to construct a low-redundancy memory bank storing the basis features for sparse representation, a novel basis feature sampling (BFS) algorithm is proposed by considering both the maximum coverage and the optimum feature representation simultaneously. As a result, SFRAD incorporates both the advantages of absolute similarity and linear representation; and this enhances the generalization in low-shot scenarios. Extensive experiments on the MVTec anomaly detection (MVTec AD), Kolektor surface-defect dataset (KolektorSDD), Kolektor surface-defect dataset 2 (KolektorSDD2), MVTec logical constraints anomaly detection (MVTec LOCO AD), Visual anomaly (VISA), Modified national institute of standards and technology (MNIST), and CIFAR-10 datasets demonstrate that our proposed SFRAD outperforms the previous methods and achieves state-of-the-art unsupervised anomaly detection performance. Notably, significantly improved outcomes and results have also been achieved on low-shot anomaly detection. Code is available at https://github.com/fanghuisky/SFRAD.

For various typical cases and situations where the formulation results in an optimal control problem, the linear quadratic regulator (LQR) approach and its variants continue to be highly attractive. In certain scenarios, it can happen that some prescribed structural constraints on the gain matrix would arise. Consequently then, the algebraic Riccati equation (ARE) is no longer applicable in a straightforward way to obtain the optimal solution. This work presents a rather effective alternative optimization approach based on gradient projection. The utilized gradient is obtained through a data-driven methodology, and then projected onto applicable constrained hyperplanes. Essentially, this projection gradient determines a direction of progression and computation for the gain matrix update with a decreasing functional cost; and then the gain matrix is further refined in an iterative framework. With this formulation, a data-driven optimization algorithm is summarized for controller synthesis with structural constraints. This data-driven approach has the key advantage that it avoids the necessity of precise modeling which is always required in the classical model-based counterpart; and thus the approach can additionally accommodate various model uncertainties. Illustrative examples are also provided in the work to validate the theoretical results.

High-performance learning-based control for the typical safety-critical autonomous vehicles invariably requires that the full-state variables are constrained within the safety region even during the learning process. To solve this technically critical and challenging problem, this work proposes an adaptive safe reinforcement learning (RL) algorithm that invokes innovative safety-related RL methods with the consideration of constraining the full-state variables within the safety region with adaptation. These are developed toward assuring the attainment of the specified requirements on the full-state variables with two notable aspects. First, thus, an appropriately optimized backstepping technique and the asymmetric barrier Lyapunov function (BLF) methodology are used to establish the safe learning framework to ensure system full-state constraints requirements. More specifically, each subsystem's control and partial derivative of the value function are decomposed with asymmetric BLF-related items and an independent learning part. Then, the independent learning part is updated to solve the Hamilton-Jacobi-Bellman equation through an adaptive learning implementation to attain the desired performance in system control. Second, with further Lyapunov-based analysis, it is demonstrated that safety performance is effectively doubly assured via a methodology of a constrained adaptation algorithm during optimization (which incorporates the projection operator and can deal with the conflict between safety and optimization). Therefore, this algorithm optimizes system control and ensures that the full set of state variables involved is always constrained within the safety region during the whole learning process. Comparison simulations and ablation studies are carried out on motion control problems for autonomous vehicles, which have verified superior performance with smaller variance and better convergence performance under uncertain circumstances. The effectiveness of the safe performance of overall system control with the proposed method accordingly has been verified.

This work investigates a game-theoretic optimization approach towards robust control of uncertain dynamical systems with state and input constraints. The uncertainty involved is possibly rapidly time-varying but bounded within a prescribed fuzzy set. For this, the associated fuzzy dynamical system is appropriately established and constructed based on fuzzy set theory. To cope with the bounded state and input constraints, a novel state-and-input diffeomorphism technique is proposed, where a transformed system is formulated such that the prescribed inequality constraints are innovatively merged into the stabilization and trajectory tracking problems. Furthermore, a diffeomorphism-based robust control (DBRC) strategy is developed to ensure the uniform boundedness (UB) and uniform ultimate boundedness (UUB) of the transformed system. Under this proposed control architecture, the constraint satisfaction of the original system is thus always analytically ensured based on the rigorous properties of the diffeomorphism technique. The resulting control parameter optimization problem then has to take into account the multiple considerations (and compromise) amongst the factors of the steady-state performance; the finite convergence time; and the control effort. For this, a two-player Nash game is formulated and solved in an effective manner. The Nash equilibrium is obtained and the existence of the solution is also proved theoretically. With this methodology, and with the resulting attainment of the desired Nash equilibrium, the attendant outcome of superior system performance is achieved. Finally, numerical simulations on a steer-by-wire (SBW) system demonstrate the effectiveness of the proposed approach.

A home energy management system exploits the time-varying electricity tariff and renewable energy profiles to lower residents' electricity bills via wise scheduling of various domestic appliances. This study targets the rather typical case of a general household with solar panels. All four classes of loads are considered, while many existing studies only investigate a restricted subset. Considering the high stochasticity in real-time pricing and solar power generation, we propose an online approach in a hybrid semidecentralized framework, where each shiftable load is controlled by a deep neural network (DNN), and all adjustable loads are coordinated together by fast online optimization. We train each DNN via efficient and effective imitation learning (IL) instead of popular reinforcement learning (RL). This framework allows adjustable loads to react properly to possibly poor actions of shiftable loads via online one-step optimization to alleviate their adverse impact. Numerical experiments with real-world data show that, compared with RL, our approach can reduce the training time significantly, while its execution time is only slightly affected. Moreover, our approach outperforms the traditional day-ahead optimization method and the fully decentralized multiagent RL and multiagent IL methods by a wide margin, attaining an average cost fairly close to the theoretical minimum.

This paper studies synchronized optimization control for the cooperatively connected autonomous vehicle platoon formulation applicable in various driving scenarios and accommodates multiple vehicles dynamically entering or exiting the platoon. More specifically, the proposed algorithm consists of bi-layered synchronized optimization that enables the ultimate optimized control to attain the synchronized convergence property, and also importantly, ensures the satisfaction of safety performance requirements. The first layer of the proposed approach involves formulating the platoon dynamics and ensuring that the platoon operates within safe boundaries while optimizing its overall performance. To achieve this, the prescribed performance control is utilized to ensure that the state-variables remain within a predefined region throughout the synchronized optimization process. In the second layer, the control optimization takes into account the vehicle dynamics and actuators of either heterogeneous or homogeneous individual vehicles, improving performance and coordination within the platoon. In each optimization layer, the optimized backstepping is utilized, and the norm-normalized sign function is appropriately incorporated with the decomposition design to establish the learning framework with the outcome that attains the synchronized properties simultaneously. The adaptive dynamic programming and gradient-constrained method are utilized in the learning design to iteratively optimize system control while keeping the learning parts within the admissible policy region. Importantly, it is rigorously shown that this particular development and methodology attains the noteworthy time-synchronized stability property and outcome that all vehicle agents arrive at the desired relative position at the same time with synchronized convergence. Additionally, it is also shown that the methodology of our specific algorithmic strategy significantly also attains the desired outcomes of “string stability” (jointly with the above-mentioned desired outcomes of “time-synchronized stability”). To evaluate its effectiveness, comparative studies with different methods are carried out to showcase the significantly better desired outcomes attained with this methodology of synchronized optimization. Further evaluations in scenarios involving dynamic entry and exit of multiple vehicles demonstrate the corresponding capability and effectiveness in achieving the desired objectives.

Guaranteed safety and performance under various circumstances remain technically critical and practically challenging for the wide deployment of autonomous vehicles. Safety-critical systems in general, require safe performance even during the reinforcement learning (RL) period. To address this issue, a Barrier Lyapunov Function-based safe RL (BLF-SRL) algorithm is proposed here for the formulated nonlinear system in strict-feedback form. This approach appropriately arranges and incorporates the BLF items into the optimized backstepping control method to constrain the state-variables in the designed safety region during learning. Wherein, thus, the optimal virtual/actual control in every backstepping subsystem is decomposed with BLF items and also with an adaptive uncertain item to be learned, which achieves safe exploration during the learning process. Then, the principle of Bellman optimality of continuous-time Hamilton-Jacobi-Bellman equation in every backstepping subsystem is satisfied with independently approximated actor and critic under the framework of actor-critic through the designed iterative updating. Eventually, the overall system control is optimized with the proposed BLF-SRL method. It is furthermore noteworthy that the variance of the attained control performance under uncertainty is also reduced with the proposed method. The effectiveness of the proposed method is verified with two motion control problems for autonomous vehicles through appropriate comparison simulations.

This paper presents an effective few-shot point cloud semantic segmentation approach for real-world applications. Existing few-shot segmentation methods on point cloud heavily rely on the fully-supervised pretrain with large annotated datasets, which causes the learned feature extraction bias to those pretrained classes. However, as the purpose of few-shot learning is to handle unknown/unseen classes, such class-specific feature extraction in pretrain is not ideal to generalize into new classes for few-shot learning. Moreover, point cloud datasets hardly have a large number of classes due to the annotation difficulty. To address these issues, we propose a contrastive self-supervision framework for few-shot learning pretrain, which aims to eliminate the feature extraction bias through class-agnostic contrastive supervision. Specifically, we implement a novel contrastive learning approach with a learnable augmentor for a 3D point cloud to achieve point-wise differentiation, so that to enhance the pretrain with managed overfitting through the self-supervision. Furthermore, we develop a multi-resolution attention module using both the nearest and farthest points to extract the local and global point information more effectively, and a center-concentrated multi-prototype is adopted to mitigate the intra-class sparsity. Comprehensive experiments are conducted to evaluate the proposed approach, which shows our approach achieves state-of-the-art performance. Moreover, a case study on practical CAM/CAD segmentation is presented to demonstrate the effectiveness of our approach for real-world applications.

Data-driven models for predicting the Remaining Useful Lifetime (RUL) have gained popularity due to their efficiency to enhance industrial security and reduce economic losses. Recently, there has been a notable rise in the research of transformer-based models for RUL prediction. While transformer-based models have shown significant improvements over previous LSTM-based and CNN-based models, we have raised concerns regarding high computational complexity, in-effective training with low data, no sensitivity to the order of the time series, and permutation invariant on its application to RUL prediction. The persistent issue of data scarcity and the importance of capturing the temporal relations in RUL prediction further question the suitability of transformer-based models. Considering these, We propose a simple non-transformer model, Compressed Temporal and Compressed Spatial (CTCS) Attention, which is efficient and lightweight, to capture both temporal and spatial information with the incorporation of pre- and post-positional encodings. Additionally, we introduce an Augmentation Fusion Module (AFM) to enhance the comprehension ability of the invariant characteristics of the data. The proposed methodology is evaluated on the NASA Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset and comprehensive experiments show that our proposed method not only surpasses other methods but outperforms the transformer-based model while requiring significantly fewer Floating-point operations (FLOPs), up to 32 times less.

Modern CAM/CAD workflows can benefit greatly from precise 3D semantic segmentation, which contributes to reducing the defect rate of work-pieces manufactured by computer-controlled CNCs and ultimately enhancing work efficiency. The majority of existing approaches for 3D object segmentation heavily rely on fully-supervised learning, where AI models are trained using extensive datasets with annotations. However, these models often exhibit unsatisfactory performance when confronted with scenarios characterized by high mixture but low volume. This means they struggle to accurately segment novel classes that were not encountered during training. In this paper, we introduce and formulate a noteworthy approach based on few-shot learning, which incorporates Sequential Dual Attention (SDA) and feature enhancement techniques. Our method aims to achieve effective semantic segmentation of point clouds in the context of CAM/CAD workflows. Unlike other few-shot models that solely adopt self-attention or lack attention, our SDA captures features at both the channel and spatial levels. Additionally, we design a non-parametric feature enhancement block to enhance the recognizability of each class in the feature space. Our proposed approach consistently demonstrates substantial enhancements in various few-shot point cloud semantic segmentation scenarios across two datasets, outperforming baseline methods.

Synthetic Aperture Radar (SAR) image segmentation stands as a formidable research frontier within the domain of SAR image interpretation. The fully convolutional network (FCN) methods have recently brought remarkable improvements in SAR image segmentation. Nevertheless, these methods do not utilize the peculiarities of SAR images, leading to suboptimal segmentation accuracy. To address this issue, we rethink SAR image segmentation in terms of sequential information of transformers and cross-modal features. We first discuss the peculiarities of SAR images and extract the mean and texture features utilized as auxiliary features. The extraction of auxiliary features helps unearth the distinctive information in the SAR images. Afterward, a feature-enhanced FCN with the transformer encoder structure, termed FE-FCN, which can be extracted to context-level and pixel-level features. In FE-FCN, the features of a single-mode encoder are aligned and inserted into the model to explore the potential correspondence between modes. We also employ long skip connections to share each modality’s distinguishing and particular features. Finally, we present the connection-enhanced conditional random field (CE-CRF) to capture the connection information of the image pixels. Since the CE-CRF utilizes the auxiliary features to enhance the reliability of the connection information, the segmentation results of FE-FCN are further optimized. Comparative experiments conducted on the Fangchenggang (FCG), Pucheng (PC), and Gaofen (GF) SAR datasets. Our method demonstrates superior segmentation accuracy compared to other conventional image segmentation methods, as confirmed by the experimental results.

In the area of autonomous driving, it typically brings great difficulty in solving the motion planning problem since the vehicle model is nonlinear and the driving scenarios are complex. Particularly, most of the existing methods cannot be generalized to dynamically changing scenarios with varying surrounding vehicles. To address this problem, this development here investigates the framework of integrated decision and control. As part of the modules, static path planning determines the reference candidates ahead, and then the optimal path-tracking controller realizes the specific autonomous driving task. An innovative and effective constrained finite-horizon approximate dynamic programming (ADP) algorithm is herein presented to generate the desired control policy for effective path tracking. With the generalized policy neural network that maps from the state to the control input, the proposed algorithm preserves the high effectiveness for the motion planning problem towards changing driving environments with varying surrounding vehicles. Moreover, the algorithm attains the noteworthy advantage of alleviating the typically heavy computational loads with the mode of offline training and online execution. As a result of the utilization of multi-layer neural networks in conjunction with the actor-critic framework, the constrained ADP method is capable of handling complex and multidimensional scenarios. Finally, various simulations have been carried out to show that the constrained ADP algorithm is effective.

Trajectory planning is one of the indispensable and critical components in robotics and autonomous systems. As an efficient indirect method to deal with the nonlinear system dynamics in trajectory planning tasks over the unconstrained state and control space, the iterative linear quadratic regulator (iLQR) has demonstrated noteworthy outcomes. In this article, a local-learning-enabled constrained iLQR algorithm is herein presented for trajectory planning based on hybrid dynamic optimization and machine learning. Rather importantly, this algorithm attains the key advantage of circumventing the requirement of system identification, and the trajectory planning task is achieved with a simultaneous refinement of the optimal policy and the neural network system in an iterative framework. The neural network can be designed to represent the local system model with a simple architecture, and thus it leads to a sample-efficient training pipeline. In addition, in this learning paradigm, the constraints of the general form that are typically encountered in trajectory planning tasks are preserved. Several illustrative examples on trajectory planning are scheduled as part of the test itinerary to demonstrate the effectiveness and significance of this work.

This article involves the innovative development of a symmetric Gauss–Seidel ADMM algorithm to solve the$\mathcal {H}_{\infty }$guaranteed cost control problem. In the presence of parametric uncertainties, the$\mathcal {H}_{\infty }$guaranteed cost control problem generally leads to the large-scale optimization. This is due to the exponential growth of the number of the extreme systems involved with respect to the number of parametric uncertainties. In this work, through a variant of the Youla–Kucera parameterization, the stabilizing controllers are parameterized in a convex set; yielding the outcome that the$\mathcal {H}_{\infty }$guaranteed cost control problem is converted to a convex optimization problem. Based on an appropriate reformulation using the Schur complement, it then renders possible the use of the ADMM algorithm with symmetric Gauss–Seidel backward and forward sweeps. Significantly, this approach alleviates the often-times prohibitively heavy computational burden typical in many$\mathcal H_{\infty }$optimization problems while exhibiting good convergence guarantees, which is particularly essential for the related large-scale optimization procedures involved. With this approach, the desired robust stability is ensured, and the disturbance attenuation is maintained at the minimum level in the presence of parametric uncertainties. Rather importantly too, with the attained effectiveness, the methodology thus evidently possesses extensive applicability in various important controller synthesis problems, such as decentralized control, sparse control, and output feedback control problems.

This article investigates the design of a robust fixed-order controller for single-input–single-output (SISO) polytopic systems with interval uncertainties, with the aim that the closed-loop stability is appropriately ensured and the performance specifications on sensitivity shaping are conformed in a specific finite frequency range. Utilizing the notion of generalized common Lyapunov strictly positive realness (CL-SPRness), the equivalence between strictly positive realness (SPRness) and strictly bounded realness (SBRness) is established; and then, the specifications on robust stability and performance are transformed into the SPRness of newly constructed systems and further characterized in the framework of linear matrix inequality (LMI) conditions. The proposed methodology avoids the tedious yet mandatory evaluations of the specifications on all vertices of the uncertain polytopic system in an explicit form. Instead, solving five LMIs exclusively suffices for ensuring the robust stability and performance regardless of the number of vertices, and thus, the typically heavy computational burden is considerably alleviated. It is also noteworthy that the proposed methodology additionally provides the necessary and sufficient conditions for this robust controller design with the consideration of a prescribed finite frequency range, and therefore, significantly less conservatism is attained in the system performance.

Incremental few-shot learning is highly expected for practical robotics applications. On one hand, robot is desired to learn new tasks quickly and flexibly using only few annotated training samples; on the other hand, such new additional tasks should be learned in a continuous and incremental manner without forgetting the previous learned knowledge dramatically. In this work, we propose a novel Class-Incremental Few- Shot Object Detection (CI-FSOD) framework that enables deep object detection network to perform effective continual learning from just few-shot samples without re-accessing the previous training data. We achieve this by equipping the widely-used Faster-RCNN detector with three elegant components. Firstly, to best preserve performance on the pre-trained base classes, we propose a novel Dual-Embedding-Space (DES) architecture which decouples the representation learning of base and novel categories into different spaces. Secondly, to mitigate the catastrophic forgetting on the accumulated novel classes, we propose a Sequential Model Fusion (SMF) method, which is able to achieve long-term memory without additional storage cost. Thirdly, to promote inter-task class separation in feature space, we propose a novel regularization technique that extends the classification boundary further away from the previous classes to avoid misclassification. Overall, our framework is simple yet effective and outperforms the previous SOTA with a significant margin of 2.4 points in AP performance.

Prediction of Remaining Useful Lifetime (RUL) in the modern manufacturing and automation workplace for machines and tools is essential in Industry 4.0. This is clearly evident as continuous tool wear, or worse, sudden machine breakdown, will lead to various manufacturing failures which would clearly cause economic loss. With the availability of deep learning approaches, the great potential and prospect of utilizing these for RUL prediction have resulted in several models which are designed (for RUL prediction) driven by operation data of manufacturing machines. Current efforts in these which are based on fully-supervised models heavily rely on the data labeled with their RULs. However, in these cases, the required RUL prediction data (i.e. the annotated and labeled data from faulty and/or degraded machines) can only be obtained after the machine break-down occurs. The scarcity of broken machines in the modern manufacturing and automation workplace in real- world situations increases the difficulty of getting such sufficient annotated and labeled data. In contrast, the data from healthy machines (and which are currently in operation) is much easier to be collected. Noting this challenge and the potential for improved effectiveness and applicability, we thus propose (and also fully develop) a method based on the idea of masked autoencoders which will utilize unlabeled data to do self-supervision. In thus the work here, a noteworthy masked self-supervised learning approach is developed and utilized; and this is designed to seek to build a deep learning model for RUL prediction by utilizing unlabeled data. The experiments to verify the effectiveness of this development are implemented on the C-MAPSS datasets (which is collected from the data from the NASA turbofan engine). The results rather clearly show that our development and approach here performs better, in both accuracy and effectiveness, for RUL prediction when compared with approaches utilizing a fully- supervised model.

3D part segmentation is an essential step in advanced CAM/CAD workflow. Precise 3D segmentation contributes to lower defective rate of work-pieces produced by the manufacturing equipment (such as computer controlled CNCs), thereby improving work efficiency and attaining the attendant economic benefits. A large class of existing works on 3D model segmentation are mostly based on fully-supervised learning, which trains the AI models with large, annotated datasets. However, the disadvantage is that the resulting models from the fully-supervised learning methodology are highly reliant on the completeness (or otherwise) of the available dataset, and its generalization ability is relatively poor to new unknown/unseen segmentation types (i.e., further additional so-called novel classes). In this work, we propose and develop a noteworthy few-shot learning-based approach for effective part segmentation in CAM/CAD; and this is designed to significantly enhance its generalization ability, and our development also aims to flexibly adapt to new segmentation tasks by using only relatively rather few samples. As a result, it not only reduces the requirements for the usually unattainable and exhaustive completeness of supervision datasets, but also improves the flexibility for real-world applications. In the development, drawing inspiration from the pertinent and interesting work described in the open literature as the attMPTI network, we propose and develop a multi-prototype approach (with self-attention mechanics) for few-shot point cloud part segmentation. As further improvement and innovation, we additionally adopt the transform net and the center loss block in the network. These characteristics serve to improve the comprehension for 3D features of the various possible instances of the whole work-piece and ensure the close distribution of the same class in feature space. Moreover, our approach stores data in the point cloud format that reduces space consumption, and which also makes the various procedures involved have significantly easier read and edit access (thus improving efficiency and effectiveness and lowering costs).

This study investigates the infinite-horizon optimal control (IHOC) problem for switched Boolean control networks with an average cost criterion. A primary challenge of this problem is the prohibitively high computational cost when dealing with large-scale networks. We attempt to develop a more efficient approach from a novel graph-theoretical perspective. First, a weighted directed graph structure called the optimal state transition graph (OSTG) is established, whose edges encode the optimal action for each admissible state transition between states reachable from a given initial state subject to various constraints. Then, we reduce the IHOC problem into a minimum-mean cycle (MMC) problem in the OSTG. Finally, we develop an algorithm that can quickly find a particular MMC by resorting to Karp's algorithm in the graph theory and construct an optimal switching control law based on state feedback. The time complexity analysis shows that our algorithm, albeit still running in exponential time, can outperform all the existing methods in terms of time efficiency. A 16-state-3-input signaling network in leukemia is used as a benchmark to test its effectiveness. Results show that the proposed graph-theoretical approach is much more computationally efficient and can reduce the running time dramatically: it runs hundreds or even thousands of times faster than the existing methods. The Python implementation of the algorithm is available at https://github.com/ShuhuaGao/sbcn_mmc.

Recent advances in high-throughput single-cell technologies provide new opportunities for computational modeling of gene regulatory networks (GRNs) with an unprecedented amount of gene expression data. Current studies on the Boolean network (BN) modeling of GRNs mostly depend on bulk time-series data and focus on the synchronous update scheme due to its computational simplicity and tractability. However, such synchrony is a strong and rarely biologically realistic assumption. In this study, we adopt the asynchronous update scheme instead and propose a novel framework called SgpNet to infer asynchronous BNs from single-cell data by formulating it into a multiobjective optimization problem. SgpNet aims to find BNs that can match the asynchronous state transition graph (STG) extracted from single-cell data and retain the sparsity of GRNs. To search the huge solution space efficiently, we encode each Boolean function as a tree in genetic programming and evolve all functions of a network simultaneously via cooperative coevolution. Besides, we develop a regulator preselection strategy in view of GRN sparsity to further enhance learning efficiency. An error threshold estimation heuristic is also proposed to ease tedious parameter tuning. SgpNet is compared with the state-of-the-art method on both synthetic data and experimental single-cell data. Results show that SgpNet achieves comparable inference accuracy, while it has far fewer parameters and eliminates artificial restrictions on the Boolean function structures. Furthermore, SgpNet can potentially scale to large networks via straightforward parallelization on multiple cores.

This article investigates the trajectory tracking problem of underactuated mechanical systems (UMSs) with companion nonholonomic servo constraints and uncertainties. For such motion tasks, the existing approaches in the literature attempt unrealistically to furnish a reliable closed-form solution, rendering it difficult to have high-quality tracking performance with theoretical support. In addition, the uncertainties typically pose substantial difficulty in the controller synthesis. Here, by invoking the methodology of fuzzy sets, the uncertainties in the UMSs are elegantly represented; and with this, the formulation becomes such that a closer link between the uncertain dynamical model of the UMSs and the real world is established. The reference trajectories are regarded appropriately as servo constraints, and subsequently an adaptive robust controller is designed to accomplish the trajectory tracking task from a specific viewpoint of servo constraint tracking. As supported by rigorous proofs, the closed-form solution to the proposed controller is obtained with guaranteed Lyapunov stability. Leveraging on the closed-form solution, the global optimizer to the controller gain parameter can be determined, which is shown to exhibit several important properties including existence and uniqueness. Finally, a numerical example is presented to demonstrate the effectiveness of the designed method.

In the context of autonomous driving, the iterative linear quadratic regulator (iLQR) is known to be an efficient approach to deal with the nonlinear vehicle model in motion planning problems. Particularly, the constrained iLQR algorithm has shown noteworthy advantageous outcomes of computation efficiency in achieving motion planning tasks under general constraints of different types. However, the constrained iLQR methodology requires a feasible trajectory at the first iteration as a prerequisite when the logarithmic barrier function is used. Also, the methodology leaves open the possibility for incorporation of fast, efficient, and effective optimization methods (i.e., fast-solvers) to further speed up the optimization process such that the requirements of real-time implementation can be successfully fulfilled. In this paper, a well-defined and commonly-encountered motion planning problem is formulated under nonlinear vehicle dynamics and various constraints, and the alternating direction method of multipliers (ADMM) is utilized to determine the optimal control actions leveraging the iLQR. With this development, the approach is able to circumvent the feasibility requirement of the trajectory at the first iteration. An illustrative example of motion planning for autonomous vehicles is then investigated with different driving scenarios taken into consideration, and a noteworthy achievement of high computation efficiency is attained with the proposed development. Comparing with the constrained iLQR algorithm based on the logarithmic barrier function, our proposed method reduces the average computation time by 31.93%, 38.52%, and 44.57% in the three scenarios; compared with the optimization solver IPOPT, our proposed method reduces the average computation time by 46.02%, 53.26%, and 88.43% in the three scenarios. As a result, real-time computation and implementation can be realized through our proposed framework, and thus it provides additional safety to the on-road driving tasks.

This paper investigates the cooperative planning and control problem for multiple connected autonomous vehicles (CAVs) in different scenarios. In the existing literature, most of the methods suffer from significant problems in computational efficiency. Furthermore, as the optimization problem is nonlinear and nonconvex, it typically poses great difficulty in determining the optimal solution. To address this issue, this work proposes a novel and completely parallel computation framework by leveraging the alternating direction method of multipliers (ADMM). The nonlinear and nonconvex optimization problem in the autonomous driving problem can be divided into two manageable sub-problems; and the resulting sub-problems can be solved by using effective optimization methods in a parallel framework. Here, the differential dynamic programming (DDP) algorithm is capable of addressing the nonlinearity of the system dynamics rather effectively; and the nonconvex coupling constraints with small dimensions can be resolved by invoking the notion of semi-definite relaxation (SDR), which can also be solved in a very short time. Due to the parallel computation and efficient relaxation of nonconvex constraints, our proposed approach effectively realizes real-time implementation; and thus extra assurance of driving safety is provided. In addition, two transportation scenarios for multiple CAVs are used to illustrate the effectiveness and efficiency of the proposed method.

This article investigates the finite-horizon optimal control (FHOC) problem of Boolean control networks (BCNs) from a graph theory perspective. We first formulate two general problems to unify various special cases studied in the literature: 1) the horizon length is a priori fixed and 2) the horizon length is unspecified but finite for given destination states. Notably, both problems can incorporate time-variant costs, which are rarely considered in existing work, and a variety of constraints. The existence of an optimal control sequence is analyzed under mild assumptions. Motivated by BCNs' finite state space and control space, we approach the two general problems intuitively and efficiently under a graph-theoretical framework. A weighted state transition graph and its time-expanded variants are developed, and the equivalence between the FHOC problem and the shortest-path (SP) problem in specific graphs is established rigorously. Two algorithms are developed to find the SP and construct the optimal control sequence for the two problems with reduced computational complexity, though technically, a classical SP algorithm in graph theory is sufficient for all problems. Compared with existing algebraic methods, our graph-theoretical approach can achieve state-of-the-art time efficiency while targeting the most general problems. Furthermore, our approach is the first one capable of solving Problem 2) with time-variant costs. Finally, a genetic network in the bacterium E. coli and a signaling network involved in human leukemia are used to validate the effectiveness of our approach. The results of two common tasks for both networks show that our approach can dramatically reduce the running time. Python implementation of our algorithms is available at GitHub https://github.com/ShuhuaGao/FHOC.

Previous research on finite-time control focuses on forcing a system state (vector) to converge within a certain time moment, regardless of how each state element converges. In the present work, we introduce a control problem with unique finite/fixed-time stability considerations, namely time-synchronized stability (TSS), whereat the same time, all the system state elements converge to the origin, and fixed-TSS, where the upper bound of the synchronized settling time is invariant with any initial state. Accordingly, sufficient conditions for (fixed-) TSS are presented. On the basis of these formulations of the time-synchronized convergence property, the classical sign function, and also anorm-normalized sign function, are first revisited. Then in terms of this notion of TSS, we investigate their differences with applications in control system design for first-order systems (to illustrate the key concepts and outcomes), paying special attention to their convergence performance. It is found that while both these sign functions contribute to system stability, nevertheless an important result can be drawn that norm-normalized sign functions help a system to additionally achieve TSS. Furthermore, we propose a fixed-time-synchronized sliding-mode controller for second-order systems; and we also consider the important related matters of singularity avoidance there. Finally, numerical simulations are conducted to present the (fixed-) time-synchronized features attained; and further explorations of the merits of the proposed (fixed-) TSS are described.

Typically, it is desirable to design a control system that is not only robustly stable in the presence of parametric uncertainties but also guarantees an adequate level of system performance. However, most of the existing methods need to take all extreme models over an uncertain domain into consideration, which then results in costly computation. Also, since these approaches attempt rather unrealistically to guarantee the system performance over a full frequency range, a conservative design is always admitted. Here, taking a specific viewpoint of robust stability and performance under a stated restricted frequency range (which is applicable in rather many real-world situations), this article provides an essential basis for the design of a fixed-order controller for a system with bounded parametric uncertainties, which avoids the tedious but necessary evaluations of the specifications on all the extreme models in an explicit manner. A Hurwitz polynomial is used in the design and the robust stability is characterized by the notion of positive realness, such that the required robust stability condition is then successfully constructed. Also, the robust performance criteria in terms of sensitivity shaping under different frequency ranges are constructed based on an approach of bounded realness analysis. Furthermore, the conditions for robust stability and performance are expressed in the framework of linear matrix inequality (LMI) constraints, and thus can be efficiently solved. Comparative simulations are provided to demonstrate the effectiveness and efficiency of the proposed approach.

We aim to tackle the challenging Few-Shot Object Detection (FSOD), where data-scarce categories are presented during the model learning. The failure modes of FasterRCNN in FSOD are investigated, and we find that the performance degradation is mainly due to the classification incapability (false positives) caused by category confusion, which motivates us to address FSOD from a novel aspect of classification refinement. Specifically, we address the intrinsic limitation from the aspects of both architectural enhancement and hard-example mining. We introduce a novel few-shot classification refinement mechanism where a decoupled Few-Shot Classification Network (FSCN) is employed to improve the final classification of a base detector. Moreover, we especially probe a commonly-overlooked but destructive issue of FSOD, i.e., the presence of distractor samples due to the incomplete annotations where images from the base set may contain novel-class objects but remain unlabelled. Retreatment solutions are developed to eliminate the incurred false positives. For FSCN training, the distractor is formulated as a semi-supervised problem, where a distractor utilization loss is proposed to make proper use of it for boosting the data-scarce classes, while a confidence-guided dataset pruning (CGDP) technique is developed to facilitate the few-shot adaptation of base detector. Experiments demonstrate that our proposed framework achieves state-of-the-art FSOD performance on public datasets, e.g., Pascal VOC and MS-COCO.

This paper works on multiple-pursuer single-evader (MPSE) problems with a fast evader, which means multiple pursuers try to capture one evader while the evader tries to escape from the encirclement. The biggest concern is that the maximum velocity of the evader is larger than all the pursuers. Some improved strategies for the evader and pursuers based on traditional algorithms are firstly provided. Then gene expression programming (GEP) is used to generate new strategies which are better than the traditional ones. This paper shows configurations of function set, terminal set, fitness, evaluation function, and other parameters used in the GEP method, which can be implemented in other cases or similar problems.

In this work, an adaptive iterative sliding mode control method is proposed for multi-axis mechatronic systems. Commonly, the multi-axis mechatronic systems are applied in high-speed and high-precision contouring tasks. For such contouring tasks, the multi-axis coordination is a main issue. As an inevitable challenge, several factors affect the multi-axis coordinate and diminish the contouring performance. Also, some special mechanical structure brings strong coupling to the system, which makes the system identification rather difficult. To solve these problems, this work proposes a learning-based totally model-free control approach for contouring tasks in application to such multi-axis motion stages. With this approach, all the coupling, disturbance, nonlinearity, and other unknown dynamics are regarded as lumped uncertainties in each axis. As a result, these uncertainties can be attenuated and compensated by the proposed controller. To analyze the contouring performance, a case study of a flexure-linked dual-drive H-gantry system is investigated to illustrate the effectiveness of the proposed method.

Collaborative motion planning for multi-agent systems is a challenging problem because of the existence of highly nonlinear and nonconvex constraints. Such difficulties also lead to inavoidable computational inefficiency, which significantly prohibits applying the existing collaborative motion planning algorithms to complex scenarios. This paper proposes a parallel computational algorithm to achieve collaborative motion planning efficiently, considering the nonlinear dynamics model and the nonconvex collision-avoidance constraints. Specifically, the alternating direction method of multipliers (ADMM) framework is elegantly incorporated to separate the large-scale cooperative nonconvex planning problem as two tractable and manageable subproblems, where the two subproblems handle the dynamics constraints and collision-free constraints, respectively. In the proposed approach, the differential dynamic programming (DDP) method is utilized to effectively solve the nonlinear subproblem with the dynamics constraints; meanwhile, the interior point (IPOPT) method is employed to address the nonconvex subproblem derived from the collision-avoidance constraints. Finally, two simulation scenarios are successfully implemented to illustrate the effectiveness of the proposed algorithm.

A distributed optimization algorithm for a decentralized control problem for uncertain systems is investigated in this paper. Based on ℋ2formulation, the optimal control problem under parameter uncertainties can be reformulated and solved in parameter space. Besides, the stabilizing controller gains of the decentralized control system with parameter uncertainties can be parameterized in a convex set; thus, the decentralized control problem can be reformulated as a conic optimization problem, which can be solved by using the symmetric Gauss-Seidel (sGS) semi-proximal augmented Lagrangian method (sPALM) efficiently. Then, a comprehensive analysis is provided to employ the sGS-sPALM to find the optimal solution of the decentralized control problem under parameter uncertainties. Robust performance and robust stability can be guaranteed using this methodology while satisfying the sparsity constraints resulting from the decentralized structure. Two examples are used to illustrate the effectiveness of the proposed method.

For a specific class of under-actuated mechanical systems, non-holonomic servo constraints and model uncertainties are usually encountered. For such systems, this paper investigates the design of an adaptive robust controller with parameter optimization. A tighter link between the fuzzy set theory and the control of UMSs is bridged appropriately. Based on the UMSs with fuzzy information, an adaptive robust control method is then designed, and an analytical solution of the control input is determined, even if the servo constraints are non-holonomic. Furthermore, a concomitant parameter in the designed controller is analyzed, and a feasible controller admitting the optimal performance can be determined by minimizing a predefined performance index, such that the deterministic system performance can be ensured to be at a satisfying level. As supported by rigorous proofs, the existence and the uniqueness of the global solution to the optimization problem are presented. Finally, a numerical experiment is implemented to demonstrate the effectiveness of the proposed control design methodology.

A two-degree-of-freedom (2-DoF) flexure-based nanopositioner is investigated for the planar scanning tasks, and a robust controller design scheme based on the convex inner approximation method is proposed. In practice, a flexure-based mechanism is usually represented by a second-order dynamic model. However, the second-order dynamic model cannot precisely fit the real system dynamics, and the model mismatch renders it difficult to achieve satisfying system performance in applications. Such a mismatch includes the parameter uncertainties caused by inaccurate model identification, different motion conditions, as well as high-order resonances. Note that if the controller is not well designed, the high-order resonances can be frequently activated, especially when the system input variation is significant. Therefore, to deal with the above impediments, a novel scheme for the robust controller design is proposed, with the variation of system input considered. In the proposed scheme, a subset of gains that can stabilize the closed-loop system is characterized elegantly via an inner approximation method considering the model uncertainties, and the formulated optimization problem regarding the determination of the controller parameters can be efficiently solved. Furthermore, the proposed scheme guarantees the performance regarding the H2-norm level and limits the H∞-norm level in a designated range. Finally, numerical optimization and comparative experiments are carried out, and the results evidently show the effectiveness of the proposed method.

Continued great efforts have been dedicated toward high-quality trajectory generation based on optimization methods; however, most of them do not suitably and effectively consider the situation with moving obstacles; and more particularly, the future position of these moving obstacles in the presence of uncertainty within some possible prescribed prediction horizon. To cater to this rather major shortcoming, this work shows how a variational Bayesian Gaussian mixture model (vBGMM) framework can be employed to predict the future trajectory of moving obstacles; and then with this methodology, a trajectory generation framework is proposed which will efficiently and effectively address trajectory generation in the presence of moving obstacles, and incorporate the presence of uncertainty within a prediction horizon. In this work, the full predictive conditional probability density function (PDF) with mean and covariance is obtained and, thus, a future trajectory with uncertainty is formulated as a collision region represented by a confidence ellipsoid. To avoid the collision region, chance constraints are imposed to restrict the collision probability, and subsequently, a nonlinear model predictive control problem is constructed with these chance constraints. It is shown that the proposed approach is able to predict the future position of the moving obstacles effectively; and, thus, based on the environmental information of the probabilistic prediction, it is also shown that the timing of collision avoidance can be earlier than the method without prediction. The tracking error and distance to obstacles of the trajectory with prediction are smaller compared with the method without prediction.

Dynamic control for robotic automation tasks is traditionally designed and optimized with a model-based approach, and the performance relies heavily upon accurate system modeling. However, modeling the true dynamics of increasingly complex robotic systems is an extremely challenging task and it often renders the automation system to operate in a non-optimal condition. Notably, many industrial robotic applications involve repetitive motions and constantly generate a large amount of motion data under the non-optimal condition. These motion data contain rich information, and therefore an intelligent automation system should be able to learn from these non-optimal motion data to drive the system to operate optimally in a data-driven manner. In this paper, we propose a learning-based controller optimization algorithm for repetitive robotic tasks. To achieve this, a multi-objective cost function is designed to take into consideration both the trajectory tracking accuracy and smoothness, and then a data-driven approach is developed to estimate the gradient and Hessian based on the motion data for optimization without relying on the dynamic model. Experiments based on a magnetically-levitated nanopositioning system are conducted to demonstrate the effectiveness and practical appeals of the proposed algorithm in repetitive robotic automation tasks.

Robotic systems are increasingly required not only to generate precise motions to complete their tasks but also to handle the interactions with the environment or human. Significantly, soft interaction brings great challenges on the force control due to the nonlinear, viscoelastic and inhomogeneous properties of the soft environment. In this paper, a robust impedance control scheme utilizing integral backstepping technology and integral terminal sliding mode control is proposed to achieve force tracking for an ultrasonic motor-actuated end-effector in a soft environment. In particular, the steady-state performance of the target impedance while in contact with soft environment is derived and analyzed with the nonlinear Hunt-Crossley model. Finally, the dynamic force tracking performance of the proposed control scheme is verified via several experiments.

This paper addresses the trajectory analysis, mission design, and control law for multiple microsatellites to cooperatively circumnavigate a host spacecraft. This cooperative circumnavigation (CCN) problem is defined to drive a group of networked microsatellites to a predefined planar ellipse concerning a host spacecraft while maintaining a geometric formation configuration. We first design several potential functions to guide the microsatellites to the given planar elliptical orbit with a proper radius. Next, the affine Laplacian matrix is introduced to characterize the desired formation shape of microsatellites. Based on the potential functions and the Laplacian matrix, a CCN control law is finally proposed. Then, the simulation results of eight microsatellites with earth-orbiting mission scenarios are given, where the natural trajectory motion is incorporated which consumes nearly zero-fuel.

Data-driven control methods for modern controller design are becoming popular recently. However, the traditional Proportional-Integral-Derivative (PID) controller is still the most widely used controller to the industrial preference. To tune the parameters of the PID controller, optimal PID tuning approaches such as solving the Riccati equation of the Linear Quadratic Regulator (LQR) provide the optimal solution. The disadvantages of the LQR are that an accurate model of the system is required, and the high-order system must be reduced to the second-order system so that the Riccati equation can be solved. In this paper, a novel data-driven method is proposed to cope with these problems. For the system which is difficult to be identified accurately, the proposed data-driven method can skip the procedure of system identification and tune the parameters of the PID controller directly with the experimental data instead of solving the Riccati equation. This data-driven tuning method also ensures that the parameters of the PID controller for the high-order system are optimized without using the reduced-order model of the system. Simulations are conducted on a tray indexing system with the second-order model and the full-order model demonstrating high applicability and accuracy of the proposed method.

Even though path planning is a well-studied problem in 2D environment, finding an optimal or near-optimal path in a complex and unknown 3D environment has great prospect, but it is hard to find the optimal path quickly. In this paper, we propose a new algorithm called Hierarchical Lazy Theta* (HLT*), which can plan the near-optimal path efficiently for real-time operation based on the heuristic-based path-finding algorithm Lazy Theta* with a hierarchical path planning approach. Path refinement, smoothing, and polishing are used to refine the path to ensure the feasibility of computed path. Its computation time and path quality are dependent on parameters, such as map size, environment complexity, sensor detection range, refinement range, computation time limits, and any restriction on planning time. Simulation experiments are used to assess the performance, and the simulation results show that HLT* algorithm is capable of planning a high-quality path in a shorter time.

This paper presents a novel content-driven associative memory (CDAM) to associate large-scale color images based on the subjects that represent the images' content. Compared to traditional associative memories, CDAM inherits their tolerance to random noise in images and possesses greater robustness against correlated noise that distorts an image's spatial contextual structure. A three-layer recurrent neural tensor network (RNTN) is designed as the network model of CDAM. Multiple salient objects detection algorithm and partial radial basis function (PRBF) kernel are proposed for subject determination and content-driven association, respectively. Convergence of the RNTN is analyzed based on the properties of PRBF kernels. Extensive comparative experiment results are provided to verify the CDAM's efficiency, robustness, and accuracy.

In this paper, we propose a method of reference adaptation for robots in physical interactions with unknown environments. A cost function is constructed to describe the interaction performance, which combines trajectory tracking error and interaction force between the robot and the environment. It is minimized by the proposed reference adaptation based on trajectory parametrization and iterative learning. An adaptive impedance control is developed to make the robot be governed by the target impedance model. Simulation and experiment studies are conducted to verify the effectiveness of the proposed method.

In this paper, we present an adaptive control of robotic manipulators with parametric uncertainties and motion constraints. Position and velocity constraints are considered and they are unified and converted into the constraint of the nominal input. An adaptive neural network control is developed to achieve trajectory tracking, while the problems of motion constraints are addressed by considering the saturation effect of the nominal input. The uniform boundedness of all closed-loop signals is verified through Lyapunov analysis. Simulation and experiment results on a 2-degree-of-freedom robotic manipulator demonstrate the effectiveness of the proposed method.

In this paper, we introduce a novel control scheme based on the social force model for robots navigating in human environments. Social proxemics potential field is constructed based on the theory of proxemics and used to generate social interaction force for design of robot motion control. A combined kinematic/dynamic control is proposed to make the robot follow the target social force model, in the presence of kinematic velocity constraints. Under the proposed framework, given a specific social convention, robot is able to generate and modify its path smoothly without violating the proxemics constraints. The validity of the proposed method is verified through experimental studies using the V-rep platform.

In this paper, a system for the wide area surveillance of general urban environments using multiple Mini-VTOL UAVs is developed. Given the information of terrain and buildings in the target area, the problem of (robust) complete coverage of the urban environment is solved by a three-step approximation approach. Firstly, the target area and the observation area are discretized into two sets respectively. Secondly, the visibility between these two sets is checked. Finally, a set covering problem is solved based on the greedy approaches. Two case studies based on real-world data are carried out to demonstrate the effectiveness of our developed system.

In this paper, optimal critic learning is developed for robot control in a time-varying environment. The unknown environment is described as a linear system with time-varying parameters, and impedance control is employed for the interaction control. Desired impedance parameters are obtained in the sense of an optimal realization of the composite of trajectory tracking and force regulation. Q -function-based critic learning is developed to determine the optimal impedance parameters without the knowledge of the system dynamics. The simulation results are presented and compared with existing methods, and the efficacy of the proposed method is verified.

Over the past few decades, a considerable amount of literature has been published on shape classification. Since classification of well-segmented shapes has become easy to achieve, a number of recent studies have emphasized the importance of robustness to noise and deformations. So in this paper, we undertake the task of classifying similar & noisy binary shape images, using a biologically inspired technique called log-polar transform (LPT). The LPT mapping technique achieves scale and rotation invariance by simulating the foveal mechanism of the human vision system. In order to ensure optimal shape representation in the log-polar space, an iterative method is presented for the LPT lattice design. In addition to optimal shape representation, the use of linear discriminant analysis is proposed for dimensionality reduction and elimination of noisy features. Besides eliminating noisy features, discriminant analysis plays a crucial role in differentiating between similar shape categories. The proposed shape classification framework is tested on five publicly available databases, and substantial boost in classification accuracy is reported compared to state-of-the-art methods. In addition to superior classification accuracy, real time performance is demonstrated using an efficient PC-based implementation.

Valve Stiction is a common nonlinear phenomenon in pneumatic control valves and it causes oscillations in the control loops. A model of valve stiction that is easy to implement and accurate is desired for analysis of this phenomenon. Compared with the physical model, the data-driven model does not require excess knowledge on various physical parameters, thus it is widely used in modeling and diagnosis of valve stiction behavior. In this paper, modifications are made to the Two-layer binary tree data-driven model to overcome its shortcomings on handling instantaneous input command on reverse motion. It has simpler logic structure compared with recent proposed XCH model. Accuracy of the revised binary tree model is then tested and validated by ISA control valve standard test.

The performance of Brain-Computer Interface (BCI) applications are sometimes hindered by non-stationarity in the EEG data from sessions on different days. This paper proposes an algorithm for adaptive training of a SVM classifier to address the non-stationarity in EEG by adapting the kernel to data from subsequent sessions. The kernel width parameter of the kernel function of the SVM classifier is adapted using an information theoretic cost function based on minimum error entropy (MEE). An experiment is performed using the proposed method on EEG data collected without feedback from 12 healthy subjects in two sessions on separate days. The results using the proposed method yielded a mean accuracy of 75%, which is significantly better compared to the baseline result of 67% without kernel adaptation (P=0.00029).

Integrated servo-mechanical design for achieving positive realness constraints and control specifications of high-performance mechatronics is generally a nonlinear and nonconvex optimization problem. In this paper, a generalized Kalman-Yakubovic-Popov Lemma-based algorithm is proposed for simultaneous finite frequency redesign of the mechanical plant and controller using a convex separable parametrization. Our simulation results using the proposed algorithm achieve a high-bandwidth control system with disturbance attenuation capabilities at the phase-stabilized resonant modes. An identical closed-loop performance is achieved with a reduced-order controller from considering the mechanical realization of controller anti-resonant zeros.

The scanning accuracy of piezoelectric mechanisms over broadband frequencies is limited due to inherent dynamic hysteresis. This phenomenon has been a key bottleneck to the use of piezoelectric mechanisms in fast and precision scanning applications. This paper presents a systematic model identification and composite control strategy without hysteresis measurement for such applications. First, least squares estimation using harmonic signals is applied to achieve the Preisach density function. Next, the hysteresis output is estimated, such that the non-hysteretic dynamics can be identified. The discrete composite control strategy is proposed with a feedforward-feedback structure. The feedforward controller is the primary component designed for the performance. The secondary proportional-integral (PI) feedback controller is employed to suppress disturbances for robustness. Finally, the identification and composite control strategy is implemented with a dSPACE 1104 board for a real piezoelectric actuator setup. The experimental results indicate that adequate scanning performance can be sustained at a rate higher than the first resonant frequency.

This paper addresses an important problem known as EEG non-stationarity in Brain-computer Interfacing. We propose a novel technique called Dynamically Weighted Classification with Clustering (DWCC), which explores hidden states in non-stationary EEG using a modified k-means clustering method by combining cosine distance measure and mutual information criterion. DWCC builds a set of classifiers, one for each pair of clusters from different classes. A dynamically-weighted classifier ensemble network is trained to combine the outputs of the classifiers, where we propose to dynamically assign the weight of a classifier for each test sample based on its distances to the cluster centres associated with the classifier. Experimental results on publicly available BCI Competition IV Dataset 2a yielded a mean accuracy of 81.5% which is statistically significant (t-test p<0.05) compared to the baseline result of 75.9% using a single classifier.

The implementation of Model Predictive Control (MPC) requires to solve an optimization problem online. The computation time, often not negligible especially for Nonlinear MPC (NMPC), introduces a delay in the feedback loop. Moreover, it impedes fast sampling rate setting for the controller to react to uncertainties quickly. In this paper, a dual time scale control scheme is proposed for linear/nonlinear systems with external disturbances. A pre-compensator works at fast sampling rate to suppress uncertainty, while the outer MPC controller updates the open loop input sequence at a slower rate. The computation delay is explicitly considered and compensated in the MPC design. Four Robust MPC algorithms for linear/nonlinear systems in the literature are tailored for the proposed control scheme. The recursive feasibility and stability are rigorously analyzed. Simulation examples validate the proposed approaches.

To compress research and development (R&D) cycle times of high-tech mechatronic products with conformance performance metrics, managing R&D projects to allow engineers from electrical, mechanical, and manufacturing disciplines receive real-time design feedback and assessment are essential. In this paper, we propose a systems design procedure to integrate mechanical design, structure prototyping, and servo evaluation through careful comprehension of the servo-mechanical-prototype production cycle commonly employed in mechatronic industries. Our approach focuses on the Modal Parametric Identification of key feedback parameters for fast exchange of design specifications and information. This enables efficient conduct of product design evaluations, and supports schedule compression of the R&D project life cycle in the highly competitive consumer electronics industry. Using the commercial hard disk drive as a case example, we demonstrate how our approach allow inter-disciplinary specifications to be communicated among engineers from different backgrounds to speed up the R&D process for the next generation of intelligent manufacturing. This provides the management of technology team with powerful decision-making tools for project strategy formulation, and improvements in project outcome are potentially massive because of the low costs of change.

In this paper, we consider the problem of allocating machine resources among multiple agents, each of which is responsible to solve a flowshop scheduling problem. We present an iterated combinatorial auction mechanism in which bid generation is performed within each agent, while a price adjustment procedure is performed by a centralized auctioneer. While this approach is fairly well-studied in the literature, our primary innovation is in an adaptive price adjustment procedure, utilizing variable step-size inspired by adaptive PID-control theory coupled with utility pricing inspired by classical microeconomics. We compare with the conventional price adjustment scheme proposed in Fisher (1985), and show better convergence properties. Our secondary contribution is in a fast bid-generation procedure executed by the agents based on local search. Putting both these innovations together, we compare our approach against a classical integer programming model as well as conventional price adjustment schemes, and show drastic run time improvement with insignificant loss of global optimality.

The piecewise affine (PWA) model represents an attractive model structure for approximating nonlinear systems. In this paper, a procedure for obtaining the PWA autoregressive exogenous (ARX) (autoregressive systems with exogenous inputs) models of nonlinear systems is proposed. Two key parameters defining a PWARX model, namely, the parameters of locally affine subsystems and the partition of the regressor space, are estimated, the former through a least-squares-based identification method using multiple models, and the latter using standard procedures such as neural network classifier or support vector machine classifier. Having obtained the PWARX model of the nonlinear system, a controller is then derived to control the system for reference tracking. Both simulation and experimental studies show that the proposed algorithm can indeed provide accurate PWA approximation of nonlinear systems, and the designed controller provides good tracking performance.

In this paper, adaptive neural network (NN) control is investigated for a class of block triangular multiinput-multioutput nonlinear discrete-time systems with each subsystem in pure-feedback form with unknown control directions. These systems are of couplings in every equation of each subsystem, and different subsystems may have different orders. To avoid the noncausal problem in the control design, the system is transformed into a predictor form by rigorous derivation. By exploring the properties of the block triangular form, implicit controls are developed for each subsystem such that the couplings of inputs and states among subsystems have been completely decoupled. The radial basis function NN is employed to approximate the unknown control. Each subsystem achieves a semiglobal uniformly ultimately bounded stability with the proposed control, and simulation results are presented to demonstrate its efficiency.

Piecewise affine model is a useful tool for approximating nonlinear systems. In this paper, we first propose a procedure for obtaining the piecewise affine ARX models of nonlinear systems. Two parameters which fully characterize a piecewise affine ARX model, namely the parameters of the locally linear/affine subsystems, as well as the partitions of the regressor space, will be estimated, the former through a least-squares based identification method using multiple models, and the latter using standard procedures such as neural network classifier or support vector machine classifier. Based on the piecewise affine ARX model of the nonlinear system, we then proceed to derive a model-based controller to control the system for reference tracking. Simulation studies show that our algorithm can indeed provide accurate piecewise affine approximation of nonlinear systems, and that the proposed controller provides good tracking performance.

The design of multiclass BCI is a very challenging task because of the need to extract complex spatial and temporal patterns from noisy multidimensional time series generated from EEG measurements. This paper proposes a Multiclass Common Spatial Pattern (MCSP) based on Joint Approximate Diagonalization (JAD) for multiclass BCIs. The proposed method based on fast Frobenius diagonalization (FFDIAG) is compared with another method based on Jacobi angles on the BCI competition IV dataset 2a. The classification accuracies obtained from 10×10-fold cross-validations on the training dataset are compared using K-Nearest Neighbor, Classification Trees and Support Vector Machine classifiers. The proposed MCSP based on FFDIAG yields an averaged accuracy of 53.6% compared to 32.8% given by the method based on Jacobi angles and 27.8% of the one versus rest CSP methods.

In this brief, adaptive neural control is presented for a class of output feedback nonlinear systems in the presence of unknown functions. The unknown functions are handled via on-line neural network (NN) control using only output measurements. A barrier Lyapunov function (BLF) is introduced to address two open and challenging problems in the neuro-control area: 1) for any initial compact set, how to determine a priori the compact superset, on which NN approximation is valid; and 2) how to ensure that the arguments of the unknown functions remain within the specified compact superset. By ensuring boundedness of the BLF, we actively constrain the argument of the unknown functions to remain within a compact superset such that the NN approximation conditions hold. The semiglobal boundedness of all closed-loop signals is ensured, and the tracking error converges to a neighborhood of zero. Simulation results demonstrate the effectiveness of the proposed approach.

In this paper, adaptive control is investigated for a class of discrete-time nonlinear multi-agent systems (MAS). Each agent is of uncertain dynamics and is affected by other agents in its neighborhood. An agent is able to sense the outputs of the agents inside its neighborhood but is unable to sense those outside its neighborhood. Among all the agents, there is a hidden leader, which knows the desired tracking trajectory, but it is affected by and can only affect those agents inside its neighborhood while all other agents are not aware of its leadership. The decentralized adaptive control is designed for each agent by using the information of its neighbors. Under the proposed decentralized adaptive controls, both rigid mathematical proof and simulation studies are provided to show that all the agents are guaranteed to reach their common goal, i.e., following the desired reference.

In this paper, adaptive variable structure neural control is investigated for a class of nonlinear systems under the effects of time-varying state delays and uncertain hysteresis inputs. The unknown time-varying delay uncertainties are compensated for using appropriate Lyapunov-Krasovskii functionals in the design, and the effect of the uncertain hysteresis with the Prandtl-Ishlinskii (PI) model representation is also mitigated using the proposed control. By utilizing the integral-type Lyapunov function, the closed-loop control system is proved to be semiglobally uniformly ultimately bounded (SGUUB). Extensive simulation results demonstrate the effectiveness of the proposed approach.

In this paper, adaptive neural control is investigated for a class of unknown nonlinear systems in pure-feedback form with the generalized Prandtl-Ishlinskii hysteresis input. To deal with the nonaffine problem in face of the nonsmooth characteristics of hysteresis, the mean-value theorem is applied successively, first to the functions in the pure-feedback plant, and then to the hysteresis input function. Unknown uncertainties are compensated for using the function approximation capability of neural networks. The unknown virtual control directions are dealt with by Nussbaum functions. By utilizing Lyapunov synthesis, the closed-loop control system is proved to be semiglobally uniformly ultimately bounded, and the tracking error converges to a small neighborhood of zero. Simulation results are provided to illustrate the performance of the proposed approach.

10.1145/1409540.1409547

This paper is concerned with the$H_{\infty} $filter design for nonlinear systems with time-varying delay via Takagi–Sugeno fuzzy model approach. Delay-dependent design method is proposed in terms of linear matrix inequalities (LMIs), which forms the main contribution of this paper. The main technique used is the free-weighting matrix method combined with a matrix decoupling approach. The results for rate-independent case, delay-independent case, and delay-free case are also given as easy corollaries. An illustrative example is given to show the effectiveness of the present method.

This paper addresses the problem of observer-based Hinfincontrol for nonlinear systems with time-varying delay represented by Takagi-Sugeno (T-S) fuzzy model. It presents a single-step linear matrix inequality (LMI) method for the fuzzy control design, which overcomes the drawback of the two-step LMI approach often encountered in the literature. The derivation relies mainly on a proposed matrix decoupling technique using which a resultant matrix inequality can be equivalently converted to strict LMIs. When restricted to delay-free fuzzy systems, the present results improve or reduce to existing ones. Illustrative examples show the effectiveness and merits of the present results.

In this paper, adaptive neural network (NN) control is investigated for a class of nonlinear pure-feedback discrete-time systems. By using prediction functions of future states, the pure-feedback system is transformed into an n-step-ahead predictor, based on which state feedback NN control is synthesized. Next, by investigating the relationship between outputs and states, the system is transformed into an input-output predictor model, and then, output feedback control is constructed. To overcome the difficulty of nonaffine appearance of the control input, implicit function theorem is exploited in the control design and NN is employed to approximate the unknown function in the control. In both state feedback and output feedback control, only a single NN is used and the controller singularity is completely avoided. The closed-loop system achieves semiglobal uniform ultimate boundedness (SGUUB) stability and the output tracking error is made within a neighborhood around zero. Simulation results are presented to show the effectiveness of the proposed control approach.

In this paper, output feedback adaptive neural network (NN) controls are investigated for two classes of nonlinear discrete-time systems with unknown control directions: 1) nonlinear pure-feedback systems and 2) nonlinear autoregressive moving average with exogenous inputs (NARMAX) systems. To overcome the noncausal problem, which has been known to be a major obstacle in the discrete-time control design, both systems are transformed to a predictor for output feedback control design. Implicit function theorem is used to overcome the difficulty of the nonaffine appearance of the control input. The problem of lacking a priori knowledge on the control directions is solved by using discrete Nussbaum gain. The high-order neural network (HONN) is employed to approximate the unknown control. The closed-loop system achieves semiglobal uniformly-ultimately-bounded (SGUUB) stability and the output tracking error is made within a neighborhood around zero. Simulation results are presented to demonstrate the effectiveness of the proposed control.

Deoxyribonucleic acid (DNA) coding methods determine the meaning of a certain character in individual chromosomes by the characters surrounding it. The meaning of each character is context dependent, not position dependent. Although position-dependent coding is most commonly used in genetic algorithms (GAs), a context-dependent coding formation is in fact more closer to the natural DNA chromosome. With the context dependency, the DNA coding methods allow intron parts, redundancy, and variable string length in encoded strings while remaining compatible with the standard genetic operations. This paper tries to explicitly explore the influence of those special features of the DNA coding scheme. Two fundamental DNA coding methods (with and without the use of introns) are constructed and compared with the integer coding method, which lacks the features of interest. The performance of the proposed DNA coding methods is analyzed through the robot soccer role assignment problem. The context-dependent coding exhibits the advantages in handling the negative effect of epistasis. The redundancy and intron parts are helpful in preventing useful schemata from disruption and in increasing the population diversity. The variable length of the individual string enables GAs to evolve both the size and the structure of the fuzzy rule base.

In this work, a fault detection method is developed based on a neural network (NN) learning model. The robust observer is designed for monitoring fault, without NN learning, when the system of concern is operating in the normal healthy mode. By comparing appropriate states with their signatures, the fault diagnosis can be carried out and the NN learning is then triggered to identify the fault function.

In this paper, we consider the decentralized adaptive control design problem for uncertain mechanical systems, where uncertainty may arise due to isolated subsystem and/or interconnections among subsystems. Radial basis function neural networks are used to approximate the nonlinear functions to include both dynamic and interconnection uncertainties in each subsystem. The stability of the thus designed control system can be guaranteed by a rigid proof. Finally, a simulation example is given to illustrate the effectiveness of the proposed algorithm.

This correspondence studies the problem of observer-based H infinity control for time-delay Takagi-Sugeno (T-S) fuzzy systems. It provides a delay-dependent linear matrix inequality (LMI)-based method for the control design. It is known that the key important problem in the literature, even for delay-independent case, lies in the difficulty of decoupling matrix variables in corresponding matrix inequalities. This correspondence suggests a decoupling technique for solving matrix inequalities with coupled variables, and provides an LMI-based algorithm by adopting the idea of the cone complementarity problem. The derivation relies on the appropriate choice of Lyaponuv-Krasovskii functionals which incorporate the intersections among local systems. Illustrative examples are given to show the effectiveness of the present delay-dependent result.

This paper presents a discrete-time robust nonlinear control method to achieve fast and accurate set-point tracking for servo systems subject to actuator saturation and disturbances. The idea here is to use a combination of composite nonlinear feedback (CNF) control and disturbance estimation cum compensation. The CNF control is responsible for superior transient performance, i.e., to guarantee a fast response with low overshoot, while the disturbance estimator/compensator is used to remove the steady state bias that would otherwise be existent due to disturbances. Practical application in a micro hard disk drive servo system will be given to demonstrate the effectiveness of this control method

Adaptive smart neural network controller design is presented in this paper for wheeled mobile robots with unknown dynamics. The controller is constructed at the dynamical level. The smart neural control scheme is designed such that the current control action not only can utilize the knowledge that neural networks learned from the past experience, but also keep the learning ability in the operational phase and finish the same control task in a 'smarter' way. The proposed neural control scheme can act smartly in the operational phase after the networks have been well trained in the training phase, in a way similar to the control process of human in learning to accomplish some complicated control tasks. All the system states are shown to be able to track the desired trajectory. Numerical simulation is conducted to verify the effectiveness of the proposed method

This paper studies a class of fuzzy time-delay descriptor systems in the extended Takagi-Sugeno (T-S) fuzzy model. Sufficient conditions are derived for the stability and stabilization in terms of linear matrix inequalities (LMIs). Illustrative examples are given to show the effectiveness and the advantages of the present results

Fisher linear discriminant (FLD) has recently emerged as a more efficient approach for extracting features for many pattern classification problems as compared to traditional principal component analysis. However, the constraint on the total number of features available from FLD has seriously limited its application to a large class of problems. In order to overcome this disadvantage, a recursive procedure of calculating the discriminant features is suggested in this paper. The new algorithm incorporates the same fundamental idea behind FLD of seeking the projection that best separates the data corresponding to different classes, while in contrast to FLD the number of features that may be derived is independent of the number of the classes to be recognized. Extensive experiments of comparing the new algorithm with the traditional approaches have been carried out on face recognition problem with the Yale database, in which the resulting improvement of the performances by the new feature extraction scheme is significant.

In this letter, we solve the problem of decentralized adaptive asymptotic tracking for a class of large scale systems with significant nonlinearities and uncertainties. Neural networks (NNs) are used as a control part to cancel the effect of the unknown nonlinearity. Semiglobal asymptotic stability results are obtained and the tracking error converges to zero.

This paper studies the problem of H(infinity) output tracking control for nonlinear time-delay systems using Takagi-Sugeno (T-S) fuzzy model approach. An LMI-based design method is proposed for achieving the output tracking purpose. Illustrative examples are given to show the effectiveness of the present results.

In view of a recent new application of variable structure control (VSC) to the stabilization problem for Takagi-Sugeno (T-S) fuzzy models, this paper aims to study the stabilization of uncertain fuzzy time-delay systems in T-S fuzzy model via VSC approach. There are mainly two features in this paper: one lies in the incorporation of time-delays (both smooth and nonsmooth delays) in which case Lyapunov functionals and Razumikhin Theorem are required to solve the stabilization problem; the other feature is that not only matched uncertainties but also mismatched uncertainties in the state variables are considered. As a sequence, the contribution of this paper consists of various control schemes proposed for the VSC design and the present results are in terms of linear matrix inequalities (LMIs). An illustrative example is given to show the effectiveness of our various results.

A geometrical interpretation of the multilayer perceptron (MLP) is suggested in this paper. Some general guidelines for selecting the architecture of the MLP, i.e., the number of the hidden neurons and the hidden layers, are proposed based upon this interpretation and the controversial issue of whether four-layered MLP is superior to the three-layered MLP is also carefully examined.

In this paper, adaptive neural network (NN) control is investigated for a class of discrete-time multi-input-multi-output (MIMO) nonlinear systems with triangular form inputs. Each subsystem of the MIMO system is in strict feedback form. First, through two phases of coordinate transformation, the MIMO system is transformed into input-output representation with the triangular form input structure unchanged. By using high-order neural networks (HONNs) as the emulators of the desired controls, effective output feedback adaptive control is developed using backstepping. The closed-loop system is proved to be semiglobally uniformly ultimate bounded (SGUUB) by using Lyapunov method. The output tracking errors are guaranteed to converge into a compact set whose size is adjustable, and all the other signals in the closed-loop system are proved to be bounded. Simulation results show the effectiveness of the proposed control scheme.

Practical adaptive neural control is presented for a class of nonlinear systems with unknown time delays in strict-feedback form. Using appropriate Lyapunov-Krasovskii functionals, the unknown time delays are compensated for. Controller singularity problems are solved by practical neural network control. A novel differentiable control function is provided such that the practical design can be carried out in the decoupled backstepping design. It is proved that the proposed design method is able to guarantee semi-global uniform ultimate boundedness of all the signals in the closed-loop system, and the tracking error is proven to converge to a small neighborhood of the origin.

This paper presents a distributed coevolutionary classifier (DCC) for extracting comprehensible rules in data mining. It allows different species to be evolved cooperatively and simultaneously, while the computational workload is shared among multiple computers over the Internet. Through the intercommunications among different species of rules and rule sets in a distributed manner, the concurrent processing and computational speed of the coevolutionary classifiers are enhanced. The advantage and performance of the proposed DCC are validated upon various datasets obtained from the UCI machine learning repository. It is shown that the predicting accuracy of DCC is robust and the computation time is reduced as the number of remote engines increases. Comparison results illustrate that the DCC produces good classification rules for the datasets, which are competitive as compared to existing classifiers in literature.

This work presents a new dynamical optimal learning (DOL) algorithm for three-layer linear neural networks and investigates its generalization ability. The optimal learning rates can be fully determined during the training process. The mean squared error is guaranteed to be stably decreased and the learning is less sensitive to initial parameter settings. The simulation results illustrate that the proposed DOL algorithm gives better generalization performance and faster convergence as compared to standard error back propagation algorithm.

We present in this article a MATLAB toolkit with a user-friendly graphical interface for composite nonlinear feedback control system design. The toolkit can be utilized to design a fast and smooth tracking controller for a class of linear systems with actuator and other nonlinearities as well as with external disturbances. The toolkit is capable of displaying both time-domain and frequency-domain responses on its main panel, and generating three different types of control laws, namely, the state feedback, the full order measurement feedback and the reduced order measurement feedback controllers. The usage and design procedure of the toolkit are illustrated by a practical example on the design of a hard disk drive servo system. The toolkit can be utilized to design servo systems that deal with point-and-shoot fast targeting.

In this paper, robust adaptive control strategy is presented for a wheeled mobile robot in the presence of model perturbations that violates the nonholonomic assumption. The nonholonomic constraint of the vehicle is assumed to be violated by an unknown slippage. Consequently, a perturbed kinematic model of the system is obtained. Using backstepping, the proposed controller is constructed at the dynamical level. The robust adaptive controller is to eliminate the needs for the LIP form of the system dynamics and the exact bounds of the system dynamics. All the system states are shown to be able to track the desired trajectory. The simulation results demonstrate the effectiveness of the proposed controllers.

The constraint on the total number of features available from the Fisher linear discriminant (FLD) has seriously limited its application to a large class of problems. In order to overcome this disadvantage of FLD, a recursive procedure for calculating the discriminant features is suggested in this paper. Extensive experiments of comparing the new algorithm with the traditional PCA and FLD approaches have been carried out on a face recognition problem, in which the resulting improvement of the performance by the new feature extraction scheme is significant.

An extensive fuzzy behavior-based architecture is proposed for the control of mobile robots in a multiagent environment. The behavior-based architecture decomposes the complex multirobotic system into smaller modules of roles, behaviors and actions. Fuzzy logic is used to implement individual behaviors, to coordinate the various behaviors, to select roles for each robot and, for robot perception, decision-making, and speed control. The architecture is implemented on a team of three soccer robots performing different roles interchangeably. The robot behaviors and roles are designed to be complementary to each other, so that a coherent team of robots exhibiting good collective behavior is obtained.

In this paper, adaptive neural control is presented for a class of strict-feedback nonlinear systems with unknown time delays. The proposed design method does not require a priori knowledge of the signs of the unknown virtual control coefficients. The unknown time delays are compensated for using appropriate Lyapunov-Krasovskii functionals in the design. It is proved that the proposed backstepping design method is able to guarantee semiglobal uniformly ultimately boundedness of all the signals in the closed-loop. In addition, the output of the system is proven to converge to a small neighborhood of the origin. Simulation results are provided to show the effectiveness of the proposed approach.

In this paper, adaptive neural network (NN) control is investigated for a class of multiinput and multioutput (MIMO) nonlinear systems with unknown bounded disturbances in discrete-time domain. The MIMO system under study consists of several subsystems with each subsystem in strict feedback form. The inputs of the MIMO system are in triangular form. First, through a coordinate transformation, the MIMO system is transformed into a sequential decrease cascade form (SDCF). Then, by using high-order neural networks (HONN) as emulators of the desired controls, an effective neural network control scheme with adaptation laws is developed. Through embedded backstepping, stability of the closed-loop system is proved based on Lyapunov synthesis. The output tracking errors are guaranteed to converge to a residue whose size is adjustable. Simulation results show the effectiveness of the proposed control scheme.

In this paper, we develop a predictive learning controller for ram velocity of injection molding based on neural networks. We first introduce a model of describing the injection molding, including the time horizon and the batch index. The feedback control plus biased function is proposed for controlling this plant. More specifically, a radial basis function (RBF) network is used to approximate the biased function based on the time horizon. The weights in the RBF are determined by a predictive control scheme based on the batch index. For this algorithm, relevant convergence is investigated. Simulation results reveal that the proposed control can achieve our claims.

In this paper, robust adaptive neural network (NN) control is presented to solve the control problem of nonholonomic systems in chained form with unknown virtual control coefficients and strong drift nonlinearities. The robust adaptive NN control laws are developed using state scaling and backstepping. Uniform ultimate boundedness of all the signals in the closed-loop are guaranteed, and the system states are proven to converge to a small neighborhood of zero. The control performance of the closed-loop system is guaranteed by appropriately choosing the design parameters. The proposed adaptive NN control is free of control singularity problem. An adaptive control based switching strategy is used to overcome the uncontrollability problem associated with x0 (t0) = 0. The simulation results demonstrate the effectiveness of the proposed controllers.

In this paper, for joint torque optimization of redundant manipulators subject to physical constraints, we show that velocity-level and acceleration-level redundancy-resolution schemes both can be formulated as a quadratic programming (QP) problem subject to equality and inequality/bound constraints. To solve this QP problem online, a primal-dual dynamical system solver is further presented based on linear variational inequalities. Compared to previous researches, the presented QP-solver has simple piecewise-linear dynamics, does not entail real-time matrix inversion, and could also provide joint-acceleration information for manipulator torque control in the velocity-level redundancy-resolution schemes. The proposed QP-based dynamical system approach is simulated based on the PUMA560 robot arm with efficiency and effectiveness demonstrated.

The issue for designing robust adaptive stabilizing controllers for nonlinear systems in Takagi-Sugeno fuzzy model with both parameter uncertainties and external disturbances is studied in this paper. It is assumed that the parameter uncertainties are norm-bounded and may be of some structure properties and that the external disturbances satisfy matching conditions and, besides, are also norm-bounded, but the bounds of the external disturbances are not necessarily known. Two adaptive controllers are developed based on linear matrix inequality technique and it is shown that the controllers can guarantee the state variables of the closed loop system to converge, globally, uniformly and exponentially, to a ball in the state space with any pre-specified convergence rate. Furthermore, the radius of the ball can also be designed to be as small as desired by tuning the controller parameters. The effectiveness of our approach is verified by its application in the control of a continuous stirred tank reactor.

This paper considers a transportation problem for moving empty or laden containers for a logistic company. A model for this truck and trailer vehicle routing problem (TTVRP) is first constructed in the paper. The solution to the TTVRP consists of finding a complete routing schedule for serving the jobs with minimum routing distance and number of trucks, subject to a number of constraints such as time windows and availability and multimodal combinatorial optimization problem, a hybrid multiobjective evolutionary algorithm (HMOEA) is applied to find the Pareto optimal routing solutions for the TTVRP. Detailed analysis is performed to extract useful decision-making information from the multiobjective optimization results. The computational results have shown that the HMOEA is effective for solving multiobjective combinatorial problems, such as finding useful trade-off solutions for the TTVRP.

Although evolutionary algorithm is a powerful optimization tool, its computation cost involved in terms of time and hardware increases as the size and complexity of the problem increases. In this paper, a Java-based distributed evolutionary computing package (Paladin-DEC) is presented by exploiting the inherent parallel nature of evolutionary algorithms. The package enhances the concurrent processing and performance of evolutionary algorithms by allowing inter-communications of subpopulations among various computers over the Internet. The Paladin-DEC is incorporated with the features of security, scalability and fault tolerance, and is capable of keeping data integrity throughout the computation. The effectiveness and advantages of the Paladin-DEC are illustrated through a case study of drug scheduling in cancer chemotherapy.

The main objectives of multiobjective evolutionary algorithms are to minimize the distance between the solution set and true Pareto front, to distribute the solutions evenly and to maximize the spread of solution set. This paper addresses these issues by presenting two features that enhance the ability of multiobjective evolutionary algorithms. The first feature is a variant of the mutation operator that adapts the mutation rate along the evolution process to maintain a balance between the introduction of diversity and local fine-tuning. In addition, this adaptive mutation operator adopts a new approach to strike a compromise between the preservation and disruption of genetic information. The second feature is a novel enhanced exploration strategy that encourages the exploration towards less populated areas and hence achieves better discovery of gaps in the generated front. This strategy also preserves nondominated solutions in the evolving population and hence gives good convergence. Comparative studies show that the proposed features are effective.

Evolutionary techniques have become one of the most powerful tools for solving multiobjective optimization (MOO) problems. However the computational cost involved in terms of time and hardware often become surprisingly burdensome as the size and complexity of the problem increases. We propose a distributed cooperative coevolutionary algorithm (DCCEA), which evolves multiple solutions in the form of cooperative subpopulations and exploits the inherent parallelism by sharing the computational workload among computers over the network. Through its multiple features such as archiving, dynamic sharing and extending operator, solutions of DCCEA are not only pushed to the true Pareto front but also well distributed. Simulation results show that DCCEA has a very competitive performance and reduces the runtime effectively.

This paper presents a kind of cooperative co-evolutionary algorithm (CCEA) for multi-objective optimization (MOO). In this algorithm, solutions evolve in the form of cooperative subpopulations. An archive stores non-dominated solutions and helps to evaluate individuals in the subpopulations. The mechanism of niching is applied to maintain the diversity of solutions in the archive. Meanwhile, an extending operator is designed to mine information on solution distribution from the archive and guide the search to regions that are not explored enough. Extensive simulations are performed on different benchmark problems for various multi-objective evolutionary algorithms (MOEAs) and indicate that CCEA is strongly competitive with five recent well-known MOEAs in finding a good non-dominated solution set.

Vehicle routing problem with time windows (VRPTW) involves the routing of a set of vehicles with limited capacity from a central depot to a set of geographically dispersed customers with known demands and predefined time windows. This paper proposes a hybrid multiobjective evolutionary algorithm (HMOEA) that incorporates various heuristics for local exploitation in the evolutionary search and the concept of Pareto's optimality for solving multiobjective optimization in VRPTW problems. The proposed HMOEA optimizes all routing constraints and objectives simultaneously, which improves the routing solutions in many aspects, such as lower routing cost, wider scattering area and better convergence trace.

This paper presents an evolutionary algorithm with a new goal-sequence domination scheme for better decision support in multi-objective optimization. The approach allows the inclusion of advanced hard/soft priority and constraint information on each objective component, and is capable of incorporating multiple specifications with overlapping or non-overlapping objective functions via logical 'OR' and 'AND' connectives to drive the search towards multiple regions of trade-off. In addition, we propose a dynamic sharing scheme that is simple and adaptively estimated according to the on-line population distribution without needing any a priori parameter setting. Each feature in the proposed algorithm is examined to show its respective contribution, and the performance of the algorithm is compared with other evolutionary optimization methods. It is shown that the proposed algorithm has performed well in the diversity of evolutionary search and uniform distribution of non-dominated individuals along the final trade-offs, without significant computational effort. The algorithm is also applied to the design optimization of a practical servo control system for hard disk drives with a single voice-coil-motor actuator. Results of the evolutionary designed servo control system show a superior closed-loop performance compared to classical PID or RPT approaches.

Multistability is a property necessary in neural networks in order to enable certain applications (e.g., decision making), where monostable networks can be computationally restrictive. This article focuses on the analysis of multistability for a class of recurrent neural networks with unsaturating piecewise linear transfer functions. It deals fully with the three basic properties of a multistable network: boundedness, global attractivity, and complete convergence. This article makes the following contributions: conditions based on local inhibition are derived that guarantee boundedness of some multistable networks, conditions are established for global attractivity, bounds on global attractive sets are obtained, complete convergence conditions for the network are developed using novel energy-like functions, and simulation examples are employed to illustrate the theory thus developed.

Zhang et al. presented an excellent neural-network (NN) controller for a class of nonlinear control designs. The singularity issue is completely avoided. Based on a modified Lyapunov function, their lemma illustrates the existence of an ideal control which is important in establishing the NN approximator. In this paper, we provide a Lyapunov function to realize an alternative ideal control which is more direct and simpler. The major contributions of this paper are divided into two parts. First, it proposes a control scheme which results in a smaller dimensionality of NN than that of Zhang et al. In this way, the proposed NN controller is easier to implement and more reliable for practical purposes. Second, by removing certain restrictions from the design reported by Zhang et al., we further develop a new NN controller, which can be applied to a wider class of systems.

The advantages and disadvantages of evolving neural control systems for mobile robots using genetic algorithms are investigated. The Khepera robot is trained using the evolutionary neural networks (ENN) algorithm for the task of obstacle avoidance. The feasibility of using Q-learning for robot learning is also studied. It is found that Q-learning can be successfully used to train a robot and is more promising than the ENN algorithm in this case. The Webots simulation software has been used to carry out all the experiments.

This paper presents the optimal control of drug scheduling in cancer chemotherapy using a distributed evolutionary computing software. Unlike conventional methods that often require gradient information or hybridization of different approaches in drug scheduling, the proposed evolutionary optimization methodology is simple and capable of automatically finding the near-optimal solutions for complex cancer chemotherapy problems. It is shown that different number of variable pairs in evolutionary representation for drug scheduling can be easily implemented via the software, since the computational workload is shared and distributed among multiple computers over the Internet. Simulation results show that the proposed evolutionary approach produces excellent control of drug scheduling in cancer chemotherapy, which are competitive or equivalent to the best solutions published in literature.

In this paper, we present the registration of disparate spatial data. To be specific, we consider the registration of digital terrain elevation data (DTED) to National High Altitude Photography (NHAP). Initially, the DTED is shaded to form a synthetic image, and our registration process maps point in the shaded image to points in the NHAP. For the purpose of comparison, we propose two distinct techniques for matching. The first method is a semi-autonomous. It requires two pairs of user defined matched points to estimate an initial transform as starting point in the search for the best fitting transform using Nelder-Mead Simplex Method. The second method, being more novel in nature, attempts to eliminate the need for any user intervention and registers the two data autonomously by employing the Multi Objective Evolutionary Algorithm (MOEA) toolbox. Both methods worked well in estimating the best fitting affine transform to register the image and elevation data, and the MOEA based autonomous technique outperforms the much simpler single objective based semi autonomous technique.

Genetic programming (GP) has emerged as a promising approach to deal with the classification task in data mining. This paper extends the tree representation of GP to evolve multiple comprehensible IF-THEN classification rules. We introduce a concept mapping technique for the fitness evaluation of individuals. A covering algorithm that employs an artificial immune system-like memory vector is utilized to produce multiple rules as well as to remove redundant rules. The proposed GP classifier is validated on nine benchmark data sets, and the simulation results confirm the viability and effectiveness of the GP approach for solving data mining problems in a wide spectrum of application domains.

A comparison of mobile robot navigation using evolutionary neural networks and the pain based algorithm is discussed in this paper. The controllers are designed based on evolutionary neural networks and the pain-based algorithms. The performance of the controllers are verified with the Khepera robot.

The output tracking control problem for nonlinear systems in the presence of both parameter perturbations and external disturbances is studied. Our approach is based on the Takagi-Sugeno (T-S) fuzzy modeling method and a variable structure control (VSC) technique. Therefore, the systems considered are not and need not be in the triangular and parametric strict-feedback form, which are prevalent among adaptive model following control for nonlinear systems, or in the normal form, which pervades almost all existing results in neuro-fuzzy model following control approach. We first study the problem of stabilization of T-S fuzzy systems by using a VSC technique. A method for the design of a switching surface based on linear matrix inequalities is developed and a stabilizing controller based on a reaching law concept in the presence of both parameter perturbations and external disturbances is proposed. Then, the method is extended to design controllers for output tracking of T-S fuzzy nonlinear systems in two cases, i.e. systems which possess the so-called strong passive subsystems and strong stable zero dynamics, respectively. Finally, illustrative examples are presented to demonstrate the whole design procedure from the original nonlinear systems to their fuzzification and finally to the realization of the desired controllers. Simulation results show that the goal of output tracking can be achieved by the proposed controllers.

We develop a decentralized neural-network (NN) controller for a class of large-scale nonlinear systems with the high-order interconnections. The controller is a mixed NN comprised of a conventional NN and a special NN. The conventional NN is used to approximate the unknown nonlinearities in the subsystem, while a special NN is used to counter the high-order interconnections. We prove that this NN structure can achieve a stable controller for the large-scale systems.

A class of energy-based position controllers for a kind of flexible spacecraft is proposed. Closed-loop stability of the original distributed parameter system can be achieved, as well as asymptotic stability for the truncated system, which is obtained through representing the deflection of the appendage by an arbitrary finite number of flexible modes. The feedback gains of the controller are tuned by a genetic algorithm (GA) optimization process to achieve good results for tip motion based on some suitable fitness functions. Numerical simulations are carried out on a kind of spacecraft with one flexible appendage, and satisfactory results are obtained.

Existing multi-objective (MO) evolutionary algorithms apply a fixed search space in the parameter domain. This approach needs a good guess or a-prior knowledge of a promising search area since a wrongly specified range of search space often leads to poor solutions. To address the issue, this paper proposes a novel approach of adaptive search space for MO optimization. Through the method of shrinking and expanding, the technique is capable of directing the evolution to reach more promising search regions even if it is not covered in the initial search space. The role of the inductive learning process is also introduced, which is performed by an exploratory multi-objective evolutionary algorithm to enhance the search from being trapped in local optima as well as to promote the population diversity along the discovered Pareto-optimal front. Features of the proposed approach are experimented and investigated upon benchmark MO optimization problems.

A DNA coded genetic-algorithm (GA) is proposed to optimize the rule-base of a fuzzy logic controller (FLC). The controller is designed for a vehicle-active suspension system to improve the driving comfort. The DNA codes GA constructed optimal decision-making rules for the fuzzy logic controller. Simulation results demonstrate the effectiveness of the algorithm.

The rapid advances of evolutionary methods for multi-objective (MO) optimization poses the difficulty of keeping track of the developments in this field as well as selecting an appropriate evolutionary approach that best suits the problem in-hand. This paper aims to analyze the strength and weakness of different evolutionary methods proposed in the literature. For this purpose, ten existing well-known evolutionary MO approaches have been experimented and compared extensively on two benchmark problems with different MO optimization difficulties and characteristics. Besides considering the usual two important aspects of MO performance, i.e., the spread across the Pareto-optimal front as well as the ability to attain the global optimum or final trade-offs, this paper also proposes a few useful performance measures for better and comprehensive examination of each approach both quantitatively and qualitatively. Simulation results for the comparisons are commented and summarized.

Many evolutionary algorithm based methods have been proposed for handling constraints in numerical optimization problems. These techniques, however, are often based upon the approach of formulating constraints in the objective domain or repairing/rejecting infeasible solutions through specialized genetic operators. The drawback of these approaches is that the potential for both feasible and infeasible solutions coexist, which often leads to a large search space with complex or discontinuous fitness landscape. These infeasible chromosomes must be evaluated or detected with extra computational effort before they are penalized or eliminated from the population. Moreover, these methods need to ensure the domination of feasible candidate solutions during genetic reproductions in order to eliminate the infeasible ones, which can easily misdirect the evolution towards the local optima whenever a feasible solution is reproduced in problems that contain difficult-to-find feasible regions. This paper describes a constraint handling methodology that formulates the optimization constraints directly into the gene domains in evolutionary algorithms. It allows the constraints to be encoded into the chromosomes and as such, trimming away sections of infeasible regions in constraint optimization problems. This results in a smaller search space and reduces the efforts of evolution in finding the global optimum solution.

In vehicle routing problems with time window constraints (VRPTW), a set of vehicles with limited capacity, are to be routed from a central depot to a set of geographically dispersed customers with known demands and predefined time windows. To solve the problem, the optimized assignment of vehicles to each customer is needed as to achieve the minimal total cost without violating the capacity and time window constraints. Combinatorial optimization problems of this kind are NP-hard and are best solved to the near optimum by heuristics. The authors describe their research on a rare class of genetic algorithms, known as the messy genetic algorithms (mGA) in solving the VRPTW problem. The mGA has the merit of directly realizing the relational search needed in VRPTW representation, which cannot be easily realized using simple heuristic methods. The mGA was applied to solve the benchmark Solomon's 56 VRPTW 100-customer instances, and yielded 23 solutions better than or equivalent to the best solutions ever published in literature.

Model-free controllers are presented for multi-link smart materials robots. The controllers are derived from the basic energy-work relationship in the absence of the system model which is complex and difficult. To obtain for multi-link smart materials robots. The smart materials bonded along the links are used to apply additional control to suppress the residue vibration effectively. One can achieve not only the closed-loop stability of the original system, but also the asymptotic stability of the truncated system, which is obtained through representing the deflection of each link by an arbitrary finite number of flexible modes. Simulation results are provided to show the effectiveness of the presented approach.

Evolutionary algorithms have been recognized to be well suited for multiobjective optimization. These methods, however, need to "guess" for an optimal constant population size in order to discover the usually sophisticated tradeoff surface. This paper addresses the issue by presenting a novel incrementing multiobjective evolutionary algorithm (IMOEA) with dynamic population size that is computed adaptively according to the online discovered tradeoff surface and its desired population distribution density. It incorporates the method of fuzzy boundary local perturbation with interactive local fine tuning for broader neighborhood exploration. This achieves better convergence as well as discovering any gaps or missing tradeoff regions at each generation. Other advanced features include a proposed preserved strategy to ensure better stability and diversity of the Pareto front and a convergence representation based on the concept of online population domination to provide useful information. Extensive simulations are performed on two benchmark and one practical engineering design problems.

This paper presents an interactive graphical user interface (GUI) based multiobjective evolutionary algorithm (MOEA) toolbox for effective computer-aided multiobjective (MO) optimization. Without the need of aggregating multiple criteria into a compromise function, it incorporates the concept of Pareto's optimality to evolve a family of nondominated solutions distributing along the tradeoffs uniformly. The toolbox is also designed with many useful features such as the goal and priority settings to provide better support for decision-making in MO optimization, dynamic population size that is computed adaptively according to the online discovered Pareto-front, soft/hard goal settings for constraint handlings, multiple goals specification for logical "AND"/"OR" operation, adaptive niching scheme for uniform population distribution, and a useful convergence representation for MO optimization. The MOEA toolbox is freely available for download at http://vlab.ee.nus.edu.sg/-kctan/moea.htm which is ready for immediate use with minimal knowledge needed in evolutionary computing. To use the toolbox, the user merely needs to provide a simple "model" file that specifies the objective function corresponding to his/her particular optimization problem. Other aspects like decision variable settings, optimization process monitoring and graphical results analysis can be performed easily through the embedded GUIs in the toolbox. The effectiveness and applications of the toolbox are illustrated via the design optimization problem of a practical ill-conditioned distillation system. Performance of the algorithm in MOEA toolbox is also compared with other well-known evolutionary MO optimization methods upon a benchmark problem.

The design problem of proportional and proportional-plus-integral (PI) controllers for nonlinear systems is studied. First, the Takagi-Sugeno (T-S) fuzzy model with parameter uncertainties is used to approximate the nonlinear systems. Then a numerically tractable algorithm based on the technique of iterative linear matrix inequalities is developed to design a proportional (static output feedback) controller for the robust stabilization of the system in T-S fuzzy model. Next, we transform the problem of PI controller design to that of proportional controller design for an augmented system and thus bring the solution of the former problem into the configuration of the developed algorithm. Finally, the proposed method is applied to the design of robust stabilizing controllers for the excitation control of power systems. Simulation results show that the transient stability can be improved by using a fuzzy PI controller when large faults appear in the system, compared to the conventional PI controller designed by using linearization method around the steady state.

We present a new compensation technique for a friction model, which captures problematic friction effects such as Stribeck effects, hysteresis, stick-slip limit cycling, pre-sliding displacement and rising static friction. The proposed control utilizes a PD control structure and an adaptive estimate of the friction force. Specifically, a radial basis function (RBF) is used to compensate the effects of the unknown nonlinearly occurring Stribeck parameter in the friction model. The main analytical result is a stability theorem for the proposed compensator which can achieve regional stability of the closed-loop system. Furthermore, we show that the transient performance of the resulting adaptive system is analytically quantified. To support the theoretical concepts, we present dynamic simulations for the proposed control scheme.

This paper considers improving the tip regulation performance of a joint-PD controlled single-link flexible manipulator by introducing nonlinear strain feedback. The controller is developed by applying Lyapunov's direct method. The stability of the closed-loop system is theoretically proven based on the partial differential equations (PDE) which govern the motion of the flexible robot, instead of using the traditional truncated models. The controller is very simple in its form, and only the measurements of joint angle, joint velocity, and strain of the bending beam are needed for implementation. The controller is very robust as well because it is independent of system parameters.