This paper addresses the challenge of periodic disturbances in discrete-time nonlinear systems by proposing a direct data-driven repetitive control strategy. This framework treats the periodic disturbance term as an augmented state of the nonlinear system. A Fourier-series-based semi-definite programming (SDP) approach is developed to approximate and compensate for the periodic disturbance through disturbance rejection techniques. The proposed scheme enables learning from contaminated data while providing an estimation of the robust positive invariant (RPI) set. Numerical simulations and robotic manipulator experiments validate the effectiveness of the method in mitigating periodic interference, demonstrating its potential for practical applications in control systems engineering.

This paper proposes an exponential-type variable parameter finite-time zeroing neural network (EVPFTZNN) that incorporates a finite-time bounded activation function (BAF). The proposed variable-parameter function exhibits exponential growth dependent on the error signal within prescribed bounds, while its boundary parameters remain fully configurable to accommodate specific application requirements. For example, most parameters are time-varying and are subject to physical constraints, meaning they cannot exceed certain maximum values. Consequently, unbounded activation functions and variable parameters that grow infinitely with time may be somewhat challenging in actual hardware implementation. The EVPFTZNN model studied in this paper is capable of circumventing this problem. Theoretical derivation proves the finite-time convergence of the EVPFTZNN and provides the analysis of its disturbance rejection capability. Furthermore, to verify the practicality of the EVPFTZNN, this paper applies it to solve the joint-drift-free inverse kinematics problem of redundant robots. Compared with several existing ZNNs, computer simulations and physical experiments validate the superior performance of the EVPFTZNN for solving joint-drift-free inverse kinematics of redundant robots.

Clustering complex-shaped clusters is still chal lenging for most existing clustering algorithms. Herein, the peak-padding clustering algorithm (PeakPad)-clustering by padding density peaks with the minimum padding cost-is proposed. PeakPad executes clustering on the density surface and views complex-shaped clusters as combinations of highly associated single-peak clusters. The minimum padding cost that fully considers the surrounding context of a density peak is proposed to reflect a density peak's center potential, enabling PeakPad to have robust center detection performance. Unlike mean-shift (MSC), which detects centers based on their attributes in a complex-shaped density surface embedded in the high-dimensional space of density and features, PeakPad detects centers in a standard-shaped surface embedded in the 2-D density-change (DC) density space (composed of density and DC feature). Such standardization allows PeakPad to have fast and robust cluster center detection performance on complex-shaped clusters based on the minimum padding cost. Besides, PeakPad can provide a reasonable evaluation of the association between single-peak clusters by using the minimum padding cost. As a result, PeakPad can fast capture complex-shaped clusters, achieve robust center detection performance, and be suitable for large datasets. Benchmark test results on both synthetic and real datasets demonstrate the effectiveness of PeakPad.

This study presents a nonlinear feedback regulator for three-dimensional overhead cranes that exploits composite outputs to deliver potent sway suppression. Dedicated barrier functions confine these composite signals within set limits. Owing to the simple structure, the control scheme maintains oscillation suppression under velocity and cable length uncertainties. We validate stability through a Lyapunov-based proof augmented by LaSalle’s invariance principle. Simulation results confirm that the controller achieves trolley positioning and effectively cancels oscillations under external disturbances.

This study proposes a nonlinear regulation controller for 3D overhead cranes, capable of achieving payload sway suppression under complex conditions. Utilizing a special form of barrier functions, constraints on the trolley motion and payload sway are derived. By analyzing the nonlinear terms, parameter estimation is incorporated to the control law, eliminating the need for prior knowledge of crane dynamics. To ensure smooth operation under varying transportation distances, a saturation function is employed to constrain the control torque generated by regulation errors. Within the Lyapunov framework, LaSalle’s invariance principle is invoked to demonstrate asymptotic convergence of the system states. Simulations validate the theoretical claims, including robustness under different transfer scenarios.

In the magnetic actuation system, MAC (Magnetically Actuated Capsule) motion is disturbed by gastrointestinal resistance, and dynamic constraints exist between MAC and EPM (External Permanent Magnet), leading to control difficulties. This paper presents a data-driven control method for magnetic actuation system. Firstly, a data-driven modeling approach is proposed for addressing the modeling challenges, relying solely on MAC visual positioning input and EPM position output data. Secondly, To effectively track rapidly changing MAC desired trajectories, determining the upper bound of system inputs through analysis of the magnetic field relationship, and integrating it with adaptive parameter reset conditions, enables the establishment of dynamic constraints for the MAC system. Finally, dynamic compensation is applied to account for non-linear resistance terms and inaccuracies in data model representation. A dual-visual positioning experimental platform simulating the gastrointestinal environment is established to validate the proposed algorithm’s effectiveness in MAC trajectory tracking under different conditions.Note to Practitioners—This article aims to design a data-driven method that incorporates dynamic constraint relationships between MAC and EPM, enabling capsules to move rapidly within the gastrointestinal system. This paper aims to increase control speed, enabling the energy to be primarily used for capturing the lesion area rather than during motion, and improving MAC tracking for desired trajectory accuracy. To validate the algorithm’s effectiveness under various resistances and driving forces, a dual-camera positioning system is designed and tested on a simulated gastrointestinal platform. In future work, we plan to replace the external camera with an internal capsule camera for MAC localization and design a medical system that actively utilizes patient lesion data to drive WCE for examinations.

In this paper, the issue of data-driven prescribed-time convergence control for certain types of discrete-time nonlinear systems is investigated. The dynamic linearization technique and the discrete prescribed-time gain-scheduled feedback function have been combined to create a model-free adaptive prescribed time control strategy. To keep the steady-state error within a bespoke steady-state error band, the controller is switched to a steady-state controller once the system has completed its prescribed time control task. Theoretically, it is demonstrated that the error dynamics can gradually compress from the initial error to the steady-state error band during the initial and prescribed time periods. Furthermore, all of the rule designs in this study are data-driven and solely rely on system input and output data. Manipulator experiments and numerical modeling demonstrate the viability of the proposed strategy.Note to Practitioners—The motivation for this paper stems from the need to design a prescribed-time control method for the industrial robot trajectory tracking control tasks. Prescribed-time control method offers a potential to improve both the speed and coordination of industrial robot trajectory tracking. However, due to the increasing complexity of the robot structure and operating environment, it has brought great difficulties to the use of model-based prescribed-time control. To address this issue, a data-driven prescribed-time control approach is proposed, the controller design and convergence proof through mathematical derivations are also provided. Finally, the effectiveness of the proposed control scheme is validated through numerical simulations and industrial robotic manipulator experiments. This research contributes a viable and efficient time-optimal control method for trajectory-tracking tasks in the realm of industrial robotics.

An adaptive fuzzy iterative learning control(AFILC) method is presented to address the state tracking issue of constrained systems with arbitrary initial state errors and unknown control gain. A novel desired error trajectory is systematically developed in the polynomial form to relax the identical initial condition, which allows for arbitrary setting of initial values for all the system state errors. The proposed desired error trajectory can also relax the iteration-invariance restriction on the reference signals due to the independence of the reference trajectories. An asymmetric integral fractional barrier Lyapunov function is developed, keeping the tracking error within the preassigned boundary. Moreover, there is no need to estimate the unknown control gain function in the controller design, reducing computation burden. Numerical simulations and experiments in the permanent magnet synchronous motor experimental platform are provided to illustrate the efficacy of the proposed method. Note to Practitioners—Most practical systems often perform repetitive tasks in industrial processes, such as the repetitive handling process of manipulators, and the rotation process of motors, etc. Iterative learning control method is model independent, and fully utilizes the repetitive characteristics during system operation. However, due to irregular initial state drifts caused by locating operations at different iterations, the identical initial condition is often violated in practical iterative learning control applications. This paper presents an adaptive fuzzy iterative learning control method to address the state tracking issue of constrained systems with arbitrary initial state errors and unknown control gain. The problem of inconsistent initial values is addressed by designing a desired error trajectory in the polynomial form, such that arbitrary setting of initial values for all the system state errors is allowed. For safe operation in practice, an asymmetric integral fractional barrier Lyapunov function is developed to keep the tracking error within the preassigned boundary. The satisfactory experimental results on the permanent magnet synchronous motor experimental platform also demonstrate the practical effectiveness of the proposed method.

In this paper, the robust adaptive learning bipartite consensus problem for heterogeneous nonlinear multiagent systems with unknown control gains, external disturbances, and actuator constraints under signed directed graphs is investigated. A novel neural distributed protocol is proposed, whose key techniques lie in the introduction of deadzone-modified Lyapunov functions and integral Lyapunov functions into the command filtered backstepping design. The former enhances the robustness of the undertaken system by attenuating the impact of complex uncertainties and transforms the robustness problem into a convergence one by introducing the deadzones, while the latter efficiently tackles both the state-dependent control gain functions and the input saturation nonlinearities, simplifying the consensus design significantly. In addition, the requirement of the command filtered design for the control gain functions is relaxed by the appropriate system transformation. Furthermore, the incremental adaptive algorithm takes the place of the integral adaptation for parameter learning, avoiding numerical integration in implementation. The theoretical results of the performance analysis are presented in detail, in which the boundedness of all closed-loop variables is examined, and the asymptotic consensus is achieved, in the sense that the bipartite synchronization error converges to a pre-specified region asymptotically. Numerical results verify the feasibility of the presented scheme. Note to Practitioners—This paper is devoted to the problem of robust adaptive learning bipartite consensus for heterogeneous nonlinear multiagent systems (MASs). It is important to investigate heterogeneous MASs in practical engineering applications, and a typical example is the air-ground cooperative combat system consisting of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs). In addition, the bipartite consensus allows MASs to accomplish more diverse tasks. However, uncertain nonlinearities, external disturbances, and actuator constraints are widespread in system dynamics. Moreover, the unknown control gains make consensus design challenging. These issues are well addressed by adopting the key techniques including deadzone-modified strategy, integral Lyapunov synthesis and incremental adaptive learning mechanism. Furthermore, the proposed scheme guarantees the asymptotic convergence of the bipartite synchronization errors with a pre-specified accuracy, and enhances the system robustness under complex heterogeneous nonlinearities. Therefore, the presented method contributes to practical applications.

Graph clustering technique is highly effective in detecting complex-shaped clusters, in which graph building is a crucial step. Nevertheless, building a reasonable graph that can exhibit high connectivity within clusters and low connectivity across clusters is challenging. Herein, we design a max-ascent-angle graph called the “Y-graph”, a high-sparse graph that automatically allocates dense edges within clusters and sparse edges across clusters, regardless of their shapes or dimensionality. In the graph, every point$x$is allowed to connect its nearest higher-density neighbor$\delta$, and another higher-density neighbor$\gamma$, satisfying that the angle$\angle \delta x\gamma$is the largest, called “max-ascent-angle”. By seeking the max-ascent-angle, points are automatically connected as the Y-graph, which is a reasonable graph that can effectively balance inter-cluster connectivity and intra-cluster non-connectivity. Besides, an edge weight function is designed to capture the similarity of the neighbor probability distribution, which effectively represents the density connectivity between points. By employing the Normalized-Cut (Ncut) technique, a Ncut-Y algorithm is proposed. Benefiting from the excellent performance of Y-graph, Ncut-Y can fast seek and cut the edges located in the low-density boundaries between clusters, thereby, capturing clusters effectively. Experimental results on both synthetic and real datasets demonstrate the effectiveness of Y-graph and Ncut-Y.

In this article, a predefined-time event-triggered attitude consensus control problem is investigated for multiple spacecraft with time-varying state constraints. A backstepping-based control scheme for achieving a consensus in spacecraft attitudes within a predefined time is presented. To enhance the efficiency of communication resource utilization, an event-triggering mechanism is designed by employing a saturation function. This mechanism allows for the application of three existing triggering strategies in multiple spacecraft systems. Then, a novel asymmetric fractional barrier Lyapunov function (BLF) guarantees adherence to time-varying constraints while addressing the issue of excessive control amplitude associated with large constraint boundaries in conventional logarithmic BLFs (LBLFs). This enables the proposed approach to handle both constrained and unconstrained scenarios. The efficacy of the proposed control strategy is illustrated through numerical simulation examples.

This paper proposes a nonlinear feedback control for overhead cranes that offer satisfactory performance by taking advantages of only a composite output. Particularly, the construction of a barrier function keeps the composite output between predefined boundary values, which can enhance the safety of the system. Nonetheless, the controller with simple structure ensures the stabilization of the payload despite the presence of parametric uncertainties. To substantiate the stability proof, two analytical methodologies are employed: the Lyapunov technique and LaSalle’s invariance principle. The simulation evidences efficient positioning and oscillation elimination of the controller for various uncertain parameters, large initial errors and external disturbances, without tuning the gains of each term.

Current methods for achieving fixed-time convergence in continuous sliding mode control are extensively studied, whereas there are limited studies on fixed-step convergence in discrete sliding mode control. The control objective of this paper aims to develop control schemes assuring that the number of convergence steps of the tracking error remains within the desired range, even as the initial value increases, and this paper proposes a guaranteed fixed-step convergence approach to form the reaching law. An upper bound on convergent steps, independent of the initial value, is established, thereby having the fixed-step convergence property . The performance assessment, including attractiveness, invariance, and convergence steps, is provided to meet the required specifications. The steady-state band results offer guidance in selecting parameters to achieve the control objective. To achieve fixed-step convergence of the tracking error, one can develop a dead-beat terminal sliding mode control scheme, where the tracking error is governed by the prescribed error dynamics. To illustrate the approach more concretely, specific designs are proposed for both switching and non-switching reaching laws aimed at ensuring fixed-step convergence. The simulation and experiment results validate the performance evaluation and demonstrate effectiveness of the proposed control schemes.

In this article, the fuzzy adaptive learning bipartite consensus problem is addressed for strict-feedback multiagent systems subject to state constraints under a structurally unbalanced signed graph. An agent hierarchical categorization strategy is suggested, with the assistance of which the requirements on the network topology can be relaxed, and the bipartition of all agents is easily achieved, even if the communication graph is structurally unbalanced. In addition, taking advantage of the treatment with symmetric fractional barrier Lyapunov functions, which transforms asymmetric constrained scenarios into symmetric cases and subsequently into equivalent unconstrained ones, it facilitates the realization of the bipartite consensus under state constraints and the performance analysis is greatly simplified. Furthermore, the fuzzy logic systems are employed to approximate the uncertainties involved in the system. It is shown that the boundedness of all variables of the closed-loop system undertaken and the convergence of consensus errors are established, even for the structurally unbalanced topology graph. Numerical results demonstrate feasibility of the presented consensus scheme.

Since most existing single-prototype clustering algorithms are unsuitable for complex-shaped clusters, many multi-prototype clustering algorithms have been proposed. Nevertheless, the automatic estimation of the number of clusters and the detection of complex shapes are still challenging, and to solve such problems usually relies on user-specified parameters and may be prohibitively time-consuming. Herein, a stable-membership-based auto-tuning multi-peak clustering algorithm (SMMP) is proposed, which can achieve fast, automatic, and effective multi-prototype clustering without iteration. A dynamic association-transfer method is designed to learn the representativeness of points to sub-cluster centers during the generation of sub-clusters by applying the density peak clustering technique. According to the learned representativeness, a border-link-based connectivity measure is used to achieve high-fidelity similarity evaluation of sub-clusters. Meanwhile, based on the assumption that a reasonable clustering should have a relatively stable membership state upon the change of clustering thresholds, SMMP can automatically identify the number of sub-clusters and clusters, respectively. Also, SMMP is designed for large datasets. Experimental results on both synthetic and real datasets demonstrated the effectiveness of SMMP.

Most existing clustering algorithms require presetting cluster number and often fail to capture complex shapes. Herein, we propose a clustering algorithm by pruning a density-boosting cluster tree of density mounts—DEnsity MOuntains Separation clustering algorithm (DEMOS). A cluster is assumed to be a density-connected area with multiple (or a single) density mounts (i.e., single-peak clusters) and a relatively large dis-connectivity from density-connected areas of higher densities. Based on this assumption, DEMOS can easily detect the number of clusters and robustly reconstruct their complex shapes. It first builds the dataset into a peak graph, where each density peak represents a density mount. A multi-valley-link-based connectivity estimation method is embedded to efficiently estimate the connectivity between density peaks during peak graph building. Then, by applying a new linkage metric designed based on our assumption, DEMOS builds density mounts into a reasonably density-boosting cluster tree. After obtaining a robust center detection in a clarity-enhancing decision graph (i.e., a two-dimensional plot for detecting centers), DEMOS prunes the cluster tree into final clusters to finish clustering. Experimental results on both synthetic and real datasets demonstrated the effectiveness of DEMOS and its applicability to large-scale data clustering.

This article addresses the problem of adaptive learning control (ALC) for nonlinear systems in nonstrict-feedback form. For parameter learning, an incremental adaptive mechanism is proposed and used as an alternative to integral adaptation, with which numerical integration in implementation can be avoided. Taking advantage of the error-tracking approach, a novel integral Lyapunov function, developed specifically for tackling state-dependent control gains, is incorporated into the approximation-based backstepping design. In addition, the technical challenges associated with nonstrict-feedback structures are successfully overcome, by employing the key property of neural networks in the ALC design. It is shown that with the aid of the technique lemma for robustness analysis, the proposed ALC control strategy guarantees robust convergence of the tracking error, despite the complex uncertainties involved. The design method guarantees the tracking performance and facilitates the implementation of the suggested algorithm. Illustrative examples are provided which verify the effectiveness of the presented ALC control scheme.

Abstract Automatic classification of diseases in endoscopic images is essential to the improvement of diagnostic performance and the reduction of colorectal cancer mortality. However, due to the ambiguous boundary between background and foreground, abnormal classification in endoscopic images is still challenging. To tackle such a situation, an adaptive aggregation with self‐attention network (AASAN), including a global branch, a local branch, and a fusion branch, is proposed imitating the diagnosis process of endoscopists. On this basis, the self‐attention with relative position encoding (SA‐RPE) module is designed to capture long‐range dependencies and gather lesion neighborhood information. Furthermore, an adaptive aggregation feature (AAF) module is proposed and embedded into the fusion branch for final image label prediction, which is helpful to capture more discriminant features. Extensive experiments show that the classification accuracy of the authors' method on Kvasir public dataset reaches 96.37% in a fivefold cross‐validation, higher than the state‐of‐the‐art deep learning algorithms.

This article concerns with terminal recurrent neural network (RNN) models for time-variant computing, featuring finite-valued activation functions (AFs), and finite-time convergence of error variables. Terminal RNNs stand for specific models that admit terminal attractors, and the dynamics of each neuron retains finite-time convergence. The might-existing imperfection in solving time-variant problems, through theoretically examining the asymptotically convergent RNNs, is pointed out for which the finite-time-convergent models are most desirable. The existing AFs are summarized, and it is found that there is a lack of the AFs that take only finite values. A finitely valued terminal RNN, among others, is taken into account, which involves only basic algebraic operations and taking roots. The proposed terminal RNN model is used to solve the time-variant problems undertaken, including the time-variant quadratic programming and motion planning of redundant manipulators. The numerical results are presented to demonstrate effectiveness of the proposed neural network, by which the convergence rate is comparable with that of the existing power-rate RNN.

Fuzzy c-means (FCM) algorithm as a traditional clustering algorithm for image segmentation cannot effectively preserve local spatial information of pixels, which leads to poor segmentation results with inconsistent regions. For the remedy, superpixel technologies are applied, but spatial information preservation highly relies on the quality of superpixels. Density peak clustering algorithm (DPC) can reconstruct spatial information of arbitrary-shaped clusters, but its high time complexity$O(n^2)$and unrobust allocation strategy decrease its applicability for image segmentation. Herein, a fast density peak clustering method (PGDPC) based on the kNN distance matrix of data with time complexity$O(nlog(n))$is proposed. By using the peak-graph-based allocation strategy, PGDPC is more robust in the reconstruction of spatial information of various complex-shaped clusters, so it can rapidly and accurately segment images into high-consistent segmentation regions. Experiments on synthetic datasets, real and Wireless Capsule Endoscopy (WCE) images demonstrate that PGDPC as a fast and robust clustering algorithm is applicable to image segmentation.

In this article, a neural-network-based adaptive fixed-time control scheme is proposed for the attitude tracking of uncertain rigid spacecrafts. A novel singularity-free fixed-time switching function is presented with the directly nonsingular property, and by introducing an auxiliary function to complete the switching function in the controller design process, the potential singularity problem caused by the inverse of the error-related matrix could be avoided. Then, an adaptive neural controller is developed to guarantee that the attitude tracking error and angular velocity error can both converge into the neighborhood of the equilibrium within a fixed time. With the proposed control scheme, no piecewise continuous functions are required any more in the controller design to avoid the singularity, and the fixed-time stability of the entire closed-loop system in the reaching phase and sliding phase is analyzed with a rigorous theoretical proof. Comparative simulations are given to show the effectiveness and superiority of the proposed scheme.

Liquid desiccant dehumidification system (LDDS) has emerged as an energy-efficient approach for air dehumidification. In this paper, a simple model for the liquid desiccant cooling and dehumidification air conditioning (LDCDAC) system is proposed. The model is built by using artificial neural network (ANN) to describe the cooling, dehumidification and regeneration performance of the LDCDAC system. The system outlet parameters, such as chilled water temperature, air temperature and humidity, can be calculated directly from the inlet parameters. A multilayer neural network is adopted, and the ANN model is trained by the experimental data collected under different operating conditions. The model predictions of the heat and mass transfer rates are compared with the experimental values. The results indicate that the model predicting errors are within ±8%. The proposed model can be used in control and optimization applications of the LDCDAC system.

For practical mechanical systems, uncertainties/disturbances, such as unmodeled dynamics and frictions, are nonignorable factors. For existing control methods, these factors are usually neglected or addressed by a robust way. As a consequence, the nominal control performance of these methods is sacrificed. Moreover, there exists the chattering problem for some existing robust methods, such as sliding mode control laws. To deal with these drawbacks, a continuous global sliding mode controller along with a nonlinear disturbance observer is designed for the regulation and disturbance estimation control of the overhead crane system. Specifically, the original crane dynamic model is transformed into a quasi-integrator-chain form through some transformations. Then, a nonlinear disturbance observer is designed and a continuous global sliding mode control method is introduced on the basis of the constructed disturbance observer. The stability and convergence characteristics are proven through rigorous theoretical analysis. Finally, to demonstrate the performance of the designed controller, a series of experimental tests are performed, and a comparison study between the devised method here and an existing method is given. Note to Practitioners-This article is motivated by the desire to deal with the regulation and disturbance rejection of the overhead crane system. In practical applications, uncertainties/disturbances are unavoidable problems for overhead cranes. For most existing methods, these issues are usually addressed in a robust way. To handle these existing problems, a nonlinear disturbance observer and a continuous global sliding mode controller are proposed for the regulation and disturbance estimation control of the overhead crane system. The disturbance observer is introduced to estimate and compensate for uncertain disturbances, and the sliding mode controller is designed to guarantee the convergence of the state variables of the closed-loop system. In the future, we will try to apply this method to practical overhead cranes.

This paper deals with the design of a sensing matrix along with a sparse recovery algorithm by utilizing the probability-based prior information for compressed sensing systems. With the knowledge of the probability for each atom of the dictionary being used, a diagonal weighted matrix is obtained and then the sensing matrix is designed by minimizing a weighted function such that the Gram of the equivalent dictionary is as close to the Gram of dictionary as possible. An analytical solution for the corresponding sensing matrix is derived that requires low computational complexity. We also exploit this prior information through the sparse recovery stage and propose a probability-driven orthogonal matching pursuit algorithm that improves the accuracy of the recovery. Simulations for synthetic data and application scenarios of video streaming are carried out to compare the performance of the proposed methods with some existing algorithms. The results reveal that the proposed compressed sensing (CS) approach outperforms existing CS systems.

Based on the maximum likelihood estimation principle, we derive a collaborative estimation framework that fuses several different estimators and yields a better estimate. Applying it to compressive sensing (CS), we propose a collaborative CS (CCS) scheme consisting of a bank of $K$ CS systems that share the same sensing matrix but have different sparsifying dictionaries. This CCS system is expected to yield better performance than each individual CS system, while requiring the same time as that needed for each individual CS system when a parallel computing strategy is used. We then provide an approach to designing optimal CCS systems by utilizing a measure that involves both the sensing matrix and dictionaries and hence allows us to simultaneously optimize the sensing matrix and all the $K$ dictionaries. An alternating minimization-based algorithm is derived for solving the corresponding optimal design problem. With a rigorous convergence analysis, we show that the proposed algorithm is convergent. Experiments are carried out to confirm the theoretical results and show that the proposed CCS system yields significant improvements over the existing CS systems in terms of the signal recovery accuracy.

This study deals with the issue of designing the sensing matrix for a compressed sensing (CS) system assuming that the dictionary is given. Traditionally, the measurement of small mutual coherence is considered to design the optimal sensing matrix so that the Gram of the equivalent dictionary is as close to the target Gram as possible, where the equivalent dictionary is not normalised. In other words, these algorithms are designed to solve the CS problem using an optimisation stage followed by normalisation. To achieve a global solution, a novel strategy of the sensing matrix design is proposed by using a gradient‐based method, in which the measure of real mutual coherence for the equivalent dictionary is considered. According to this approach, a minimised objective function based on alternating minimisation is also developed through searching the target Gram within a set of relaxed equiangular tight frames. Some experiments are done to compare the performance of the newly designed sensing matrix with the existing ones under the condition that the dictionary is fixed. For the simulations of synthetic data and real image, the proposed approach provides better signal reconstruction accuracy.

To increase the transportation efficiency and ensure the safety of the crane system, the trolley/bridge is required to be driven to reach different preset destinations without retuning control gains of the controller, while the maximum payload swing amplitudes need to be similar for different transportation processes of different distances. Motivated by the desire to achieve these objectives, based on the energy shaping methodology and passivity-based control (PBC), an enhanced coupling control method is derived for three-dimensional (3-D) underactuated overhead cranes. Specifically, on the basis of the energy shaping methodology and PBC, a constructive storage function is constructed by solving partial differential equations. Then, a novel enhanced coupling control scheme enforcing the dissipation inequality with respect to the constructed storage function is investigated straightforwardly, and the corresponding stability analysis is proven by Lyapunov techniques and LaSalle's invariance theorem. Finally, the feasibility and effectiveness of the proposed method is demonstrated by digital simulations and experimental tests.

In this paper, a distributed compressed estimation (DCE) scheme is presented based on a distributed recursive-least squares algorithm for sparse signals and systems along with a sensing matrix design procedure based on compressive sensing techniques. The D-CE scheme consists of compression and decompression modules inspired by compressive sensing to perform distributed compressed estimation. A design procedure is developed under the DCE framework and a novel algorithm is developed to optimize the sensing matrix, which can further improve the performance of the proposed DCE and distributed adaptive algorithms. Simulations for a wireless sensor network show the advantages of the proposed scheme and algorithm in terms of convergence rate and mean square error performance.

This paper deals with the sensing matrix optimization problem for compressed sensing (CS) systems. Traditionally, the optimal sensing matrix is designed such that the Gram of the equivalent dictionary defined as the product of the sensing matrix and the dictionary is as close to a target Gram with some proper properties as possible. In this study, the sensing matrix is designed to make the equivalent dictionary approximate to a certain target frame. In addition, to avoid the sparse representation error (SRE) to be amplified in the measurement domain, a penalty term related to the SRE is included in the design criterion. An alternating minimization algorithm is proposed to solve the optimum sensing matrix problem, where the target frame is taken as the relaxed equiangular tight frame, which is constructed with a new method with the purpose of reducing the mutual coherence and maintaining the tightness of the frame, then the solution of the optimal sensing matrix is derived analytically with the target frame fixed. Experiments are carried out with synthetic data and real images, which demonstrate promising performance of the proposed algorithms and superiority of the CS system designed with the optimized sensing matrix to existing ones in terms of signal reconstruction accuracy.

This paper deals with designing sensing matrix for compressive sensing systems. Traditionally, the optimal sensing matrix is designed so that the Gram of the equivalent dictionary is as close as possible to a target Gram with small mutual coherence. A novel design strategy is proposed, in which, unlike the traditional approaches, the measure considers of mutual coherence behavior of the equivalent dictionary as well as sparse representation errors of the signals. The optimal sensing matrix is defined as the one that minimizes this measure and hence is expected to be more robust against sparse representation errors. A closed-form solution is derived for the optimal sensing matrix with a given target Gram. An alternating minimization-based algorithm is also proposed for addressing the same problem with the target Gram searched within a set of relaxed equiangular tight frame Grams. The experiments are carried out and the results show that the sensing matrix obtained using the proposed approach outperforms those existing ones using a fixed dictionary in terms of signal reconstruction accuracy for synthetic data and peak signal-to-noise ratio for real images.

In this paper, we propose a new method to optimize the sensing matrix and the overcomplete dictionary simultaneously in a block-sparse system. This method mainly includes two parts: the optimization of the sensing matrix for a given dictionary and the optimization of the overcomplete dictionary with a block structure for a predefined sensing matrix. Simulation results show that our novel method can significantly improve the dictionary recovery ability and lower the representation error compared with other dictionary learning methods in block-sparse systems.

As shown in [9], for a state-space realization (A,B,C,d) of a digital filter there are no self-sustained oscillations (i.e., limit cycles) if there exists some diagonal matrix D ≫ 0 such that D-ATDA ≥ 0. Based on this result, we show that the optimal roundoff realizations are free of limit cycles. For any given realizations, a method is proposed to check the existence of such a D. A novel class of robust state-space realizations is derived and characterized, which are free of limit cycles and yield a minimal error propagation gain. The optimal realization problem for this class of realizations is formulated and solved analytically. Two examples are presented to test the efficiency of the proposed method and to demonstrate the behavior of the obtained optimal realization.