This paper tackles the consensus tracking control challenge for a category of uncertain nonlinear multi-agent systems subject to unknown time-varying disturbances and output constraints. Even if only a portion of followers can directly receive the leader’s information, by developing a new barrier Lyapunov function and integrating it with the backstepping technique, a distributed robust adaptive state feedback controller is developed, which not only guarantees that the system outputs remain within prescribed constraints but also ensures that the output error between any two followers and the tracking error converge to the origin asymptotically. Neither control design nor theoretical analysis relies on eigenvalue information of Laplacian, thus eliminating the demand for global information and enhancing the utilization efficiency of local communication resources. The effectiveness of the control strategy is ultimately demonstrated through a practical example of single-link manipulators.

The increasingly mature visual feedback technology has greatly promoted the development of high-altitude unmanned operations, particularly in bolt-tightening operations for the power transmission tower. Nevertheless, traditional control algorithms relying on visual feedback encounter specific difficulties and challenges during bolt-tightening tasks execution. This paper presents a disturbance observer-based finite-time control algorithm to reject disturbances during bolt-tightening operations and mechanical errors inherent to the robotic system. Through theoretical analysis of this algorithm, based on visual feedback technology and the finite-time control method, the finite-time stability of the overall closed-loop system is ensured. Both the simulation and experimental results are given to validate the feasibility of the proposed control method. Compared with traditional PID, LQR, and MPC controllers, the convergence speed is increased by more than 16%, and the accuracy of steady-state error is also increased by more than 22%, moreover, the success rate is as high as 98%. It is highlighted that the proposed control method has superior disturbance rejection capability, faster convergence speed, and better reliability. Note to Practitioners—This study addresses challenges in visual feedback-dependent bolt-tightening operations for power transmission towers. A novel disturbance observer-based finite-time control algorithm is introduced, ensuring the stability of the closed-loop system. The method excels in rejecting disturbances and minimizing mechanical errors during bolt-tightening tasks. The simulation and experimental results confirm its superiority over conventional controllers in terms of disturbance rejection, convergence rate, and operational precision. Practitioners should consider the algorithm’s potential to enhance the efficiency and reliability of bolt-tightening in high-altitude unmanned operations.

This paper investigates event-triggered prescribed-time stabilization for time-varying nonlinear systems. The motivation arises from three challenging issues: the singularity resulting from infinite control gains at the prescribed time instant, the management of infinity of implicit time variation, and trade-off between control effort and triggering intervals. Using a delicate trick that the finite value of a new time-varying function remains unchanged once all state variables of the system hit zero, we create an event-triggered strategy incorporating a sophisticated switching trigger rule equipped with a time-dependent threshold, based on the continuous feedback domination method with a series of integral functions containing nested sign functions. Better than existing results on prescribed-time stabilization, the scheme presented in this paper not only guarantees that states converge to zero precisely within the prescribed time and sustains non-truncated controller operation, but also uniquely tackles the prevention of the Zeno phenomenon. At last, the stabilization of tunnel diode circuit is conducted to confirm the validity and the effectiveness of our strategy.

This article explores the problem of prescribed-time stabilization for a class of high-order polynomial nonlinear systems with unknown time-varying nonlinearities. The key technique behind the proposed strategy involves fixing the time-varying components to their bounded values before the prescribed time and establishing a new lemma to suppress the time-varying continuous functions in the investigated system. We design a continuous bounded feedback controller to address the singularities induced by infinite control gains at the prescribed time and to suppress the implicit effects of time variations. Superior to the existing prescribed-time stabilization results, our strategy achieves the states' convergence within the prescribed-time and the nontruncated run of controller simultaneously. We employ the wing rock motion to demonstrate the practicality and superiority of the developed strategies.

This study focuses on addressing the challenge of global output feedback control problem for a class of time-varying stochastic nonlinear systems subject to multiple uncertainties. The primary challenge concerns how to construct time-varying functions to counteract the effects of unmeasurable error coming from system output as well as the persistently increasing nonlinearities. By employing a full-order state observer and the dual gain approach, we design an output feedback regulator over the entire time domain to guarantee the existence and uniqueness of the closed-loop system’s solution and the almost sure asymptotic convergence of the state. This methodology achieves both the domination of the unknown growth rate and the unified system design, irrespective of sensor sensitivity. Finally, practical and numerical simulation examples demonstrate the feasibility of the presented approach.

This article is concerned with the global fast finite-time adaptive stabilization for a class of high-order uncertain nonlinear systems in the presence of serious nonlinearities and constraint communications. By renovating the technique of continuous feedback domination to the construction of a serial of integral functions with nested sign functions, this article first proposes a new event-triggered strategy consisting of a sharp triggered rule and a time-varying threshold. The strategy guarantees the existence of the solutions of the closed-loop systems and the fast finite-time convergence of original system states while reaching a compromise between the magnitude of the control and the trigger interval. Quite different from traditional methods, a simple logic is presented to avoid searching all the possible lower bounds of trigger intervals. An example of the maglev system and a numerical example are provided to demonstrate the effectiveness and superiority of the proposed strategy.

The work is centered on developing a constructive method of synthesizing an output-feedback second-order sliding-mode (SOSM) controller. By leveraging a coordinate transformation and revamping the technique of adding a power integrator, a state-feedback SOSM controller is first constructed under the full-state measurement. In order to tackle the challenge posed by the unmeasurable first derivative of the sliding variable, a discontinuous observer with appropriately selected scaling gains is put forward to overcome the measurable lack. Through the interactive cooperation of the state-feedback SOSM controller and the discontinuous observer, an output-feedback SOSM controller is successfully designed without reliance upon the separation principle. The finite-time convergence of the whole system is rigorously validated by dint of the Lyapunov function-based analysis. The efficiency of the theoretical results is ultimately corroborated through a simulation study.

This article aims to solve two intricate problems in nonlinear control: 1) the zero-division of the control by requiring its differentiability and 2) the finite-time stabilization via adaptive feedback for a class of uncertain nonlinear systems with asymmetric output constraints. The issue is how to control the system states to converge to the origin quickly while not violating the output constraints. This article develops an adaptive stabilizing controller constituting a piecewise tangent-type barrier function and a series of non-negative integral functions with sign functions, which is bounded over the whole time horizon and ensures the fast convergence of the system states. The innovation is two-fold: a technical lemma is proposed for the first time to make the designed controller completely decoupled from the first state variable of the system. The proposed strategy can handle both constrained and unconstrained systems without reconstructing barrier functions associated with the output constraint. Finally, the stabilization of a liquid-level system is investigated to demonstrate the effectiveness of the control scheme.

This article develops a new controller design approach to stabilize system states onto the equilibrium at an arbitrarily selected time instant irrespective of the initial system states and parameters. By the stabilization approach, the actual convergence time (not the bound of actual convergence time) is independent of the initial value of system states. This feature differentiates our proposed prescribed-instant stability from conventional fixed, predefined, and prescribed time stability. In this work, we propose the controller design method for the prescribed-instant stability of n -order integrator systems. The proposed control is bounded and can gradually go to zero at an arbitrarily selected time instant, at which the system states reach zero simultaneously. This special stability of the controlled system is analyzed by reduction to absurdity. In simulations, an example of comparison with frequently used prescribed-time control is presented to show the difference. Moreover, the proposed stabilization method is validated by a magnetic suspension system with matched disturbances.

The consensus problem for second-order multiagent systems based on nonsmooth sampled-data control is considered. First, a continuous-time nonsmooth consensus protocol is proposed, which can realize the consensus of systems in a finite time when the external disturbance is absent. Next, based on the sampled data and the zero-order holder, a new discrete-time nonsmooth protocol is proposed. Considering external disturbances, the explicit relationship between the ultimate boundary of errors of any two agents and the sampling period and external disturbance is given with the Lyapunov method and graph theory, which theoretically shows that the nonsmooth control algorithm has a stronger ability to resist external disturbance than the smooth control algorithm. Finally, a simulation example shows the superiority of the nonsmooth consensus algorithm over a smooth consensus algorithm.

In this article, the problem of global fixed-time stabilization for a class of high-order uncertain nonlinear systems has been investigated. Quite different from traditional methods, a novel finite-time control scheme is presented for the first time based on a serial of exponential functions and fractional power integration with nested sign functions, which can guarantee that the convergent time of the states of the closed-loop systems is finite and independent of any initial conditions by the simple choice of design parameters. The remarkable contribution of this article lies in the fact that it provides an alternative to manipulate the possibility of initial states being far from the origin. As a practical application, the finite-time stabilizing design of maglev systems is provided to demonstrate the effectiveness and the superiority of the proposed strategy.

The work has devised a novel fixed-time control scheme for a class of nonlinear systems with output constraints. Two distinctive features are incorporated into the gained results. One is that the upper bound of settling time for the closed-loop system can be estimated without dependence on system initial states, and hence, can be acquired arbitrarily small through tuning control design parameters. The other is that the output constraint can be handled by means of a barrier Lyapunov function (BLF). Via the BLF and the backstepping-like technique, a fixed-time controller, which can cope with a type of nonlinear systems concurrently with and without output constraints, is systematically built. The rigorous analysis on the strength of the Lyapunov theory is given to show that the investigated system under the proposed controller is fixed-time stable, and the violation of a preset output constraint is averted. The case studies of a series elastic actuator system are offered to substantiate the derived theoretical results.

We present the Maestro memory-on-logic 3D-IC architecture for coordinated parallel use of a plurality of systolic arrays (SAs) in performing deep neural network (DNN) inference. Maestro reduces under-utilization common for a single large SA by allowing parallel use of many smaller SAs on DNN weight matrices of varying shapes and sizes. In order to buffer immediate results in memory blocks (MBs) and provide coordinated high-bandwidth communication between SAs and MBs in transferring weights and results Maestro employs three innovations. (1) An SA on the logic die can access its corresponding MB on the memory die in short distance using 3D-IC interconnects, (2) through an efficient switch based on H-trees, an SA can access any MB with low latency, and (3) the switch can combine partial results from SAs in an elementwise fashion before writing back to a destination MB. We describe the Maestro architecture, including a circuit and layout design, detail scheduling of the switch, analyze system performance for real-time inference applications using input with batch size equal to one, and showcase applications for deep learning inference, with ShiftNet for computer vision and recent Transformer models for natural language processing. For the same total number of systolic cells, Maestro, with multiple smaller SAs, leads to 16x and 12x latency improvements over a single large SA on ShiftNet and Transformer, respectively. Compared to a floating-point GPU implementation of ShiftNet and Transform, a baseline Maestro system with 4,096 SAs (each with 8x8 systolic cells) provides significant latency improvements of 30x and 47x, respectively.

This study investigates the hybrid design by using the Takagi-Sugeno (T-S) fuzzy system modeling method and the Terminal Sliding Mode Control (TSMC) technique. The combined scheme is shown to have the merits of both approaches. The presented scheme can alleviate the on-line computational burden because T-S fuzzy model can approximate the original nonlinear system and some of the parameters can be off-line computed. Moreover, it can also preserve the advantages of TSMC, including rapid response, robustness to uncertainties and/or external disturbance, and guaranteeing the fast finite-time state convergence. The proposed method is applied to a two-link robot manipulator dynamics, and it is also compared to the combination of T-S fuzzy system and conventional Sliding Mode Control (SMC) design. Simulation results demonstrate the benefits of the proposed scheme.

This paper explores the design of active reliable control systems for a class of uncertain nonlinear affine systems using an integral-type sliding mode control (ISMC) scheme. The presented scheme not only maintains the main advantages of the ISMC design, including robustness, rapid response and easy implementation, but it can also tolerate some actuator faults when fault detection and diagnosis information is available. In this study, the uncertainties and/or disturbances are not required to be of the matched type; however, when they are matched, the state trajectories of the nominal healthy subsystem and the uncertain faulty system are identical. As a result, engineers can predictively address the matched uncertain faulty system performance in light of the performance of the nominal healthy subsystem. The analytic results are also applied to the study of a vehicle brake reliable control system. Simulation results demonstrate the benefits of the proposed scheme.

This paper presents a novel segmentation algorithm to segment a body posture into different body parts using the technique of deformable triangulation. To analyze each posture more accurately, they are segmented into triangular meshes, where a spanning tree can be found from the meshes using a depth-first search scheme. Then, we can decompose the tree into different subsegments, where each subsegment can be considered as a limb. Then, two hybrid methods (i.e., the skeleton-based and model-driven methods) are proposed for segmenting the posture into different body parts according to its occlusion conditions. To analyze occlusion conditions, a novel clustering scheme is proposed to cluster the training samples into a set of key postures. Then, a model space can be used to classify and segment each posture. If the input posture belongs to the nonocclusion category, the skeleton-based method is used to divide it into different body parts that can be refined using a set of Gaussian mixture models (GMMs). For the occlusion case, we propose a model-driven technique to select a good reference model for guiding the process of body part segmentation. However, if two postures' contours are similar, there will be some ambiguity that can lead to failure during the model selection process. Thus, this paper proposes a tree structure that uses a tracking technique so that the best model can be selected not only from the current frame but also from its previous frame. Then, a suitable GMM-based segmentation scheme can be used to finely segment a body posture into the different body parts. The experimental results show that the proposed method for body part segmentation is robust, accurate, and powerful.

This paper presents a novel posture classification system that analyzes human movements directly from video sequences. In the system, each sequence of movements is converted into a posture sequence. To better characterize a posture in a sequence, we triangulate it into triangular meshes, from which we extract two features: the skeleton feature and the centroid context feature. The first feature is used as a coarse representation of the subject, while the second is used to derive a finer description. We adopt a depth-first search (dfs) scheme to extract the skeletal features of a posture from the triangulation result. The proposed skeleton feature extraction scheme is more robust and efficient than conventional silhouette-based approaches. The skeletal features extracted in the first stage are used to extract the centroid context feature, which is a finer representation that can characterize the shape of a whole body or body parts. The two descriptors working together make human movement analysis a very efficient and accurate process because they generate a set of key postures from a movement sequence. The ordered key posture sequence is represented by a symbol string. Matching two arbitrary action sequences then becomes a symbol string matching problem. Our experiment results demonstrate that the proposed method is a robust, accurate, and powerful tool for human movement analysis.