The invariant ellipsoid methodology focuses on minimizing the invariant/attractive set for a linear control system subjects to bounded external disturbances. In this note, the invariant ellipsoid methodology is adapted to multiagent systems (MASs) by leveraging the generalized homogeneous control. A necessary and sufficient condition for the optimal rejection of external disturbances using a homogeneous control protocol is presented. Compared to linear control protocols, the generalized homogeneous approach yields faster convergence and enhanced accuracy. Theoretical results are validated by the numerical simulations of the multiagent system comprised of unicycle mobile robots (UMRs).

Enhancing system security under denial-of-service (DoS) attacks requires robust compensation mechanisms. However, existing model-free adaptive control-based compensation solutions are limited to constant reference signals and neglect control optimization, causing insufficient tracking performance in dynamic attacks. This study develops a data-driven adaptive dynamic programming (ADP) resilient control scheme for networked control system under aperiodic DoS attacks. An ADP method with a modified performance index is proposed to derive a globally optimal controller, while a dynamic penalty factor is introduced to accelerate error convergence. Leveraging ADP technology and the latest available control increments, a compensation mechanism for time-varying reference signals is designed to reduce performance degradation. Finally, theoretical proofs ensure error convergence, and comparative simulations verify the strategy's superiority.

This paper presents a novel vision-based artificial lateral line (ALL) sensor, FlowSight, enhancing the perception capabilities of underwater robots. Through an autonomous vision system, FlowSight allows for simultaneous sensing the speed and direction of local water flow without relying on external auxiliary equipment. Inspired by the lateral line neuromast of fish, a flexible bionic tentacle is designed to sense water flow. Deformation and motion characteristics of the tentacle are modeled and analyzed using bidirectional fluid-structure interaction (FSI) simulation. Upon contact with water flow, the tentacle converts water flow information into elastic deformation information, which is captured and processed into an image sequence by the autonomous vision system. Subsequently, a water flow perception method based on deep neural networks is proposed to estimate the flow speed and direction from the captured image sequence. The perception network is trained and tested using data collected from practical experiments conducted in a controllable swim tunnel. Finally, the FlowSight sensor is integrated into the bionic underwater robot RoboDact, and a closed-loop motion control experiment based on water flow perception is conducted. Experiments conducted in the swim tunnel and water pool demonstrate the feasibility and effectiveness of FlowSight sensor and the water flow perception method.

A detailed modeling approach that utilizes the virtual work idea is developed for modeling the dynamical formulas of the rigid-flexible coupled robotic mechanisms (RFCRMs) with large beam-deformations across the horizontal plane. To follow the required angular positions of RFCRMs, a virtual robust linear quadratic state feedback (RLQSF) input is constructed using the converted full-actuated model in conjunction with an event-triggered robust adaptive fault-tolerant control (ETRAFTC) approach. The integration of virtual input and the proposed RLQSF law design enables simultaneous angular tracking and vibration elimination. To make up for the defective actuators with part loss of efficacy and evaluate the unknown fault parameters, an adaptive estimation law with a projection mapping operator is adopted. With the help of the Lyapunov direct approach, the angular position tracking errors and the flexible vibration of RFCRMs are demonstrated to converge to a tiny confined compact set with fewer communications. At last, the performance of the designed ETRAFTC is presented via three numerical scenarios.

Soft slender robots have attracted more and more research attentions in these years due to their continuity and compliance natures. However, mechanics modeling for soft robots interacting with environment is still an academic challenge because of the non-linearity of deformation and the non-smooth property of the contacts. In this work, starting from a piece-wise local strain field assumption, we propose a nonlinear dynamic model for soft robot via Cosserat rod theory using Newtonian mechanics which handles the frictional contact with environment and transfer them into the nonlinear complementary constraint (NCP) formulation. Moreover, we smooth both the contact and friction constraints in order to convert the inequality equations of NCP to the smooth equality equations. The proposed model allows us to compute the dynamic deformation and frictional contact force under common optimization framework in real time when the soft slender robot interacts with other rigid or soft bodies. In the end, the corresponding experiments are carried out which valid our proposed dynamic model.

Recently soft robotics has rapidly become a novel and promising area of research with many designs and applications due to their flexible and compliant structure. However, it is more difficult to derive the nonlinear dynamic model of such soft robots. The differential kinematics and dynamics of the soft manipulator can be formulated as a set of highly nonlinear partial differential equations (PDEs) via the classic Cosserat rod theory. In this work, we propose a discrete modeling technique named piecewise linear strain (PLS) to solve the PDEs of Cosserat-based models, based on which the associated analytic models are deduced. To validate the accuracy of the proposed Cosserat model, the static model of the conical cantilever rod under gravity as a simple example is simulated by using different discretization methods. Results indicate that PLS Cosserat model is comparable to the mechanical deformation behavior of a real-world soft manipulator. Finally, a parameters identification scheme for this model is established, and the simulation as well as experimental validation demonstrate that using this method can identify the model physical parameters with high accuracy.

Researching networks' theoretical properties and behavior have drawn considerable attention from the perspective of ordinary differential equation (ODE). For solving ODE, explicit and implicit Euler schemes are the most common methods. Some works utilize explicit Euler theory to analyze and design networks. However, focusing on parameters convergence and system stability, implicit Euler has been proved to be better than explicit one. It motivates us to explore implicit Euler's potential in neural networks. In this paper, we establish connections between implicit Euler and networks, which also effectively explain some existing networks such as LISTA and DRRN. In addition, by re-deriving implicit Euler, we propose an adaptive implicit network (AIM-NET) which allows model to have a flexible convergence interval to ensure parameters convergence as well as model performance. Particularly, we obtain AIM-LISTA and AIM-DRRN by applying AIM-NET on LISTA and DRRN respectively. Finally, we perform experiments on both synthetic data and real images and the experimental results show that adaptive implicit structure is able to significantly improve performance.

A novel scheme for an "upgrade" of a linear control algorithm to a non-linear one is developed based on the concepts of a generalized homogeneity and an implicit homogeneous feedback design. Some tuning rules for a guaranteed improvement of a regulation quality are proposed. Theoretical results are confirmed by real experiments with the quadrotor QDrone of Quanser™.

This paper presents design of a robust controller with disturbance compensation for an indoor blimp robot and its realization. The movement of blimp in horizontal plane is modeled as a slider-like nonlinear system complemented with uncertain bounded disturbances. To design the output feedback controller, a homogeneous differentiator is used as an observer. Then the method for disturbance evaluation is designed, the perturbation estimate is next used in the controller for cancellation of the influence of exogenous disturbances. Control scheme is implemented on a concrete blimp, finally, the performance of blimp disturbance compensation based controller is verified in experiments.

Soft robot is an emergent research field which has variant promising applications. However, the design of soft robots nowadays still follows the trial-and-error process, which is not at all efficient. This paper proposes to design soft robots by pre-checking controllability during the numerical design phase. Finite-element method is used to model the dynamics of silicone soft robots, based on which the differential geometric method is applied to analyze the controllability of the points of interest. Such a verification is also investigated via model order reduction technique and Galerkin projection. The proposed methodology is finally validated by numerically designing a controllable parallel soft robot.

Modular Robot Manipulators are user-configurable manipulators which provide rapid design and inexpensive implementation. To be easy-use, smart actuators embedded with position input and position feedback controller are adopted, these local controllers render the manipulators position controlled, but also result in limited performance and precision. This paper targets the case that the built-in controller does not provide desirable precision for set-point regulation. Firstly a joint-level model is established, of which the nominal model can be identified with derivative observer based on the position feedback, then an auxiliary adaptive controller coping with parametric uncertainty is proposed which leads to an error close to zero, a switching control strategy is introduced considering the actuator saturation. The proposed control method is implemented on a 5-DOF modular manipulator, with comparison to classic integral controller.

Armed with structures, group sparsity can be exploited to extraordinarily improve the performance of adaptive estimation. In this letter, the adaptive estimation algorithm for cluster structured sparse signals, called A-CluSS, is proposed. In particular, a hierarchical Bayesian model is built, where both sparse prior and cluster structured prior are exploited simultaneously. The adaptive updating formulas for statistical variables are obtained via the variational Bayesian inference and the resulted algorithms can adaptively estimate the cluster structured sparse signals without knowledge of block size, block numbers and block locations. Superiority of proposed A-CluSS is demonstrated via various simulations.

This paper proposes a motion planning approach for non-holonomic mobile robots. Firstly, motion planning using i-PID controller is presented. Then we improve the old potential field function to produce smooth repulsive force. Finally a new repulsive function of robot orientation and angular velocity is proposed to improve the performance of obstacle avoidance. The effectiveness and the robustness of the proposed method are shown thereafter via several simulations.

An intelligent PID controller (i-PID controller) is applied to control the nonholonomic mobile robot with measurement disturbance. Because of the particularity of the nonholonomic systems, this paper propose to use a switching parameter α in the i-PID controller. We show in simulations that the proposed method is able to control the nonholonomic mobile robots with measurement disturbance, and it can also stabilize the robot at a static point.

In traditional framework of Compressive Sensing (CS), only sparse prior on the property of signals in time or frequency domain is adopted to guarantee the exact inverse recovery. Besides sparse prior, cluster prior is introduced in this paper in order to investigate a class of structural sparse signals, called clustered sparse signals. A hierarchical statistical model is employed via Bayesian approach to model both the sparse prior and cluster prior and Markov Chain Monte Carlo (MCMC) sampling is implemented for the inference. Unlike the state-of-the-art algorithms based on the cluster prior, the proposed algorithm solves the inverse problem without any prior knowledge of the cluster parameters, even without the knowledge of the sparsity. The experimental results show that the proposed algorithm outperforms many state-of-the-art algorithms.

Compressive sensing is a new methodology to capture signals at sub-Nyquist rate. To guarantee exact recovery from compressed measurements, one should choose specific matrix, which satisfies the Restricted Isometry Property (RIP), to implement the sensing procedure. In this letter, we propose to construct the sensing matrix with chaotic sequence following a trivial method and prove that with overwhelming probability, the RIP of this kind of matrix is guaranteed. Meanwhile, its experimental comparisons with Gaussian random matrix, Bernoulli random matrix and sparse matrix are carried out and show that the performances among these sensing matrix are almost equal.