Model-free finite-time robust control using fractional-order ultra-local model and prescribed performance sliding surface for upper-limb rehabilitation exoskeleton.

To address the trajectory tracking issue of upper-limb rehabilitation exoskeleton with uncertainties and external disturbances, this paper proposes a fractional-order ultra-local model-based model-free finite-time robust controller (FO-FTRC) using predefined performance sliding surface. Different from previous model-free control strategies, a novel multi-input multi-output (MIMO) fractional-order ultra-local model which is a virtual model is proposed to approximate the complex uncertain nonlinear exoskeleton dynamics in a short sliding time window. This allows the design of controller to be independent of any exoskeleton model information and reduces the difficulty of controller design. The developed robust model-free control method incorporates a fractional-order quasi-time delay estimator (FO-QTDE), unknown disturbance estimator (UDE) as well as prescribed performance sliding mode control (PPSMC). The FO-QTDE is utilized to estimate the unknown lumped uncertainties which employs short time delayed knowledge only about the control input. However, the low-pass filter is always added for FO-QTDE when disturbances change fast, which leads to unavoidable estimation error. Then, UDE is designed to further eliminate the estimation error of FO-QTDE to enhance control performance. The PPSMC is constructed to converge sliding surface to zero in a finite time. Besides, the sliding surface is always limited in performance boundaries. After that, the overall system stability and convergence analyses are demonstrated by using the Lyapunov theorem. Finally, with the comparison to other methods of α-variable adaptive model free control (α-AMFC), time-delay estimation-based continuous nonsingular fast terminal sliding mode controller (TDE-CNFTSMC), time delay estimation (TDE)-based model-free fractional-order nonsingular fast terminal sliding mode control (MFF-TSM) and fractional-order proportion-differential (PDß), the co-simulation results on 7-degree-of-freedom (DOF) iReHave upper-limb exoskeleton virtual prototype and experiment results on 2-DOF upper-limb exoskeleton are obtained to illustrate the effectiveness and superiority of the proposed FO-FTRC method.

Improved Optimization Strategy Based on Region Division for Collaborative Multi-Agent Coverage Path Planning.

In this paper, we investigate the algorithms for traversal exploration and path coverage of target regions using multiple agents, enabling the efficient deployment of a set of agents to cover a complex region. First, the original multi-agent path planning problem (mCPP) is transformed into several single-agent sub-problems, by dividing the target region into multiple balanced sub-regions, which reduces the explosive combinatorial complexity; subsequently, closed-loop paths are planned in each sub-region by the rapidly exploring random trees (RRT) algorithm to ensure continuous exploration and repeated visits to each node of the target region. On this basis, we also propose two improvements: for the corner case of narrow regions, the use of geodesic distance is proposed to replace the Eulerian distance in Voronoi partitioning, and the iterations for balanced partitioning can be reduced by more than one order of magnitude; the Dijkstra algorithm is introduced to assign a smaller weight to the path cost when the geodesic direction changes, which makes the region division more "cohesive", thus greatly reducing the number of turns in the path and making it more robust. The final optimization algorithm ensures the following characteristics: complete coverage of the target area, wide applicability of multiple area shapes, reasonable distribution of exploration tasks, minimum average waiting time, and sustainable exploration without any preparation phase.

Transcriptional and Physiological Analysis Reveal New Insights into the Regulation of Fertilization (N, P, K) on the Growth and Synthesis of Medicinal Components of Dendrobium denneanum.

Dendrobium denneanum is an important medicinal and ornamental plant. Its ornamental and medicinal values are affected by its vegetative growth conditions and chemical composition accumulation. This study adopted an orthogonal experimental design to treat D. denneanum with nine different levels of nitrogen (N), potassium (K), and phosphorus (P). The morphological indicators of the plant were positively correlated with the nitrogen concentration. The polysaccharide content was the highest at 1500 mg·L-1 nitrogen and 3000 mg·L-1 phosphorous and was 26.84% greater than the control. The flavonoid content increased by 36.2% at 500 mg·L-1 nitrogen, 2000 mg·L-1 phosphorous, and 300 mg·L-1 potassium. Principal component score analysis showed that nitrogen had the most significant impact on the various indicators of D. denneanum, followed by phosphorus and potassium. The comprehensive score showed that the T9 treatment (N: 1500 mg·L-1, P: 3000 mg·L-1, K: 500 mg·L-1) had the strongest effect on D. denneanum. Transcriptional analysis showed that compared with the control, the T9 treatment led to 2277 differentially expressed genes (1230 upregulated and 1047 downregulated). This includes fifteen genes enriched in the MAPK signaling pathway, five genes in phenylpropanoid biosynthesis, and two genes in flavonoid biosynthesis. These genes may be involved in regulating plant growth and the biosynthesis of polysaccharides and flavonoids. This study provides guidance for the optimal use of N, P, and K in the cultivation of D. denneanum.

Fixed-Time Coverage Control of Mobile Robot Networks Considering the Time Cost Metric.

In this work, we studied the area coverage control problem (ACCP) based on the time cost metric of a robot network with an input disturbance in a dynamic environment, which was modeled by a time-varying risk density function. A coverage control method based on the time cost metric was proposed. The area coverage task that considers the time cost consists of two phases: the robot network is driven to cover the task area with a time-optimal effect in the first phase; the second phase is when the accident occurs and the robot is driven to the accident site at maximum speed. Considering that there were movable objects in the task area, a time-varying risk density function was used to describe the risk degree at different locations in the task area. In the presence of the input disturbance, a robust controller was designed to drive each robot, with different maximum control input values, to the position that locally minimized the time cost metric function in a fixed time, and the conditions for maximum control input were obtained. Finally, simulation results and comparison result are presented in this paper.

Robust Tightly Coupled Pose Measurement Based on Multi-Sensor Fusion in Mobile Robot System.

Currently, simultaneous localization and mapping (SLAM) is one of the main research topics in the robotics field. Visual-inertia SLAM, which consists of a camera and an inertial measurement unit (IMU), can significantly improve robustness and enable scale weak-visibility, whereas monocular visual SLAM is scale-invisible. For ground mobile robots, the introduction of a wheel speed sensor can solve the scale weak-visibility problem and improve robustness under abnormal conditions. In this paper, a multi-sensor fusion SLAM algorithm using monocular vision, inertia, and wheel speed measurements is proposed. The sensor measurements are combined in a tightly coupled manner, and a nonlinear optimization method is used to maximize the posterior probability to solve the optimal state estimation. Loop detection and back-end optimization are added to help reduce or even eliminate the cumulative error of the estimated poses, thus ensuring global consistency of the trajectory and map. The outstanding contribution of this paper is that the wheel odometer pre-integration algorithm, which combines the chassis speed and IMU angular speed, can avoid the repeated integration caused by linearization point changes during iterative optimization; state initialization based on the wheel odometer and IMU enables a quick and reliable calculation of the initial state values required by the state estimator in both stationary and moving states. Comparative experiments were conducted in room-scale scenes, building scale scenes, and visual loss scenarios. The results showed that the proposed algorithm is highly accurate-2.2 m of cumulative error after moving 812 m (0.28%, loopback optimization disabled)-robust, and has an effective localization capability even in the event of sensor loss, including visual loss. The accuracy and robustness of the proposed method are superior to those of monocular visual inertia SLAM and traditional wheel odometers.

Multisynchronization of Coupled Heterogeneous Genetic Oscillator Networks via Partial Impulsive Control.

This paper focuses on the collective dynamics of multisynchronization among heterogeneous genetic oscillators under a partial impulsive control strategy. The coupled nonidentical genetic oscillators are modeled by differential equations with uncertainties. The definition of multisynchronization is proposed to describe some more general synchronization behaviors in the real. Considering that each genetic oscillator consists of a large number of biochemical molecules, we design a more manageable impulsive strategy for dynamic networks to achieve multisynchronization. Not all the molecules but only a small fraction of them in each genetic oscillator are controlled at each impulsive instant. Theoretical analysis of multisynchronization is carried out by the control theory approach, and a sufficient condition of partial impulsive controller for multisynchronization with given error bounds is established. At last, numerical simulations are exploited to demonstrate the effectiveness of our results.

Quantized stabilization of wireless networked control systems with packet losses.

This paper considers stabilization of discrete-time linear systems, where wireless networks exist for transmitting the sensor and controller information. Based on Markov jump systems, we show that the coarsest quantizer that stabilizes the WNCS is logarithmic in the sense of mean square quadratic stability and the stabilization of this system can be transformed into the robust stabilization of an equivalent uncertain system. Moreover, a method of optimal quantizer/controller design in terms of linear matrix inequality is presented. Finally, a numerical example is provided to illustrate the effectiveness of the developed theoretical results.

Impulsive Multiconsensus of Second-Order Multiagent Networks Using Sampled Position Data.

A multiconsensus problem of multiagent networks is solved in this paper, where multiconsensus refers to that the states of multiple agents in each subnetwork asymptotically converge to an individual consistent value when there exist information exchanges among subnetworks. A distributed impulsive protocol is proposed to achieve multiconsensus of second-order multiagent networks in terms of three categories: 1) stationary multiconsensus; 2) the first dynamic multiconsensus; and 3) the second dynamic multiconsensus. This impulsive protocol utilizes only sampled position data and is implemented at sampling instants. For those three categories of multiconsensus, the control parameters in the impulsive protocol are designed, respectively. Moreover, necessary and sufficient conditions are derived, under which each multiconsensus can be reached asymptotically. Several simulations are finally provided to demonstrate the effectiveness of the obtained theoretical results.

Stability and bifurcation analysis of new coupled repressilators in genetic regulatory networks with delays.

The genetic regulatory networks are complex dynamic systems which reflect various kinetic behaviors of living things. In this paper, a new structure of coupled repressilators is introduced to exploit the underlying functions. The new coupled repressilator model consists of two identical repressilators inhibiting each other directly. The coupling delays are taken into account. The existence of a unique equilibrium for this system is verified firstly, then the stability criteria for equilibrium are analyzed without and with coupling delays. The different functions on equilibrium and its stability played by related biochemical parameters in the structure including maximal transcription rate, coupling strength, the decay rate ratio between proteins and mRNAs, and coupling delays are discussed. At last, several numerical simulations are made to demonstrate our results.