Adaptive Critic Learning-Based Optimal Bipartite Consensus for Multiagent Systems With Prescribed Performance.

Developing a distributed bipartite optimal consensus scheme while ensuring user-predefined performance is essential in practical applications. Existing approaches to this problem typically require a complex controller structure due to adopting an identifier-actor-critic framework and prescribed performance cannot be guaranteed. In this work, an adaptive critic learning (ACL)-based optimal bipartite consensus scheme is developed to bridge the gap. A newly designed error scaling function, which defines the user-predefined settling time and steady accuracy without relying on the initial conditions, is then integrated into a cost function. The backstepping framework combines the ACL and integral reinforcement learning (IRL) algorithm to develop the adaptive optimal bipartite consensus scheme, which contributes a critic-only controller structure by removing the identifier and actor networks in the existing methods. The adaptive law of the critic network is derived by the gradient descent algorithm and experience replay to minimize the IRL-based residual error. It is shown that a compute-saving learning mechanism can achieve the optimal consensus, and the error variables of the closed-loop system are uniformly ultimately bounded (UUB). Besides, in any bounded initial condition, the evolution of bipartite consensus is limited to a user-prescribed boundary under bounded initial conditions. The illustrative simulation results validate the efficacy of the approach.

Versatile xanthan gum-based support bath material compatible with multiple crosslinking mechanisms: rheological properties, printability, and cytocompatibility study.

Extrusion-based bioprinting is a promising technology for the fabrication of complex three-dimensional (3D) tissue-engineered constructs. To further improve the printing accuracy and provide mechanical support during the printing process, hydrogel-based support bath materials have been developed. However, the gel structure of some support bath materials can be compromised when exposed to certain bioink crosslinking cues, hence their compatibility with bioinks can be limited. In this study, a xanthan gum-based composite support material compatible with multiple crosslinking mechanisms is developed. Different support bath materials can have different underlying polymeric structures, for example, particulate suspensions and polymer solution with varying supramolecular structure) and these properties are governed by a variety of different intermolecular interactions. However, common rheological behavior can be expected because they have similar demonstrated performance and functionality. To provide a detailed exploration/identification of the common rheological properties expressed by different support bath materials from a unified perspective, benchmark support bath materials from previous studies were prepared. A comparative rheological study revealed both the structural and shear behavior characteristics shared by support bath materials, including yield stress, gel complex moduli, shear-thinning behavior, and self-healing properties. Gel structural stability and functionality of support materials were tested in the presence of various crosslinking stimuli, confirming the versatility of the xanthan-based support material. We further investigated the effect of support materials and the diameter of extrusion needles on the printability of bioinks to demonstrate the improvement in bioink printability and structural integrity. Cytotoxicity and cell encapsulation viability tests were carried out to confirm the cell compatibility of the xanthan gum-based support bath material. We propose and demonstrate the versatility and compatibility of the novel support bath material and provide detailed new insight into the essential properties and behavior of these materials that serve as a guide for further development of support bath-based 3D bioprinting.

An Incremental Broad-Learning-System-Based Approach for Tremor Attenuation for Robot Tele-Operation.

The existence of the physiological tremor of the human hand significantly affects the application of tele-operation systems in performing high-precision tasks, such as tele-surgery, and currently, the process of effectively eliminating the physiological tremor has been an important yet challenging research topic in the tele-operation robot field. Some scholars propose using deep learning algorithms to solve this problem, but a large number of hyperparameters lead to a slow training speed. Later, the support-vector-machine-based methods have been applied to solve the problem, thereby effectively canceling tremors. However, these methods may lose the prediction accuracy, because learning energy cannot be accurately assigned. Therefore, in this paper, we propose a broad-learning-system-based tremor filter, which integrates a series of incremental learning algorithms to achieve fast remodeling and reach the desired performance. Note that the broad-learning-system-based filter has a fast learning rate while ensuring the accuracy due to its simple and novel network structure. Unlike other algorithms, it uses incremental learning algorithms to constantly update network parameters during training, and it stops learning when the error converges to zero. By focusing on the control performance of the slave robot, a sliding mode control approach has been used to improve the performance of closed-loop systems. In simulation experiments, the results demonstrated the feasibility of our proposed method.

Adaptive Actuator Failure Compensation Control Schemes for Uncertain Noncanonical Neural-Network Systems.

In this article, direct adaptive actuator failure compensation control is investigated for a class of noncanonical neural-network nonlinear systems whose relative degrees are implicit and parameters are unknown. Both the state tracking and output tracking control problems are considered, and their adaptive solutions are developed which have specific mechanisms to accommodate both actuator failures and parameter uncertainties to ensure the closed-loop system stability and asymptotic state or output tracking. The adaptive actuator failure compensation control schemes are derived for noncanonical nonlinear systems with neural-network approximation, and are also applicable to general parametrizable noncanonical nonlinear systems with both unknown actuator failures and unknown parameters, solving some key technical issues, in particular, dealing with the system zero dynamics under uncertain actuator failures. The effectiveness of the developed adaptive control schemes is confirmed by simulation results from an application example of speed control of dc motors.

Adaptive Consensus Tracking Control of Uncertain Nonlinear Multiagent Systems With Predefined Accuracy.

In this article, we consider the leader-follower consensus control problem of uncertain multiagent systems, aiming to achieve the improvement of system steady state and transient performance. To this end, a new adaptive neural control approach is proposed with a novel design of the Lyapunov function, which is generated with a class of positive functions. Guided by this idea, a series of smooth functions is incorporated into backstepping design and Lyapunov analysis to develop a performance-oriented controller. It is proved that the proposed controller achieves a perfect asymptotic consensus performance and a tunable L2 transient performance of synchronization errors, whereas most existing results can only ensure the stability. Simulation demonstrates the obtained results.

Chemical-Structure Evolution Model for the Self-Assembling of Amine-Terminated Monolayers on Nanoporous Carbon-Doped Organosilicate in Tightly Controlled Environments.

Amine-terminated self-assembled monolayers are molecular nanolayers, typically formed via wet-chemical solution on specific substrates for precision surface engineering or interface modification. However, homogeneous assembling of a highly ordered monolayer by the facile, wet method is rather tricky because it involves process parameters, such as solvent type, molecular concentration, soaking time and temperature, and humidity level. Here, we select 3-aminopropyltrimethoxysilane (APTMS) as a model molecule of aminosilane for the silanization of nanoporous carbon-doped organosilicate (p-SiOCH) under tightly controlled process environments. Surface mean roughness (Ra) and the water contact angle (θ) of the p-SiOCH layers upon silanization at a 10% humidity-controlled environment behave similarly and follow a three-stage evolution: a leap to a maximum at 15 min for Ra (from 0.227 to 0.411 nm) and θ (from 25 to 86°), followed by a gradual decrease to 0.225 nm and 69o, finally leveling off at the above values (>60 min). The -NH3+ fraction indicating monolayer disorientation evolves in a similar fashion. The fully grown monolayer is highly oriented yielding an unprecedented low -NH3+ fraction of 0.08 (and 0.92 of upright -NH2 groups). However, while having a similar thickness of approximately 1.4 ± 0.1 nm, the molecular layers grown at 30% relative humidity exhibit a significantly elevated -NH3+ fraction of 0.42, indicating that controlling the humidity is vital to the fabrication of highly oriented APTMS molecular layers. A bonding-structure evolution model, as distinct from those offered previously, is proposed and discussed.

Epiphytic bryophyte biomass estimation on tree trunks and upscaling in tropical montane cloud forests.

Epiphytic bryophytes (EB) are some of the most commonly found plant species in tropical montane cloud forests, and they play a disproportionate role in influencing the terrestrial hydrological and nutrient cycles. However, it is difficult to estimate the abundance of EB due to the nature of their "epiphytic" habitat. This study proposes an allometric scaling approach implemented in twenty-one 30 × 30 m plots across an elevation range in 16,773 ha tropical montane cloud forests of northeastern Taiwan to measure EB biomass, a primary metric for indicating plant abundance and productivity. A general allometry was developed to estimate EB biomass of 100 cm2 circular-shaped mats (n = 131) with their central depths. We developed a new point-intercept instrument to rapidly measure the depths of EB along tree trunks below 300 cm from the ground level (sampled stem surface area (SSA)) (n = 210). Biomass of EB of each point measure was derived using the general allometry and was aggregated across each SSA, and its performance was evaluated. Total EB biomass of a tree was estimated by referring to an in-situ conversion model and was interpolated for all trees in the plots (n = 1451). Finally, we assessed EB biomass density at the plot scale of the study region. The general EB biomass-depth allometry showed that the depth of an EB mat was a salient variable for biomass estimation (R 2 = 0.72, p < 0.001). The performance of upscaling from mats to SSA was satisfactory, which allowed us to further estimate mean (±standard deviation) EB biomass of the 21 plots (272 ± 104 kg ha-1). Since a significant relationship between tree size and EB abundance is commonly found, regional EB biomass may be mapped by integrating our method and three-dimensional remotely sensed airborne data.

Adaptive Neural Control of a Class of Stochastic Nonlinear Uncertain Systems With Guaranteed Transient Performance.

In this paper, an adaptive neural network control for stochastic nonlinear systems with uncertain disturbances is proposed. The neural network is considered to approximate an uncertain function in a nonlinear system. And computational burden in operation is reduced by handling the norm of the neural-network vector. However, it will arise chattering issue, which is a challenge to avoid it from the symbolic operation. Further, traditional schemes often view error of estimate as bounded constant, but it is a time-varying function exactly, which may lead control schemes cannot conform to practical situation and guarantee stability of systems. Thus, backstepping technology and the neural network technology combined to stabilize stochastic nonlinear systems together to handle the aforementioned issues. It is proved that the proposed control scheme can guarantee the satisfactory asymptotic convergence performance and predetermined transient tracking error performance. From simulation results, the proposed control scheme is verified that can guarantee the satisfactory effectiveness.

Adaptive Control of Noncanonical Neural-Network Nonlinear Systems With Unknown Input Dead-Zone Characteristics.

Most of the available results on adaptive control of uncertain nonlinear systems with input dead-zone characteristics are for canonical nonlinear systems whose relative degrees are explicit and for which a Lyapunov-based backstepping design is directly applicable. However, those results cannot be applied to noncanonical form nonlinear systems whose relative degrees are implicit and for which a Lyapunov-based backstepping design may not be applicable. This article solves the adaptive control problem of a class of noncanonical neural-network nonlinear systems with unknown input dead-zones. A complete solution framework is developed, using a new gradient-based design which is applicable to noncanonical nonlinear systems with input dead-zones. Signal boundedness of the closed-loop system and the desired tracking performance are ensured with the developed control schemes. Their effectiveness is illustrated by an application example of speed control of dc motors. This article can be readily extended to handle general parametrizable noncanonical nonlinear systems with unknown dynamics and input dead-zones, to solve such an open problem.

Adaptive Fuzzy Tracking Control of Nonlinear Systems With Asymmetric Actuator Backlash Based on a New Smooth Inverse.

This paper is concentrated on the problem of adaptive fuzzy tracking control for an uncertain nonlinear system whose actuator is encountered by the asymmetric backlash behavior. First, we propose a new smooth inverse model which can approximate the asymmetric actuator backlash arbitrarily. By applying it, two adaptive fuzzy control scenarios, namely, the compensation-based control scheme and nonlinear decomposition-based control scheme, are then developed successively. It is worth noticing that the first fuzzy controller exhibits a better tracking control performance, although it recourses to a known slope ratio of backlash nonlinearity. The second one further removes the restriction, and also gets a desirable control performance. By the strict Lyapunov argument, both adaptive fuzzy controllers guarantee that the output tracking error is convergent to an adjustable region of zero asymptotically, while all the signals remain semiglobally uniformly ultimately bounded. Lastly, two comparative simulations are conducted to verify the effectiveness of the proposed fuzzy controllers.

Adaptive Position/Attitude Tracking Control of Aerial Robot With Unknown Inertial Matrix Based on a New Robust Neural Identifier.

This paper presents a novel adaptive controller for controlling an autonomous helicopter with unknown inertial matrix to asymptotically track the desired trajectory. To identify the unknown inertial matrix included in the attitude dynamic model, this paper proposes a new structural identifier that differs from those previously proposed in that it additionally contains a neural networks (NNs) mechanism and a robust adaptive mechanism, respectively. Using the NNs to compensate the unknown aerodynamic forces online and the robust adaptive mechanism to cancel the combination of the overlarge NNs compensation error and the external disturbances, the new robust neural identifier exhibits a better identification performance in the complex flight environment. Moreover, an optimized algorithm is included in the NNs mechanism to alleviate the burdensome online computation. By the strict Lyapunov argument, the asymptotic convergence of the inertial matrix identification error, position tracking error, and attitude tracking error to arbitrarily small neighborhood of the origin is proved. The simulation and implementation results are provided to evaluate the performance of the proposed controller.

Adaptive Neural Output Feedback Control of Output-Constrained Nonlinear Systems With Unknown Output Nonlinearity.

This paper addresses the problem of adaptive neural output-feedback control for a class of special nonlinear systems with the hysteretic output mechanism and the unmeasured states. A modified Bouc-Wen model is first employed to capture the output hysteresis phenomenon in the design procedure. For its fusion with the neural networks and the Nussbaum-type function, two key lemmas are established using some extended properties of this model. To avoid the bad system performance caused by the output nonlinearity, a barrier Lyapunov function technique is introduced to guarantee the prescribed constraint of the tracking error. In addition, a robust filtering method is designed to cancel the restriction that all the system states require to be measured. Based on the Lyapunov synthesis, a new neural adaptive controller is constructed to guarantee the prescribed convergence of the tracking error and the semiglobal uniform ultimate boundedness of all the signals in the closed-loop system. Simulations are implemented to evaluate the performance of the proposed neural control algorithm in this paper.

Periodic nano/micro-hole array silicon solar cell.

In this study, we applied a metal catalyst etching method to fabricate a nano/microhole array on a Si substrate for application in solar cells. In addition, the surface of an undesigned area was etched because of the attachment of metal nanoparticles that is dissociated in a solution. The nano/microhole array exhibited low specular reflectance (<1%) without antireflection coating because of its rough surface. The solar spectrum related total reflection was approximately 9%. A fabricated solar cell with a 40-µm hole spacing exhibited an efficiency of 9.02%. Comparing to the solar cell made by polished Si, the external quantum efficiency for solar cell with 30 s etching time was increased by 16.7%.

Adaptive neural control for a class of nonlinear time-varying delay systems with unknown hysteresis.

This paper investigates the fusion of unknown direction hysteresis model with adaptive neural control techniques in face of time-delayed continuous time nonlinear systems without strict-feedback form. Compared with previous works on the hysteresis phenomenon, the direction of the modified Bouc-Wen hysteresis model investigated in the literature is unknown. To reduce the computation burden in adaptation mechanism, an optimized adaptation method is successfully applied to the control design. Based on the Lyapunov-Krasovskii method, two neural-network-based adaptive control algorithms are constructed to guarantee that all the system states and adaptive parameters remain bounded, and the tracking error converges to an adjustable neighborhood of the origin. In final, some numerical examples are provided to validate the effectiveness of the proposed control methods.