Optimal Tracking Control of Heterogeneous MASs Using Event-Driven Adaptive Observer and Reinforcement Learning.

This article considers the output tracking control problem of nonidentical linear multiagent systems (MASs) using a model-free reinforcement learning (RL) algorithm, where partial followers have no prior knowledge of the leader's information. To lower the communication and computing burden among agents, an event-driven adaptive distributed observer is proposed to predict the leader's system matrix and state, which consists of the estimated value of relative states governed by an edge-based predictor. Meanwhile, the integral input-based triggering condition is exploited to decide whether to transmit its private control input to its neighbors. Then, an RL-based state feedback controller for each agent is developed to solve the output tracking control problem, which is further converted into the optimal control problem by introducing a discounted performance function. Inhomogeneous algebraic Riccati equations (AREs) are derived to obtain the optimal solution of AREs. An off-policy RL algorithm is used to learn the solution of inhomogeneous AREs online without requiring any knowledge of the system dynamics. Rigorous analysis shows that under the proposed event-driven adaptive observer mechanism and RL algorithm, all followers are able to synchronize the leader's output asymptotically. Finally, a numerical simulation is demonstrated to verify the proposed approach in theory.

Single-tube Multiplex Nested PCR System for Efficient Detection of Pathogenic Microorganisms in SPF Rodents.

PCR testing is increasingly important for microbial control in SPF facilities. However, most current PCR methods are timeconsuming and require compromise between high sensitivity and high multiplexing. We developed a one-tube multiplex nested PCR strategy (MN-PCR) for simultaneous direct (that is, without culturing) detection of multiple pathogens. We first aligned sequences for the 16S rDNA genes of selected target bacteria and a panel of closely related organisms. From these data, we designed a pair of universal primers and multiple sets of species-specific PCR primers to amplify the target sequences; the universal primers were modified to include various degenerate bases and locked nucleic acids. In a single tube, 16S rDNA sequences were amplified by using the nested PCR primers under high temperature (that is, above 65°C) during the first stage of the MN-PCR procedure, when the target-species-specific PCR primers do not support amplification due to their short length. In addition, the concentration of the nested PCR primers during the first stage was adjusted to ensure that they were consumed and did not yield visible bands themselves. During the second stage, the enriched 16S rDNA sequences then served as templates for amplification of the species-specific fragments by using the multiple PCR primers at low annealing temperatures (that is, below 60°C). The results showed that our MN-PCR method detected as little as 1 fg of target bacterial DNA in a 20-µL reaction volume, whereas conventional multiplex PCR detected a minimum of 1 pg only. Compared with traditional multiplex PCR assays, our MN-PCR system is an effective and efficient culture-free process.

Resilient Asynchronous State Estimation for Markovian Jump Neural Networks Subject to Stochastic Nonlinearities and Sensor Saturations.

This article studies the problem of dissipativity-based asynchronous state estimation for a class of discrete-time Markov jump neural networks subject to randomly occurring nonlinearities, sensor saturations, and stochastic parameter uncertainties. First, two stochastic nonlinearities occurring in the system are described by statistical means and obey two Bernoulli processes independently. Then, the hidden Markov model is used to characterize the real communication environment closely between the designed estimator and the system model due to the networked-induced phenomenons that also lead to randomly occurring parametric uncertainties of the estimator considered modeled by two Bernoulli processes. A new criterion is established to guarantee that the resulting error system is stochastically stable with predefined dissipativity performance. Finally, we provide a simulation example to validate the theoretical analysis.

Event-Based Dissipative Filtering of Markovian Jump Neural Networks Subject to Incomplete Measurements and Stochastic Cyber-Attacks.

In this article, the dissipativity-based filtering of the Markovian jump neural networks subject to incomplete measurements and deception attacks is investigated by adopting an event-triggered communication strategy, where the attackers are supposed to occur in a random fashion but obey the Bernoulli distribution. Consider that the information of the system mode is transmitted to the filter over the communication network that is vulnerable to external attacks, which may lead to the undesired performance of the resulting system by injecting malicious information from the attackers. As a result, the filter has difficulty completing information from the original system. Besides, an event-triggered communication mechanism is introduced to reduce the communication frequency between data transmission due to the limited network resources, and different triggering conditions corresponding to different jump modes are developed. Then, based on the above considerations, the sufficient condition is derived to ensure the stochastic stability and dissipativity of the resulting augmented system although the deception attacks and incomplete information exist. A numerical simulated example is provided to verify the theoretical analysis.

Synchronization of Coupled Harmonic Oscillators With Asynchronous Intermittent Communication.

This paper adopts two different approaches, the small-gain technique and the integral quadratic constraints (IQCs), to investigate the synchronization problem of coupled harmonic oscillators (CHOs) via an event-triggered control strategy in a directed graph. First, a novel control protocol is proposed such that every state signal of the CHO decides when to exchange information with its neighbors asynchronously. Then, the resulting closed-loop system based on the designed control protocol is converted into a feedback interconnection of a linear system and a bounded operator, and the stable condition of the feedback interconnection is presented by employing the small-gain technique. In order to better describe the relationship between the input and output, the IQCs theorem is applied to derive the stable condition on the basis of the Kalman-Yakubovich-Popov lemma. Finally, a simulation example is provided to verify the proposed new algorithms.

Hidden-Markov-Model-Based Asynchronous Filter Design of Nonlinear Markov Jump Systems in Continuous-Time Domain.

This paper addresses the dissipative asynchronous filtering problem for a class of Takagi-Sugeno fuzzy Markov jump systems in the continuous-time domain. The hidden Markov model is applied to describe the asynchronous situation between the designed filter and the original system. Based on the stochastic Lyapunov function, a sufficient condition is developed to guarantee the stochastic stability of the filtering error systems with a given dissipative performance. Two different methods for the existence of desired filter are established. Due to the Finsler's lemma, the second approach has fewer variables to decide and brings less conservatism than the first one. Finally, an example is provided to demonstrate the correctness and advantage of the proposed approaches.

Improving Haptic Transparency for Uncertain Virtual Environments Using Adaptive Control and Gain-Scheduled Prediction.

The realism or transparency of haptic interfaces is becoming more critical as they are applied to training in fields like minimally invasive surgery (MIS). Surgical training simulators must provide a transparent virtual environment (VE) at a high update rate. Complex, deformable, cuttable tissue models have nonlinear dynamics and are computationally expensive, making it difficult to provide sufficient update rates. The objective of this work is to improve the transparency for this type of VE by formulating the unknown nonlinear dynamics as a quasi-linear parameter varying (LPV) system and designing a predictor to provide an output at a much higher update rate. An adaptive controller based on gain-scheduled prediction is considered for a nonlinear haptic device and a nonlinear, delayed, and sampled VE. The predictor uses feedback from the more accurate but slow-updating VE to update a simplified dynamic model. The predictor is designed based on numerical solutions to a linear matrix inequality derived using Lyapunov-based methods. Experimental results demonstrate the effectiveness of the gain-scheduled predictor approach and compare it to previous work using a constant-gain predictor. The gain-scheduled predictor results in significant performance improvements compared to a haptic system without prediction, but less significant improvement compared to the constant-gain approach.