Game-Based Distributed Decision Optimization for Heterogeneous Multiagent Systems With Unknown Nonlinear Dynamics.

Time-varying multiplicative/additive faults compensation in both actuators and sensors simultaneously for nonlinear systems via robust sliding mode control scheme

In this paper, we consider the adaptive synchronization problem for multi-agents systems with unknown nonlinear dynamics. The topologies of the networks are switching. A novel adaptive synchronization algorithm is proposed by using linear parameterizations of unknown nonlinear dynamics of all agents. By stability analysis and parameter convergence analysis of the proposed algorithm, adaptive synchronization can be realized based on neighboring graphs. The stability analysis is based on algebraic graph theory and Lyapunov theory, the PE condition plays a key role in parameter convergence analysis. Example is given to validate the theoretical results.

In this article, an observer-based fuzzy adaptive inverse optimal output feedback control problem is studied for a class of nonlinear systems in strict-feedback form. The considered nonlinear systems contain unknown nonlinear dynamics and their states are not measured directly. Fuzzy logic systems are applied to identify the unknown nonlinear dynamics and an auxiliary nonlinear system is constructed. Based on this auxiliary system, a fuzzy state observer is first designed to estimate the immeasurable states. By using the inverse optimal principle and adaptive backstepping design theory, an observer-based fuzzy adaptive inverse optimal output feedback control scheme is then developed. The proposed inverse optimal control scheme need not assume that the states are measurable. It also guarantees that the closed-loop system is semiglobally uniformly ultimately bounded, and achieves the optimal control objective as well. Finally, two simulation examples are provided to check the validity of the presented control method.

This paper deals with the finite-time consensus for single-integrator kinematics with unknown inherent nonlinear dynamics under a directed interaction graph. A distributed nonlinear algorithm is proposed to solve the problem given that some sufficient conditions are satisfied. Due to the existence of the unknown inherent nonlinear dynamics and the desired finite-time convergence property, the stability analysis is challenging because the traditional stability tools frequently used in solving the consensus problem cannot be applied here. For this purpose, we propose a novel comparison based stability tool, which plays an important role in the stability analysis of the proposed algorithm.

Adaptive swarm control for high-order self-organized system with unknown heterogeneous nonlinear dynamics and unmeasured states

The filter design for nonlinear uncertain systems is quite challenging since efficient estimation is required against stochastic noises, nonlinear uncertain dynamics as well as their concurrent effects. To this end, this article develops a novel filter algorithm by augmenting the disturbance as well as unknown nonlinear dynamics as an extended state and constructing consistent Kalman–Bucy algorithm. The proposed extended state based Kalman–Bucy filter (KBF) is shown to be of bounded estimation error, and the estimation accuracy can be online evaluated. More importantly, the estimation of asymptotic minimum variance is realized in condition that the changing rate of uncertainty approaches to zero. Therefore, the proposed extended state filter enables effective mitigation of disturbance and unknown nonlinear dynamics in real time by feedback control. The proposed algorithm is experimentally verified via a temperature control application in proton exchange membrane fuel cell, in which the thermocouple noise and the electrochemical uncertainty are seriously presented. The temperature variation of the extended state based KBF-based control is greatly reduced, in comparison with the conventional control. The results in this article depict a promising prospect of the proposed method for industrial control applications to handle both noises and nonlinear uncertain dynamics.

In this paper, high order consensus problem of multi-agent systems with non-identical unknown nonlinear dynamics following an unknown and nonlinear leader is investigated in networks with fixed and switching topologies. By parameterizations of unknown nonlinear dynamics of agents in the system, neighbor-based adaptive consensus algorithms are proposed by incorporating consensus errors in addition to relative position feedback. Base on algebraic graph theory, Lyapunov theory, Riccati inequalities and PE condition, analysis of stability and parameter convergence of the proposed algorithm are conducted. Finally, a simulation in switching networks is worked out to illustrate the effectiveness of the theoretical results.

Unknown nonlinear dynamics can limit the performance of model-based feedforward control. The aim of this paper is to develop a feedforward control framework for systems with unknown, typically nonlinear, dynamics. To address the unknown dynamics, a physics-based feedforward model is complemented by a neural network. The neural network output in the subspace of the model is penalized through orthogonal projection. This results in uniquely identifiable model coefficients, enabling both increased performance and good generalization. The feedforward control framework is validated on a representative system with performance limiting nonlinear friction characteristics.

In this work, an adaptive neural network (NN) optimized output-feedback control problem is studied for a class of stochastic nonlinear systems with unknown nonlinear dynamics, input saturation, and state constraints. A nonlinear state observer is designed to estimate the unmeasured states, and the NNs are used to approximate the unknown nonlinear functions. Under the framework of the backstepping technique, the virtual and actual optimal controllers are developed by employing the actor-critic architecture. Meanwhile, the tan-type Barrier optimal performance index functions are developed to prevent the nonlinear systems from the state constraints, and all the states are confined within the preselected compact sets all the time. It is worth mentioning that the proposed optimized control is clearly simple since the reinforcement learning (RL) algorithm is derived based on the negative gradient of a simple positive function. Furthermore, the proposed optimal control strategy ensures that all the signals in the closed-loop system are bounded. Finally, a practical simulation example is carried out to further illustrate the effectiveness of the proposed optimal control method.

This article investigates the adaptive neural network (NN) event-triggered containment control problem for a class of nonlinear multiagent systems (MASs). Since the considered nonlinear MASs contain unknown nonlinear dynamics, immeasurable states, and quantized input signals, the NNs are adopted to model unknown agents, and an NN state observer is established by using the intermittent output signal. Subsequently, a novel event-triggered mechanism consisting of both the sensor-to-controller and controller-to-actuator channels are established. By decomposing quantized input signals into the sum of two bounded nonlinear functions and based on the adaptive backstepping control and first-order filter design theories, an adaptive NN event-triggered output-feedback containment control scheme is formulated. It is proved that the controlled system is semi-globally uniformly ultimately bounded (SGUUB) and the followers are within a convex hull formed by the leaders. Finally, a simulation example is given to validate the effectiveness of the presented NN containment control scheme.

A novel type of control scheme combining the disturbance-observer-based control (DOBC) with terminal sliding mode (TSM) control is proposed for a class of multiple-input–multiple-output (MIMO) continuous non-linear systems subject to disturbances. The disturbances are supposed to include two parts. One in the input channel is generated by an exogenous system with uncertainty, which can represent the harmonic signals with modelling perturbations. The other is supposed to have the bounded H 2 norm. The disturbance observers based on regional pole placement and D-stability theory are presented, which can be constructed separately from the controller design. By integrating DOBC with TSM control laws, the disturbances can be rejected and attenuated, simultaneously, and the desired dynamic performances can be guaranteed for non-linear systems in finite time with known and unknown non-linear dynamics, respectively. Two simulation examples for a flight control system and a hard disk drive actuator are given respectively to demonstrate the effectiveness of the proposed control schemes compared with the previous schemes.

A practical design method is developed for path following and coordinated control of Autonomous Underwater Vehicles (AUVs). Each AUV is modeled by a system with time-varying parameters, unknown nonlinear dynamics and unknown disturbance. A robust adaptive distributed controller is designed for each AUV such that all AUVs ultimately synchronize to the desired paths with required formation. Moreover, these controllers are distributed in the sense that the controller design for each AUV only requires relative state in formation between itself and its neighbors. Finally, a simulation example demonstrates the effectiveness of the control method.

This article is concerned with the tracking control problem for uncertain high-order nonlinear systems in the presence of input saturation. A finite-time control strategy combined with neural state observer and command filtered backstepping is proposed. The neural network models the unknown nonlinear dynamics, the finite-time command filter (FTCF) guarantees the approximation of its output to the derivative of virtual control signal in finite time at the backstepping procedure, and the fraction power-based error compensation system compensates for the filtering errors between FTCF and virtual signal. In addition, the input saturation problem is dealt with by introducing the auxiliary system. Overall, it is shown that the designed controller drives the output tracking error to the desired neighborhood of the origin at a finite time and all the signals in the closed-loop system are bounded at a finite time. Two simulation examples are given to demonstrate the control effectiveness.

Objective. Removal of common mode noise and artifacts from extracellularly measured action potentials, herein referred to as spikes, recorded with multi-electrode arrays (MEAs) which included severe noise and artifacts generated by an ultrahigh field (UHF) 16.4 Tesla magnetic resonance imaging (MRI) scanner. Approach. An adaptive virtual referencing (AVR) algorithm is used to remove artifacts and thus enable extraction of neural spike signals from extracellular recordings in anesthetized rat brains. A 16-channel MEA with 150-micron inter-site spacing is used, and a virtual reference is created by spatially averaging the 16 signal channels which results in a reference signal without extracellular spiking activity while preserving common mode noise and artifacts. This virtual reference signal is then used as the input to an adaptive FIR filter which optimally scales and time-shifts the reference to each specific electrode site to remove the artifacts and noise. Main results. By removing artifacts and reducing noise, the neural spikes at each electrode site can be well extracted, even from data originally recorded with a high noise floor due to electromagnetic interference and artifacts generated by a 16.4T MRI scanner. The AVR method enables many more spikes to be detected than would otherwise be possible. Further, the filtered spike waveforms can be well separated from each other using PCA feature extraction and semi-supervised k-means clustering. While data in a 16.4T MRI scanner contains significantly more noise and artifacts, the developed AVR method enables similar data quality to be extracted as recorded on benchtop experiments outside the MRI scanner. Significance. AVR of extracellular spike signals recorded with MEAs has not been previously reported and fills a technical need by enabling low-noise extracellular spike extraction in noisy and challenging environments such as UHF MRI that will enable further study of neuro-vascular coupling at UHF.

Consensus of multi-agent systems with heterogeneous unknown nonlinear switching dynamics: A dwelling time approach