Event-triggered based predictive iterative learning control with random packet loss compensation for nonlinear networked systems.

In this paper, a novel event-triggered predictive iterative learning control (ET-PILC) method with random packet loss compensation (RPLC) mechanism is proposed for unknown nonlinear networked systems with random packet loss (RPL). First, a new RPLC mechanism is designed by utilizing both the historical and predictive data information to avoid the deterioration of control performance due to RPL. Then, a new event-triggered condition is designed based on the proposed RPLC mechanism to save communication resources and reduce computational burden. Moreover, the convergence of the modeling error and tracking control error are analyzed theoretically, and simulation results are given to demonstrate the effectiveness of the proposed method further.

Dynamic Neural Network Predictive Compensation-Based Point-to-Point Iterative Learning Control With Nonuniform Batch Length.

This article discusses the problem of nonuniform running length in incomplete tracking control, which often occurs in industrial processes due to artificial or environmental changes, such as chemical engineering. It affects the design and application of iterative learning control (ILC) that relies on the strictly repetitive property. Therefore, a dynamic neural network (NN) predictive compensation strategy is proposed under the point-to-point ILC framework. To handle the difficulty of establishing an accurate mechanism model for real process control, the data-driven approach is also introduced. First, applying the iterative dynamic linearization (IDL) technique and radial basis function NN (RBFNN) to construct the iterative dynamic predictive data model (IDPDM) relies on input-output (I/O) signal, and the extended variable is defined by a predictive model to compensate for the incomplete operation length. Then, a learning algorithm based on multiple iteration errors is proposed using an objective function. This learning gain is constantly updated through the NN to adapt to changes in the system. In addition, the composite energy function (CEF) and compression mapping prove that the system is convergent. Finally, two numerical simulation examples are given.

Data-Driven Bipartite Formation for a Class of Nonlinear MIMO Multiagent Systems.

The bipartite formation control for the nonlinear discrete-time multiagent systems with signed digraph is considered in this article, in which the dynamics of the agents are completely unknown and multi-input multi-output (MIMO). First, the unknown nonlinear dynamic is converted into the compact-form dynamic linearization (CFDL) data model with a pseudo-Jacobian matrix (PJM). Based on the structurally balanced signed graph, a distance-based formation term is constructed and a bipartite formation model-free adaptive control (MFAC) protocol is designed. By employing the measured input and output data of the agents, the theoretical analysis is developed to prove the bounded-input bounded-output stability and the asymptotic convergence of the formation tracking error. Finally, the effectiveness of the proposed protocol is verified by two numerical examples.

Improved Model-Free Adaptive Control for MIMO Nonlinear Systems With Event-Triggered Transmission Scheme and Quantization.

In this article, an improved model-free adaptive control (iMFAC) is proposed for discrete-time multi-input multioutput (MIMO) nonlinear systems with an event-triggered transmission scheme and quantization (ETQ). First, an event-triggered scheme is designed, and the structure of the uniform quantizer with an encoding-decoding mechanism is given. With the concept of partial form dynamic linearization based on event-triggered and quantization (PFDL-ETQ), a linearized data model of the MIMO nonlinear system is constructed. Then, an improved model-free adaptive controller with the ETQ process is designed. By this design, the update of the pseudo partitioned Jacobean matrix (PPJM) estimates and control inputs occurs only when the trigger conditions are met, which reduces the network transmission burden and saves the computing resources. Theoretical analysis shows that the proposed iMFAC with the ETQ process can achieve a bounded convergence of tracking error. Finally, a numerical simulation and a biaxial gantry motor contour tracking control system simulation are given to illustrate the feasibility of the proposed iMFAC method with the ETQ process.

State Estimation via Designing Controller and State Estimation-Based Stabilization for Boolean Control Networks.

This article investigates the issues of state estimation and state estimation-based stabilization for Boolean control networks (BCNs). Unlike previous state observers, this article proposes an optimal state estimator by designing a particular input sequence for the first time, where the maximum-minimum method is employed such that the state of BCNs can be uniquely estimated in short time steps. A minimum reconstructible state set (MRSS) is constructed to determine this input sequence. Next, based on the estimated state, a finite-time stabilization scheme is proposed by constructing a switching controller consisting of three stages. A controller is first developed to estimate the state of BCNs in finite-time steps, and a state reachable controller is also provided to make the state of BCNs reachable to a given equilibrium point. Subsequently, a constant controller is further developed to stabilize the state of BCNs to the equilibrium point. Finally, an oxidative stress response model is used to illustrate the effectiveness of the proposed results.

Security data-driven iterative learning control for unknown nonlinear systems with hybrid attacks and fading measurements.

To achieve the stabilization objective of a class of nonlinear systems with unknown dynamics, this paper studies the security data-driven control problem under iterative learning schemes, where the faded channels are suffering from randomly hybrid attacks. The networked attacks try to obstruct the data transmission by injecting the false data. The plant is transformed into a dynamic data-model with the iteration-related linearization method. Then, two data-driven control methods, including a compensation scheme multiplied by increasing gains, are designed by using incomplete I/O signals. The effectiveness of the algorithms and the influence brought by stochastic issues are analyzed theoretically. Finally, a numerical simulation and a tracking example of agricultural vehicles illustrate the validity of the design.

Spatial Linear Dynamic Relationship of Strongly Connected Multiagent Systems and Adaptive Learning Control for Different Formations.

This article addresses an important problem of how to improve the learnability of an intelligent agent in a strongly connected multiagent network. A novel spatial-dimensional linear dynamic relationship (SLDR) is developed to formulate the spatial dynamic relationship of an agent with respect to all the related agents. The obtained SLDR virtually exists in the computer to describe the input-output (I/O) relationship in the spatial domain and an iterative adaptation mechanism is developed to update the SLDR using I/O information to show real-time dynamical behavior of multiagent systems with nonrepetitive initial states. Subsequently, an SLDR-based adaptive iterative learning control (SLDR-AILC) is presented with rigorous analysis for iteration-variant formation control targets. Not only the 3-D dynamic behavior of the multiagent network but also the control protocols of the communicated agents are incorporated in the learning mechanism and thus strong learnability of the proposed SLDR-AILC is achieved to improve control performance. The proposed SLDR-AILC is a data-driven scheme where no explicit model structure is needed. Simulations with strongly connected topologies verify the theoretical results.

Data-Driven Adaptive Consensus Learning From Network Topologies.

The problem of consensus learning from network topologies is studied for strongly connected nonlinear nonaffine multiagent systems (MASs). A linear spatial dynamic relationship (LSDR) is built at first to formulate the dynamic I/O relationship between an agent and all the other agents that are communicated through the networked topology. The LSDR consists of a linear parametric uncertain term and a residual nonlinear uncertain term. Utilizing the LSDR, a data-driven adaptive learning consensus protocol (DDALCP) is proposed to learn from both time dynamics of agent itself and spatial dynamics of the whole MAS. The parametric uncertainty and nonlinear uncertainty are estimated through an estimator and an observer respectively to improve robustness. The proposed DDALCP has a strong learning ability to improve the consensus performance because time dynamics and network topology information are both considered. The proposed consensus learning method is data-driven and has no dependence on the system model. The theoretical results are demonstrated by simulations.

Event-Triggered Model-Free Adaptive Iterative Learning Control for a Class of Nonlinear Systems Over Fading Channels.

This article investigates the problem of event-triggered model-free adaptive iterative learning control (MFAILC) for a class of nonlinear systems over fading channels. The fading phenomenon existing in output channels is modeled as an independent Gaussian distribution with mathematical expectation and variance. An event-triggered condition along both iteration domain and time domain is constructed in order to save the communication resources in the iteration. The considered nonlinear system is converted into an equivalent linearization model and then the event-triggered MFAILC independent of the system model is constructed with the faded outputs. Rigorous analysis and convergence proof are developed to verify the ultimately boundedness of the tracking error by using the Lyapunov function. Finally, the effectiveness of the presented algorithm is demonstrated with a numerical example and a velocity tracking control example of wheeled mobile robots (WMRs).

Composite prescribed performance control of small unmanned aerial vehicles using modified nonlinear disturbance observer.

An integrated control scheme composed of modified nonlinear disturbance observer and predefined-time prescribed performance control is proposed to address the high-accuracy tracking problem of the unmanned aerial vehicles (UAVs) subjected to external mismatched disturbances. By utilizing the transformation technique that incorporates the desired performance characteristic and the newly predefined-time performance function, the original controlled system can be transformed into a new unconstrained one to achieve the fixed-time convergence of the tracking error. Then, by virtual of the transformed unconstrained system, a modified nonlinear disturbance observer (NDO) which possesses fast convergence speed is established to estimate the external disturbance. With the application of the precise estimation value to compensate the normal control design in each back-stepping step, a novel composite control scheme is constructed. The light spot of the proposed scheme is that it not only has the superior capability to attenuate unknown mismatched disturbances, but also can guarantee that the output tracking errors converge to their prescribed regions within predefined time. Finally, simulation studies verify the effectiveness of the proposed control scheme.

Data-Driven Terminal Iterative Learning Consensus for Nonlinear Multiagent Systems With Output Saturation.

This article considers the problem of finite-time consensus for nonlinear multiagent systems (MASs), where the nonlinear dynamics are completely unknown and the output saturation exists. First, the mapping relationship between the output of each agent at the terminal time and the control input is established along the iteration domain. By using the terminal iterative learning control method, two novel distributed data-driven consensus protocols are proposed depending on the input and output saturated data of agents and its neighbors. Then, the convergence conditions independent of agents' dynamics are developed for the MASs with fixed communication topology. It is shown that the proposed data-driven protocol can guarantee the system to achieve two different finite-time consensus objectives. Meanwhile, the design is also extended to the case of switching topologies. Finally, the effectiveness of the data-driven protocol is validated by a simulation example.

RBFNN-Based Data-Driven Predictive Iterative Learning Control for Nonaffine Nonlinear Systems.

In this paper, a novel data-driven predictive iterative learning control (DDPILC) scheme based on a radial basis function neural network (RBFNN) is proposed for a class of repeatable nonaffine nonlinear discrete-time systems subjected to nonrepetitive external disturbances. First, by utilizing the dynamic linearization technique (DLT) with a newly introduced and unknown system parameter pseudopartial derivative (PPD) and designing a new RBFNN estimation algorithm along the iterative learning axis for addressing the unknown PPD and the unknown nonrepetitive external disturbances, a data-driven prediction model is established. It is theoretically shown that by constructing a composite energy function (CEF) with respect to the modeling error for the first time, the convergence of the modeling error via the proposed DLT-based RBFNN modeling method can be guaranteed, and the convergence speed is tunable. Then, a DDPILC with a disturbance compensation term is designed, and the convergence of the tracking control error is analyzed. Finally, simulations of a train operation system reveal that even if the train suffers from randomly varying load disturbances and nonlinear running resistance, the proposed scheme can make both the modeling error and the tracking control error decrease successively with increasing operation number.

Data driven control for a class of nonlinear systems with output saturation.

This paper considers the problem of data driven control (DDC) for a class of non-affine nonlinear systems with output saturation. A time varying linear data model for such nonlinear system is first established by using the dynamic linearization technique, then a DDC algorithm is constructed only depending on the control input data and the saturated output data. The convergence of the proposed algorithm is strictly proved and the effect of output saturation on system performance is also analyzed. It is shown that output saturation does not change the convergence property of DDC systems, thus it causes the convergence rate to slow down. Meanwhile, the ultimate tracking error is determined by the change of desired trajectory. If the desired trajectory is a constant, then the tracking error converges to zero. Two examples are exploited to verify the theoretical results.

Data-Driven Multiagent Systems Consensus Tracking Using Model Free Adaptive Control.

This paper investigates the data-driven consensus tracking problem for multiagent systems with both fixed communication topology and switching topology by utilizing a distributed model free adaptive control (MFAC) method. Here, agent's dynamics are described by unknown nonlinear systems and only a subset of followers can access the desired trajectory. The dynamical linearization technique is applied to each agent based on the pseudo partial derivative, and then, a distributed MFAC algorithm is proposed to ensure that all agents can track the desired trajectory. It is shown that the consensus error can be reduced for both time invariable and time varying desired trajectories. The main feature of this design is that consensus tracking can be achieved using only input-output data of each agent. The effectiveness of the proposed design is verified by simulation examples.

Adaptive Iterative Learning Control for Linear Systems With Binary-Valued Observations.

This brief presents a novel adaptive iterative learning control (ILC) algorithm for a class of single parameter systems with binary-valued observations. Using the certainty equivalence principle, the adaptive ILC algorithm is designed by employing a projection identification algorithm along the iteration axis. It is shown that, even though the available system information is very limited and the desired trajectory is iteration-varying, the proposed adaptive ILC algorithm can guarantee the convergence of parameter estimation over a finite-time interval along the iterative axis; meanwhile, the tracking error is pointwise convergence asymptotically. Two examples are given to validate the effectiveness of the algorithm.

Stabilization of systems with interval time-varying delay based on delay decomposing approach.

The paper considers the stabilization for systems with interval time-varying delay. By decomposing the delay interval into multiple equidistant subintervals and considering the triple integral terms, a novel Lyapunov-krasovskii functional(LKF) is defined. Then extended integral inequality and convex combination approach are used to estimate the derivative of the constructed functional, and as a result, the new stability criterion with less conservatism and decision variables is obtained. On this basis, the state feedback controller is designed, by using linearization method, the existence condition of controller is obtained in terms of linear matrix inequalities(LMIs), and the specific form of controller is also given, moreover, by selecting the appropriate parameter value, the stabilization time of the system can be reduced. Numerical examples are given to illustrate the effectiveness of the proposed method.