Adaptive dynamic output feedback neural network control of uncertain MIMO nonlinear systems with prescribed performance

Globally Adaptive Neural Network Output-Feedback Control for Uncertain Nonlinear Systems

In this article, a globally neural-network-based adaptive control strategy with flat-zone modification is proposed for a class of uncertain output feedback systems with time-varying bounded disturbances. A high-order continuously differentiable switching function is introduced into the filter dynamics to achieve global compensation for uncertain functions, thus further to ensure that all the closed-loop signals are globally uniformity ultimately bounded (GUUB). It is proven that the output tracking error converges to the prespecified neighborhood of the origin. The effectiveness of the proposed control method is verified by two simulation examples.

Prescribed Performance Fault-Tolerant Control for Uncertain Nonlinear MIMO System Using Actor-Critic Learning Structure

This article studies the prescribed performance fault-tolerant control problem for a class of uncertain nonlinear multi-input and multioutput systems. A learning-based fault-tolerant controller is proposed to achieve the asymptotic stability, without requiring a priori knowledge of the system dynamics. To deal with the prescribed performance, a new error transformation function is introduced to convert the constrained error dynamics into an equivalent unconstrained one. Under the actor-critic learning structure, a continuous-time long-term performance index is presented to evaluate the current control behavior. Then, a critic network is used to approximate the designed performance index and provide a reinforcement signal to the action network. Based on the robust integral of the sign of error feedback control method, an action network-based controller is developed. It is shown by the Lyapunov approach that the tracking error can converge to zero asymptotically with the prescribed performance guaranteed. Simulation results are provided to validate the feasibility and effectiveness of the proposed control scheme.

Application of Reinforcement Learning and Deep Learning in Multiple-Input and Multiple-Output (MIMO) Systems

The current wireless communication infrastructure has to face exponential development in mobile traffic size, which demands high data rate, reliability, and low latency. MIMO systems and their variants (i.e., Multi-User MIMO and Massive MIMO) are the most promising 5G wireless communication systems technology due to their high system throughput and data rate. However, the most significant challenges in MIMO communication are substantial problems in exploiting the multiple-antenna and computational complexity. The recent success of RL and DL introduces novel and powerful tools that mitigate issues in MIMO communication systems. This article focuses on RL and DL techniques for MIMO systems by presenting a comprehensive review on the integration between the two areas. We first briefly provide the necessary background to RL, DL, and MIMO. Second, potential RL and DL applications for different MIMO issues, such as detection, classification, and compression; channel estimation; positioning, sensing, and localization; CSI acquisition and feedback, security, and robustness; mmWave communication and resource allocation, are presented.

Distributed Path Following of Multiple Under-Actuated Autonomous Surface Vehicles Based on Data-Driven Neural Predictors via Integral Concurrent Learning

This article addresses the problem of distributed path following of multiple under-actuated autonomous surface vehicles (ASVs) with completely unknown kinetic models. An integrated distributed guidance and learning control architecture is proposed for achieving a time-varying formation. Specifically, a robust distributed guidance law at the kinematic level is developed based on a consensus approach, a path-following mechanism, and an extended state observer. At the kinetic level, a model-free kinetic control law based on data-driven neural predictors via integral concurrent learning is designed such that the kinetic model can be learned by using recorded data. The advantage of the proposed method is two-folds. First, the proposed formation controllers are able to achieve various time-varying formations without using the velocities of neighboring vehicles. Second, the proposed control law is model-free without any parameter information on kinetic models. Simulation results substantiate the effectiveness of the proposed robust distributed guidance and model-free control laws for multiple under-actuated ASVs with fully unknown kinetic models.

Neural Networks-Based Distributed Adaptive Control of Nonlinear Multiagent Systems

The cooperative control problem of nonlinear multiagent systems is studied in this paper. The followers in the communication network are subject to unmodeled dynamics. A fully distributed neural-networks-based adaptive control strategy is designed to guarantee that all the followers are asymptotically synchronized to the leader, and the synchronization errors are within a prescribed level, where some global information, such as minimum and maximum singular value of graph adjacency matrix, is not necessarily to be known. Based on the Lyapunov stability theory and algebraic graph theory, the stability analysis of the resulting closed-loop system is provided. Finally, an numerical example illustrates the effectiveness and potential of the proposed new design techniques.

Adaptive output-feedback control with prescribed performance for trajectory tracking of underactuated surface vessels

In this paper, we address the problem of trajectory tracking control of underactuated surface vessels in a quantitative method with only position and attitude available. Combined with high-gain observer, parameter compression algorithm and performance function, an adaptive control scheme with prescribed performance is proposed. The high-gain observer is constructed to estimate the velocities, and the parameter compression algorithm is adopted to address persistent perturbations and model uncertainties in a more concise way. By prescribed performance function, the controller can be designed with prescribed performance. The results about system stability is given and proved by using the Lyapunov direct method. The signals concerning with all the errors converge to a bounded set. Compared with the existing methods, the developed scheme can reduce the number of tuning parameters, and guarantee the tracking errors bounded within the prescribed performance constraints in the transformed coordinate, which means the steady errors, convergence rates and maximum overshoots can be guaranteed by the performance function. Comparison and numerical simulations are given to demonstrate the effectiveness of the proposed scheme.

Neural Network-Based Model-Free Adaptive Near-Optimal Tracking Control for a Class of Nonlinear Systems

In this paper, the receding horizon near-optimal tracking control problem about a class of continuous-time nonlinear systems with fully unknown dynamics is considered. The main challenges of this problem lie in two aspects: 1) most existing systems only restrict their considerations to the state feedback part while the input channel parameters are assumed to be known. This paper considers fully unknown system dynamics in both the state feedback channel and the input channel and 2) the optimal control of nonlinear systems requires the solution of nonlinear Hamilton-Jacobi-Bellman equations. Up to date, there are no systematic approaches in the existing literature to solve it accurately. A novel model-free adaptive near-optimal control method is proposed to solve this problem via utilizing the Taylor expansion-based problem relaxation, the universal approximation property of sigmoid neural networks, and the concept of sliding mode control. By making approximation for the performance index, it is first relaxed to a quadratic program, and then, a linear algebraic equation with unknown terms. An auxiliary system is designed to reconstruct the input-to-output property of the control systems with unknown dynamics, so as to tackle the difficulty caused by the unknown terms. Then, by considering the property of the sliding-mode surface, an explicit adaptive near-optimal control law is derived from the linear algebraic equation. Theoretical analysis shows that the auxiliary system is convergent, the resultant closed-loop system is asymptotically stable, and the performance index asymptomatically converges to optimal. An illustrative example and experimental results are presented, which substantiate the efficacy of the proposed method and verify the theoretical results.

Multilateral Telecoordinated Control of Multiple Robots With Uncertain Kinematics

This paper addresses the telecoordinated control of multiple robots in the simultaneous presence of asymmetric time-varying delays, nonpassive external forces, and uncertain kinematics/dynamics. To achieve the control objective, a neuroadaptive controller with utilizing prescribed performance control and switching control technique is developed, where the basic idea is to employ the concept of motion synchronization in each pair of master-slave robots and among all slave robots. By using the multiple Lyapunov-Krasovskii functionals method, the state-independent input-to-output practical stability of the closed-loop system is established. Compared with the previous approaches, the new design is straightforward and easier to implement and is applicable to a wider area. Simulation results on three pairs of three degrees-of-freedom robots confirm the theoretical findings.

A Novel Switching-Based Control Framework for Improved Task Performance in Teleoperation System With Asymmetric Time-Varying Delays

This paper addresses the adaptive control for task-space teleoperation systems with constrained predefined synchronization error, where a novel switched control framework is investigated. Based on multiple Lyapunov-Krasovskii functionals method, the stability of the resulting closed-loop system is established in the sense of state-independent input-to-output stability. Compared with previous work, the developed method can simultaneously handle the unknown kinematics/dynamics, asymmetric varying time delays, and prescribed performance control in a unified framework. It is shown that the developed controller can guarantee the prescribed transient-state and steady-state synchronization performances between the master and slave robots, which is demonstrated by the simulation study.

Adaptive Neural Control of Uncertain Nonlinear Systems Using Disturbance Observer

This paper studies the problem of prescribed performance adaptive neural control for a class of uncertain multi-input and multi-output (MIMO) nonlinear systems in the presence of external disturbances and input saturation based on a disturbance observer. The system uncertainties are tackled by neural network (NN) approximation. To handle unknown disturbances, a Nussbaum disturbance observer is presented. By incorporating the disturbance observer and NNs, an adaptive prescribed performance neural control scheme is further developed. Then, the expected asymptotically convergent tracking errors between system output signals and desired signals are achieved. Numerical simulation results demonstrate the effectiveness of the proposed control scheme.

Composite learning from adaptive backstepping neural network control

In existing neural network (NN) learning control methods, the trajectory of NN inputs must be recurrent to satisfy a stringent condition termed persistent excitation (PE) so that NN parameter convergence is obtainable. This paper focuses on command-filtered backstepping adaptive control for a class of strict-feedback nonlinear systems with functional uncertainties, where an NN composite learning technique is proposed to guarantee convergence of NN weights to their ideal values without the PE condition. In the NN composite learning, spatially localized NN approximation is employed to handle functional uncertainties, online historical data together with instantaneous data are exploited to generate prediction errors, and both tracking errors and prediction errors are employed to update NN weights. The influence of NN approximation errors on the control performance is also clearly shown. The distinctive feature of the proposed NN composite learning is that NN parameter convergence is guaranteed without the requirement of the trajectory of NN inputs being recurrent. Illustrative results have verified effectiveness and superiority of the proposed method compared with existing NN learning control methods.

Neural Skyline Filtering for Imbalance Features Classification

In the current digitalized era, large datasets play a vital role in features extractions, information processing, knowledge mining and management. Sometimes, existing mining approaches are not sufficient to handle large volume of datasets. Biological data processing also suffers for the same issue. In the present work, a classification process is carried out on large volume of exons and introns from a set of raw data. The proposed work is designed into two parts as pre-processing and mapping-based classification. For pre-processing, three filtering techniques have been used. However, these traditional filtering techniques face difficulties for large datasets due to the long required time during large data processing as well as the large required memory size. In this regard, a mapping-based neural skyline filtering approach is designed. Randomized algorithm performed the mapping for large volume of datasets based on objective function. The objective function determines the randomized size of the datasets according to the homogeneity. Around 200 million DNA base pairs have been used for experimental analysis. Experimental result shows that mapping centric filtering outperforms other filtering techniques during large data processing.

Adaptive fuzzy prescribed performance control for MIMO nonlinear systems with unknown control direction and unknown dead-zone inputs

In this study, an adaptive fuzzy prescribed performance control approach is developed for a class of uncertain multi-input and multi-output (MIMO) nonlinear systems with unknown control direction and unknown dead-zone inputs. The properties of symmetric matrix are exploited to design adaptive fuzzy prescribed performance controller, and a Nussbaum-type function is incorporated in the controller to estimate the unknown control direction. This method has two prominent advantages: it does not require the priori knowledge of control direction and only three parameters need to be updated on-line for this MIMO systems. It is proved that all the signals in the resulting closed-loop system are bounded and that the tracking errors converge to a small residual set with the prescribed performance bounds. The effectiveness of the proposed approach is validated by simulation results.

Design and Implementation of Self-Adaptive PD Controller Based on Fuzzy Logic Algorithm for Omni-Directional Fast Robots in Presence of Model Uncertainties

In this paper, a self-adaptive PD (SAPD) is employed for motion control of omni-directional robots. The method contains a PD controller that can be tuned online using a fuzzy logic system (FLS). Fast and accurate positioning is one of significant challenges in robot platforms. In addition, some uncertainties have adverse effects on traditional control system's performance during the robot's motion. Slow responses, low accuracy and instability are the most important drawbacks of widespread controllers in presence of uncertain dynamics. Since the fuzzy algorithm can deal with uncertainties and nonlinearities, the proposed method can tackle the mentioned problems. The controller is designed based on an uncertain model and implemented on a four wheeled omni-directional fast robot. The novelty of this article is proposing an enhanced version of well-known gain scheduling PD controller to improve positioning performance of the robot in different circumstances. Experimental results show that the method can provide a desirable performance in the presence of uncertainties.

Globally Stable Adaptive Backstepping Neural Network Control for Uncertain Strict-Feedback Systems With Tracking Accuracy Known a Priori

This paper addresses the problem of globally stable direct adaptive backstepping neural network (NN) tracking control design for a class of uncertain strict-feedback systems under the assumption that the accuracy of the ultimate tracking error is given a priori. In contrast to the classical adaptive backstepping NN control schemes, this paper analyzes the convergence of the tracking error using Barbalat's Lemma via some nonnegative functions rather than the positive-definite Lyapunov functions. Thus, the accuracy of the ultimate tracking error can be determined and adjusted accurately a priori, and the closed-loop system is guaranteed to be globally uniformly ultimately bounded. The main technical novelty is to construct three new n th-order continuously differentiable functions, which are used to design the control law, the virtual control variables, and the adaptive laws. Finally, two simulation examples are given to illustrate the effectiveness and advantages of the proposed control method.

Adaptive neural PD control with semiglobal asymptotic stabilization guarantee

This paper proves that adaptive neural plus proportional-derivative (PD) control can lead to semiglobal asymptotic stabilization rather than uniform ultimate boundedness for a class of uncertain affine nonlinear systems. An integral Lyapunov function-based ideal control law is introduced to avoid the control singularity problem. A variable-gain PD control term without the knowledge of plant bounds is presented to semiglobally stabilize the closed-loop system. Based on a linearly parameterized raised-cosine radial basis function neural network, a key property of optimal approximation is exploited to facilitate stability analysis. It is proved that the closed-loop system achieves semiglobal asymptotic stability by the appropriate choice of control parameters. Compared with previous adaptive approximation-based semiglobal or asymptotic stabilization approaches, our approach not only significantly simplifies control design, but also relaxes constraint conditions on the plant. Two illustrative examples have been provided to verify the theoretical results.

Contact-force distribution optimization and control for quadruped robots using both gradient and adaptive neural networks

This paper investigates optimal feet forces' distribution and control of quadruped robots under external disturbance forces. First, we formulate a constrained dynamics of quadruped robots and derive a reduced-order dynamical model of motion/force. Consider an external wrench on quadruped robots; the distribution of required forces and moments on the supporting legs of a quadruped robot is handled as a tip-point force distribution and used to equilibrate the external wrench. Then, a gradient neural network is adopted to deal with the optimized objective function formulated as to minimize this quadratic objective function subjected to linear equality and inequality constraints. For the obtained optimized tip-point force and the motion of legs, we propose the hybrid motion/force control based on an adaptive neural network to compensate for the perturbations in the environment and approximate feedforward force and impedance of the leg joints. The proposed control can confront the uncertainties including approximation error and external perturbation. The verification of the proposed control is conducted using a simulation.

Sliding-mode control design for nonlinear systems using probability density function shaping

In this paper, we propose a sliding-mode-based stochastic distribution control algorithm for nonlinear systems, where the sliding-mode controller is designed to stabilize the stochastic system and stochastic distribution control tries to shape the sliding surface as close as possible to the desired probability density function. Kullback-Leibler divergence is introduced to the stochastic distribution control, and the parameter of the stochastic distribution controller is updated at each sample interval rather than using a batch mode. It is shown that the estimated weight vector will converge to its ideal value and the system will be asymptotically stable under the rank-condition, which is much weaker than the persistent excitation condition. The effectiveness of the proposed algorithm is illustrated by simulation.

Nonlinear systems identification and control via dynamic multitime scales neural networks

This paper deals with the adaptive nonlinear identification and trajectory tracking via dynamic multilayer neural network (NN) with different timescales. Two NN identifiers are proposed for nonlinear systems identification via dynamic NNs with different timescales including both fast and slow phenomenon. The first NN identifier uses the output signals from the actual system for the system identification. In the second NN identifier, all the output signals from nonlinear system are replaced with the state variables of the NNs. The online identification algorithms for both NN identifier parameters are proposed using Lyapunov function and singularly perturbed techniques. With the identified NN models, two indirect adaptive NN controllers for the nonlinear systems containing slow and fast dynamic processes are developed. For both developed adaptive NN controllers, the trajectory errors are analyzed and the stability of the systems is proved. Simulation results show that the controller based on the second identifier has better performance than that of the first identifier.

Global tracking control of strict-feedback systems using neural networks

Most existing adaptive neural controllers ensure semiglobally uniform ultimately bounded stability on the condition that the neural approximation remains valid for all time. However, such a condition is difficult to verify beforehand. As a result, deterioration of tracking performance or even instability may occur in real applications. A common recourse is to activate an extra robust controller outside the neural active region to pull back the transient. Such an approach, however, has been restricted to dynamic systems with matched uncertainty. We extend it to strict-feedback systems with mismatched uncertainties via multiswitching-based backstepping methodology. Each virtual and actual controller of the proposed design switches between an adaptive neural controller and a robust controller, with the switching algorithm being sufficiently smooth and, hence, able to be incorporated with the backstepping tool. The overall controller ensures globally uniform ultimate boundedness while simultaneously avoiding the possible control singularity. Simulation results demonstrate the validity of the proposed designs.

Universal neural network control of MIMO uncertain nonlinear systems

In this brief, a continuous tracking control law is proposed for a class of high-order multi-input-multi-output uncertain nonlinear dynamic systems with external disturbance and unknown varying control direction matrix. The proposed controller consists of high-gain feedback, Nussbaum gain matrix selector, online approximator (OLA) model and a robust term. The OLA model is represented by a two-layer neural network. The continuousness of the control signal is guaranteed to relax the requirement for the actuator bandwidth and avoid the incurred chattering effect. Asymptotic tracking performance is achieved theoretically by standard Lyapunov analysis. The control feasibility is also verified in simulation environment.