Asymmetric Input Saturation Research Articles

Adaptive Vibration Iterative Learning Control of an Euler–Bernoulli Beam System With Input Saturation

This article focuses on solving the problem of vibration attenuation of an Euler–Bernoulli beam system considering imprecise system parameters, asymmetric input saturation, and external period disturbance. By employing the backstepping technique, a kind of boundary control scheme composed of parameter adaptive laws and iterative learning terms is recommended to attenuate vibration for the flexible beam system. And a functional auxiliary system is devised to make up for the influence of input nonlinearity on the system. With the presented control scheme, the well-posedness of the beam system is proved via semigroup theory and the output signal is guaranteed bounded with the aid of rigorous Lyapunov analysis. Eventually, a simulation experiment is available in the MATLAB to expatiate on the suggested controllers’ availability and simulation diagrams also highlight that the boundary controller based on parameter adaptive law with iteration term shows better control performance than that without iteration terms.

Decentralized Adaptive Fuzzy Finite-Time Event-Triggered Control for Interconnected Nonlinear Systems Subject to Input Saturation

This article investigates the decentralized adaptive finite-time command filter control for interconnected nonlinear systems subject to input saturation by event-triggered (ET) output feedback. In practice, some subsystems may be distributed distantly, making it difficult for a centralized controller to acquire feedback signals from them. On the other hand, the finite-time control scheme with a nonsmooth controller could be used for accelerating the system convergence rate. However, with this control scheme, it is difficult to exclude Zeno behavior directly by using the existing methods. To overcome these obstacles, we consider a decentralized control scheme and exploit the property of hyperbolic tangent functions and the approximation capability of fuzzy logic systems to handle interconnected nonlinear functions. Furthermore, we develop a new switching-type finite-time ET mechanism in the presence of asymmetric input saturation. Under the developed decentralized controller, all system states are bounded as well as the tracking error converges to a compact set near the origin in a finite time. Meantime, the function's one-sided differentiability is employed to avoid Zeno phenomenon. Finally, two practical examples are provided to demonstrate the effectiveness and advantages of the presented approach.

Distributed Observer-Based Adaptive Fuzzy Consensus of Nonlinear Multiagent Systems Under DoS Attacks and Output Disturbance.

This article studies the adaptive output-feedback consensus control problem of nonlinear multiagent systems (MASs) against denial-of-service (DoS) attacks. The attacks on the edges instead of nodes are considered, where we allow different attack intensities but at least one edge is connected in each attacking interval. Affected by output disturbance, the sensor feedback signal of every agent is inaccurate, which will reduce the approximation accuracy of the observer. Then, we design a signal to revise the sensor feedback signal subject to disturbance. Meanwhile, a prescribed performance function is used to ensure the transient and steady-state performance of error. Leveraging the Lyapunov stability theory and the backstepping technique, a distributed output-feedback control scheme subject to asymmetric saturation nonlinearity is designed. For the asymmetric input saturation, an auxiliary signal is designed to simplify the designed progress of controller input. To deal with the inherent problem of "explosion of complexity" emerging with backstepping, dynamic surface control is utilized. It is proved that the consensus errors converge to small neighborhoods of the origin, and all signals within the closed-loop system are bounded. Finally, simulation results are offered to demonstrate the effectiveness of the proposed method.

Heading and velocity guidance based path following of autonomous surface vehicle with uncertainty attenuation and asymmetric saturated constraints

The path following of underactuated autonomous surface vehicle (ASV) with line-of-sight (LOS)-based heading and velocity guidance is studied thoroughly in the presence of complex uncertainties and asymmetric input saturation that actuators are likely to suffer from. On the basis of the extended-state-observer-based LOS (ELOS) principle and guided velocity design strategies, a finite-time heading and velocity guidance control (HVG) scheme is presented. Firstly, an improved ELOS (IELOS) is developed such that the unknown sideslip angle can be estimated directly, instead of requiring one more step to calculate it by the output of observers and relying on the equivalent assumption between actual heading angle and guidance angle. Secondly, a new form of velocity guidance is designed by considering magnitude and rate constraints and path’s curvature, keeping in line with ASV’s manoeuvrability and agility. Then asymmetric saturation is considered and studied by designing projection-based finite-time auxiliary systems to avoid parameter drift. All error signals of the closed-loop system of ASV are forced to converge to an arbitrarily small neighbourhood of the origin within a finite settling time by the HVG scheme. The expected performance of the presented strategy is demonstrated via a series of simulations and comparisons. In addition, to show the strong robustness of the presented scheme, stochastic noises modelled by Markov process, bidirectional step signals and faults both multiplication and addition types are considered in simulations.

Consensus of double-integrator multi-agent systems with asymmetric input saturation

Consensus of double-integrator multi-agent systems with asymmetric input saturation

Adaptive neural control for chaotic permanent magnet synchronous motor with asymmetric input saturation

This paper develops an adaptive neural control scheme for chaotic permanent magnet synchronous motor with asymmetric input saturation and full-state constraints. To solve the state constrain problem, a barrier Lyapunov function is introduced. Then, a sliding-mode differentiator is designed to avoid the “explosion of complexity” issue. A simulation example is given to verify the validity of the proposed scheme.

Command‐filtered‐based neuroadaptive control for multi‐input multi‐output saturated nonstrict‐feedback nonlinear systems with prescribed tracking performance

SummaryIn this article, the prescribed performance control strategy is extended to multi‐input multi‐output nonstrict‐feedback nonlinear systems with asymmetric input saturation, and not only each element in tracking error vector converges to a prescribed small region within preassigned finite time, but also the converging mode during the preset time is prespecifiable and controllable explicitly. By blending the barrier function with novel speed function, a prescribed performance controller using command‐filtered‐based vector‐backstepping design framework is proposed to steer the tracking error vector for the first time, where the boundedness of filter errors is guaranteed by sufficiently small time constant and an error compensator is constructed to handle the effects of filter errors. To attenuate the adverse effects resulted from nondifferentiable input saturation, hyperbolic tangent function is utilized to estimate asymmetric saturation function such that the control input is designed as a new state variable with initial value of zero in augmented system. Nussbaum function is employed to overcome singularity problem caused by the differentiation of hyperbolic tangent function. At each step of backstepping design, the universal approximation property of neural network and the command filter system are utilized to approximate uncertain dynamics and to solve algebraic loop obstacle due to nonstrict‐feedback structure, respectively. Moreover, only one parameter needs to be updated online to cope with the lumped uncertain dynamics by virtual parameter technology, rendering a control strategy with low complexity computation. The validity of the presented controller is verified by theoretical analysis and two‐link robotic system.

Controller design for nonlinear systems subject to both input saturation and asymmetry time‐varying state constraints: A novel network‐based approach

SummaryIn this article, the issues of asymmetric input saturation and asymmetric time‐varying state constraints are integrated into nonlinear systems for the first time, and a new adaptive tracking control strategy is proposed based on the multi‐dimensional Taylor network (MTN) method. Firstly, by introducing a continuous auxiliary function, the input saturation is transformed into a smooth model with bounded error. Secondly, MTNs are employed to estimate nonlinear functions, asymmetric barrier Lyapunov functions (ABLFs) are constructed to make all state variables meet asymmetric constraints, and then a novel control scheme with simple structure is developed via backstepping. Thirdly, according to the Lyapunov stability theory, it is proved that all signals in the closed‐loop systems are bounded, and all state variables do not violate the constraints. Finally, three examples show the effectiveness of the proposed scheme.

Tracking control for large‐scale switched nonlinear systems subject to asymmetric input saturation and output hysteresis: A new adaptive network‐based approach

AbstractFor large‐scale switched nonlinear systems subject to asymmetric input saturation and output hysteresis, an adaptive control strategy is put forward by using a novel neural network, that is, multi‐dimensional Taylor network (MTN), which can effectively cope with the output tracking problem of this system. Firstly, asymmetric input saturation is expressed as the combination of a linear function and a bounded error function. Then, the modified Bouc‐Wen hysteresis model is employed to solve the nonlinear problem caused by output hysteresis. Afterwards, based on the approximation ability of MTN, a novel adaptive decentralized control method is designed by combining Lyapunov stability theory with adaptive backstepping technology, which realizes the stability and boundedness of the controlled systems. It should be noted that the asymmetric input saturation, the output hysteresis, large‐scale nonlinear systems and switched nonlinear systems appear in the same framework for the first time. Finally, a practical example and a numerical example are given to verify the availability of the proposed control strategy.

Anti-Windup Synthesis for Linear Systems With Asymmetric Input Saturation: Application to Aircraft Engine Control

This brief proposes a novel anti-windup scheme for linear systems subject to asymmetric input saturation. The asymmetrically saturated system is converted into an equivalent one with symmetric saturation through coordinate transformation. Based on the transformed model, we establish the set invariance condition of the resulting closed-loop system. This condition is less conservative since the non-symmetric nature of the saturation nonlinearity can be fully exploited in the control design. An optimization algorithm to maximize the invariant set is further developed for anti-windup gain design. The theoretical aspect of this method is analyzed rigorously, and the effectiveness is verified through an application to the aircraft engine control with experiments performed on a semi-physical test bed.

Event-triggered integral reinforcement learning for nonzero-sum games with asymmetric input saturation

In this paper, an event-triggered integral reinforcement learning (IRL) algorithm is developed for the nonzero-sum game problem with asymmetric input saturation. First, for each player, a novel non-quadratic value function with a discount factor is designed, and the coupled Hamilton–Jacobi equation that does not require a complete knowledge of the game is derived by using the idea of IRL. Second, the execution of each player is based on the event-triggered mechanism. In the implementation, an adaptive dynamic programming based learning scheme using a single critic neural network (NN) is developed. Experience replay technique is introduced into the classical gradient descent method to tune the weights of the critic NN. The stability of the system and the elimination of Zeno behavior are proved. Finally, simulation experiments verify the effectiveness of the event-triggered IRL algorithm.

Predictor‐based bipartite time‐varying formation control of nonlinear multi‐agents systems via disturbance observer

AbstractFormation control is one of critical control issues in multi‐agent systems (MASs). In this article, we extend the traditional formation to a bipartite one in a structurally balanced signed graph and propose a bipartite formation control strategy for an MAS with unknown external disturbances and nonsymmetric input saturation constraints. Neural networks (NNs) are developed to approximate followers' dynamics, and their transient performance are improved by prediction errors. On this basis, an NN‐based nonlinear disturbance observer (NDO) is constructed to estimate generalized disturbances including external disturbances and NNs approximation errors. In order to reduce the number of learning parameters, a normalization method is introduced for NNs weights and applied to the proposed NDO. An auxiliary system is constructed to compensate for asymmetric input saturation, and it is not required to access unknown control gains. The stability analysis is presented, and it is proven that all signals in the MAS are bounded. A numerical simulation and a team of quadrotors examples corroborate the effectiveness and feasibility of the strategy.

Fuzzy-based dynamic event triggering formation control for nonstrict-feedback nonlinear MASs

This paper investigates the dynamic event triggering formation control issue of nonlinear multiagent systems (MASs) with asymmetric input saturation and unmeasured states. Based on fuzzy logic systems approximation technique, a high-gain fuzzy observer is constructed to estimate the unavailable states, where a leader-follower formation control strategy is proposed to form a desired time-varying formation. Incorporating the network-induced error and the relative threshold strategy, two new dynamic event triggering mechanisms and dynamic rules of threshold parameters are established, which reduce the amount of communication between controller and actuator. Moreover, to counteract the adverse effect of asymmetric input saturation, a modified auxiliary system containing the information interaction is developed with the same order as the plant. It is ensured that all closed-loop signals are semi-globally uniformly ultimately bounded. Finally, simulation results are presented to illustrate the effectiveness of the proposed event triggering fuzzy control scheme.

Neuroadaptive control of information-poor servomechanisms with smooth and nonsmooth uncertainties

For motion control of uncertain servomechanisms, nonlinear dynamics including smooth and nonsmooth types, external disturbances, signal measurement noises, asymmetric input saturation, and so on seriously hinder the further development of high-performance closed-loop control algorithms. However, already existing control strategies cannot address the above-mentioned issues at the same time. It greatly increases the difficulty of controller design especially when some states are not measurable. Inspired by above motivations, this paper exploits neural networks to deal with nonlinear dynamics including discontinuous types, and combines extended state observers to estimate disturbances and unmeasurable states for uncertain nonlinear servomechanisms. Meanwhile, the desired-command-based model compensation approach is integrated into the controller design. It is worth noting that the neural network weights are updated by the combination of the estimation error and tracking error to acquire better approximation accuracy. According to above technologies, a novel extended-state-observer-based neural network adaptive motion control algorithm will be synthesized. The bounded stability of the whole closed-loop system is proved strictly. In addition, the comparisons of the application results on an electro-hydraulic servo system verify the availability and superiority of the developed control algorithm.

Adaptive finite-time tracking control for full state constrained nonlinear systems with time-varying delays and input saturation

This paper investigates the adaptive finite-time tracking control problem for a class of nonlinear full state constrained systems with time-varying delays and input saturation. Compared with the previously published work, the considered system involves unknown time-varying delays, asymmetric input saturation, and time-varying asymmetric full state constraints. To ensure the state constraint satisfaction, the appropriate time-varying asymmetric Barrier Lyapunov Functions and the backstepping technique are utilized. Meanwhile, the finite covering lemma and the radial basis function neural networks are employed to solve the unknown time-varying delays. The assumption that the time derivative of time-varying delay functions is required to be less than one in traditional Lyapunov–Krasovskii functionals is removed by the proposed method. Moreover, the asymmetric input saturation is handled by an auxiliary design system. Taking the norm of the neural network weight vector as an adaptive parameter, a novel adaptive finite-time tracking controller with minimal learning parameters is constructed. It is proved that the proposed controller can guarantee that all signals in the closed-loop system are bounded, all states are constrained within the predefined sets, and the tracking error converges to a small neighborhood of the origin in a finite time. Finally, a comparison study simulation is given to demonstrate the effectiveness of our proposed strategy. The simulation results show that our proposed strategy has good advantages of high tracking precision and disturbance rejection.

Extended‐state‐observer‐based dynamic surface control of flexible‐joint robot systems with input saturation

SummaryThis article presents an extended‐state‐observer‐based dynamic surface control approach for flexible‐joint robot systems with asymmetric input saturation and large unknown dynamic knowledge. Traditional controllers for flexible‐joint robot systems usually use approximation technology to deal with unknown dynamics knowledge. Unlike the traditional control algorithm, this article utilizes an extended state observer to estimate the unknown dynamics. For the closed‐loop system, the delay strategy handles the time‐scale separation issue, the filtering system overcomes the “explosion of differentiation” caused by the repeated differentiation of auxiliary control signals, and the mean‐value‐theorem solves the input saturation problem of the actuator. The stability analysis implies that estimation errors of extended state observers (ESOs) and other state variables are semiglobally uniformly ultimately bounded. Compared with fuzzy control algorithms, the novel ESO‐based dynamic surface control approach not only omits online learning time but also uses only a few control parameters to obtain satisfactory tracking performance. Finally, a comparison simulation experiment is provided to illustrate the effectiveness of the gained conclusions.

Command filter-based adaptive neural finite-time control for stochastic nonlinear systems with time-varying full-state constraints and asymmetric input saturation

This article studies a tracking control problem for a class of stochastic nonlinear systems with time-varying full-state constraints and asymmetric input saturation. Firstly, the Gauss Error Function is introduced to solve the difficulty arising from the saturation nonlinearity. Meanwhile, to overcome the problem of calculating explosion caused by the repeated differentiation of the virtual control signals, a finite-time command filter with a compensation mechanism is developed. Combining the neural networks' approximation ability and the backstepping technique, an adaptive neural finite-time control strategy is proposed for the considered system by constructing the time-varying barrier Lyapunov function. Under the proposed control strategy, it is guaranteed that all signals are bounded in a sense of mean square, the output of the system can track the reference signal within a finite time and all states will not violate the constraints. Furthermore, the stability of the closed-loop system is analysed based on the stochastic finite-time stability theory. Finally, a simulation example verifies the effectiveness of the proposed control strategy.

Event-Triggered Output Feedback Control of Switched Nonlinear Systems With Input Saturation.

This article is concerned with the event-triggered output feedback control problem for a class of switched nonlinear strict-feedback systems subject to asymmetric input saturation. The nonlinear terms are assumed to be bounded by a continuous function of the output multiplied by unmeasured states. The hyperbolic tangent function is employed to process the error caused by the event-triggered scheme, and an indicator function of the saturation degree is used to analyze the influence generated by the asymmetric input saturation. By adopting the common Lyapunov function method and the dynamic gain control design approach, a new design procedure based on a reduced-order observer is proposed to construct an output feedback controller. It is proved by the Lyapunov analysis that the proposed event-triggered control scheme can ensure that all the signals of the closed-loop system are globally bounded. Furthermore, the output can be converged to a bounded region around the origin, and this region can be tuned to be small by adjusting the design parameters. Different from typical existing results on the switched nonlinear strict-feedback systems, the celebrated backstepping method is not employed in this article. The continuous stirred tank reactor is finally used to demonstrate the effectiveness of the proposed control scheme.

Adaptive multi‐dimensional Taylor network funnel control of a class of nonlinear systems with asymmetric input saturation

SummaryIn view of the result and performance of control are affected by the existence of input constraints and requirements, adaptive multi‐dimensional Taylor network (MTN) funnel control problem is studied for a class of nonlinear systems with asymmetric input saturation in this paper. Firstly, the effect of asymmetric input saturation can overcome by introducing the Gaussian error function, namely, the asymmetric saturation model is represented as a simple linear model with a bounded disturbance. Secondly, MTNs are employed to approximate the unknown functions in the controller design. Then, an adaptive MTN tracking controller is developed by blends the idea of funnel control into backstepping, which can guarantee that the tracking error always meets the given prescribed performance regarding the transient and steady state responses as well as the output of system tracks the give continuous reference signal. Finally, the effectiveness of the proposed control is demonstrated using two examples.

Disturbance-observer-based finite-time adaptive fuzzy control for non-triangular switched nonlinear systems with input saturation

This paper investigates finite-time adaptive fuzzy control for a class of non-triangular switched nonlinear systems with asymmetric input saturation and mismatched external disturbances. Firstly, the mismatched external disturbances and fuzzy approximation errors are regarded as total disturbances of the system, which are estimated accurately via the finite-time exact disturbance observer. Secondly, the prescribed performance function is adopted to constrain the system output to meet the prescribed dynamic properties. To cope with the obstacle arising from asymmetric input saturation, a smooth nonlinear function is applied to approximate the actuator nonlinearity by utilizing the mean-value theorem. Thirdly, on the basis of the fast practical finite-time stability criteria, a finite-time adaptive fuzzy control where the adaptive parameter laws contain fractional-order item is developed. Meanwhile, the stability analysis verifies that all the signals in closed-loop system are bounded and error signals can converge to the prescribed small compact sets in finite time. Finally, a simulation example is included to further demonstrate the effectiveness of the proposed control strategies.