Asymmetric Output Research Articles

Adaptive observer-based control of arbitrarily switched fractional-order uncertain nonlinear system subject to input nonlinearities and asymmetric output constraints

This paper addresses an adaptive observer-based control for a switched fractional-order uncertain nonlinear system subject to input nonlinearities and asymmetric output constraints. The switching signal is arbitrary, and the designed controller needs no information regarding this signal. By employing a universal framework, the designed controller can cope with different input nonlinearities in different modes without having any information about their characteristics. It is assumed that only system output is measured, and a designed observer estimates the system states for control implementation. The uncertainties are approximated by intelligent approximators like fuzzy logic systems (FLS) or neural networks (NN). The time-varying asymmetric output constraints have been satisfied using a common barrier Lyapunov function (BLF). Using the common Lyapunov function (CLF) method and the backstepping control technique, the virtual controllers, adaptive law and control input have been designed, and the boundedness of closed-loop signals has been guaranteed. The effectiveness of the designed control scheme has been demonstrated through a numerical and two practical simulation examples.

Adaptive Event-Triggered Control for Nonlinear Systems With Asymmetric State Constraints: A Prescribed-Time Approach

Finite/Fixed-time control yields a promising tool to optimize a system&#x0027;s settling time, but lacks the ability to separately define the settling time and the convergence domain (known as <i>practically prescribed-time stability</i>, PPTS). We provide a sufficient condition for PPTS based on a new piecewise exponential function, which decouples the settling time and convergence domain into separately user-defined parameters. We propose an adaptive event-triggered prescribed-time control scheme for nonlinear systems with asymmetric output constraints, using an exponential-type barrier Lyapunov function. We show that this PPTS control scheme can guarantee tracking error convergence performance, while restricting the output state according to the prescribed asymmetric constraints. Compared with traditional finite/fixed-time control, the proposed methodology yields separately user-defined settling time and convergence domain without the prior information on disturbance. Moreover, asymmetric state constraints can be handled in the control structure through bias state transformation, which offers an intuitive analysis technique for general constraint issues. Simulation and experiment results on a heterogeneous teleoperation system demonstrate the merits of the proposed control scheme.

Disturbance‐observer‐based adaptive finite‐time dynamic surface control for PMSM with time‐varying asymmetric output constraint

AbstractThis article presents a disturbance‐observer‐based adaptive finite‐time dynamic surface control scheme, capable of guaranteeing transient behavior for the PMSM with arbitrary asymmetric time‐varying output constraint and unmatched external disturbance. The major challenge of this paper is devising efficient strategies to tackle the nonsymmetric output restraints with arbitrary characteristics and unmatched external perturbation for the system under the finite‐time backstepping framework. Given this, a nonlinear transformation function is adopted to coordinate from the output‐constrained dynamic model to an uncontained one, and a finite‐time disturbance observer is introduced to evaluate the unmatched external perturbation. Then, a dynamic surface control approach having adaptive properties for PMSM is conceived by combing a neural network to evaluate the nonlinear functions and a first‐order filter to handle the “explosion of complexity.” Additionally, it is proved that the signals in the closed‐loop system can narrow down to a bounded region and the tracking error can merge in a limited time to tiny vicinity of zero by employing a fast finite‐time stability principle. Eventually, simulation cases and contrast results reveal the tracking performance and immunity to the disturbance of the devised controller.

Adaptive neural network fixed‐time control of p$$ p $$‐normal systems with asymmetric output constraints

AbstractThis paper studies the problem of fixed‐time control for a class of nonlinear ‐normal systems with asymmetric output constraints and external disturbances. Compared with the existing fixed‐time control schemes, the smooth switching functions are designed to avoid the singularity problem. Then, a time‐varying nonlinear transformation function (NTF) is introduced, blue and a unified tool to deal with the symmetric, asymmetric, and even unconstrained problems simultaneously. Moreover, by adding blue one power integration technology, the controller is designed, which can inhibit the influence of external disturbances on the system. According to Lyapunov stability analysis, the presented fixed‐time blue control method ensures that all signals are bounded in a fixed time. In the end, two simulation examples demonstrate the effectiveness of the proposed scheme.

A nonlinear autoregressive distributed lag analysis on the macroeconomic effect of global oil price in the Philippines

AbstractStudies have pointed out that oil price volatility influenced economic output and growth among developing and net oil‐importing countries. More than a quarter of the Philippines’ energy demand depends on crude oil, and 99% of the crude oil demand is imported mainly from the Middle East. This study was conducted to determine if there is a nonlinear or asymmetric output response to oil price movements and examine which economic sector in the Philippines is the most vulnerable to oil shocks. This contributes to the literature for a case of developing, oil importing, inflation targeting and post‐oil industry deregulated economy. A nonlinear autoregressive distributive lag model was applied to observe quarterly data from 1998:Q1 to 2019:Q4 of the relevant economic variables. Results revealed an asymmetric effect of non‐oil variables on sectoral performance. World oil prices affect economic sector outputs symmetrically or linearly. The service sector is found to be the most vulnerable sector to oil price shocks. The absence of asymmetry can be attributed to increased competition in the Philippines’ domestic oil industry brought about by oil market deregulation. Improving the energy mix and reducing oil intensity is vital to offset any oil price shock transmission to an oil‐importing economy like the Philippines.

Multiapproximator-based adaptive fault-tolerant control for teleoperation systems with deferred asymmetric time-varying output constraints

Multiapproximator-based adaptive fault-tolerant control for teleoperation systems with deferred asymmetric time-varying output constraints

Adaptive Command Filtered Control of Random Nonlinear Systems Under Output Constraint

Random nonlinear systems (RNSs) are claimed to be superior than well-investigated stochastic nonlinear systems (SNSs) in dealing with general noises. This brief considers the question of adaptive command filtered control (CFC) of a class of triangular RNSs under asymmetric output constraint. The constrained problem is turned into an unconstrained one by a nonlinear transformation at first. Then by combining CFC with adaptive technique, an adaptive controller is designed recursively to achieve the tracking objective, as well as keeping the output constraint. Finally, the validity of main results is tested via a numerical simulation example.

Consensus for Multiagent Systems Under Output Constraints and Unknown Control Directions

In this article, we design an adaptive leader&#x2013;follower consensus controller for a class of nonlinear multiagent systems in the presence of time-varying asymmetric output constraints and unknown control directions. A new state transformation approach is introduced for each agent to transform the output into an equivalent unconstrained state. An adaptive neural network-based backstepping control method and a Nussbaum function approach are integrated to design the leader&#x2013;follower consensus controller that compensates for the unknown control directions and guarantees that the consensus tracking error converges to a small compact set. Examples are given to demonstrate the effectiveness of the proposed new design techniques.

Finite-time state feedback stabilization of strict-feedback switched systems with asymmetric output-constraints

Finite-time stabilization of strict-feedback switched systems with asymmetric output constraints (AOCs) via state feedback is investigated. First, an elaborately constructed fraction-type barrier Lyapunov function (BLF) is presented. Then, with a mild assumption on strict-feedback switched systems, state feedback laws are constructed by revamping the adding a power integrator approach (AAPIA) and meanwhile a common Lyapunov function (CLF) is also obtained. The resultant closed-loop systems are finite-time stable (FTS) and the asymmetric output constraint is satisfied too. The approach, proposed in this note, can make strict-feedback switched systems with/without AOCs finite-time stable in a unified frame.

Composite Learning Finite-Time Control of Robotic Systems With Output Constraints

In this article, for robotic systems with uncertain dynamics and time-varying asymmetric output constraints, we present a composite learning finite-time control scheme with salient features benefited from two design steps. In the first step, unlike existing composite adaptive/learning control algorithms, which result in either exponential convergence or finite-time convergence but exhibit a potential singularity issue, a modified nonsingular terminal sliding mode-based composite learning controller is adopted such that both tracking error and parameter estimation error converge to zero in finite time without singularity. The unknown parameter learning law is constructed by using online historical data and regressor extension, which gives a benefit of relaxing the typically required stringent persistent excitation with a much weaker excitation condition termed interval excitation. In the second step, a universal time-varying asymmetric barrier function (UTABF) is adopted to directly constrain the system output rather than the most commonly used barrier Lyapunov function, which indirectly converts the output constraints into the conservative tracking error constraints. Moreover, the UTABF can handle both constrained and unconstrained cases uniformly without the need for changing the control structure. Both theoretical analysis and experiments results on an industrial manipulator confirm the benefits and effectiveness of the proposed control scheme.

Finite-Time Adaptive Fuzzy Event-Triggered Control of Constrained Nonlinear Systems via Bounded Command Filter

In this note, finite-time adaptive tracking control is studied for nonlinear systems with unmodeled dynamics, asymmetric time-varying output constraints, and uncertain disturbances. Without any growth conditions, fuzzy logic systems are applied to tackle the unknown complicated functions. A novel backstepping approach is developed by combining barrier Lyapunov functions and bounded finite-time command filter. By regulating the threshold parameters online, a new dynamic event-triggered controller is established to ensure that all the signals of the closed-loop system are bounded and the output can follows the preset signal in finite time. Meanwhile, the output is always staying in an asymmetric time-varying interval all the time. The feasibility of the proposed control algorithm is verified with a numerical example and a practical application.

Global Prescribed-Time Second-Order Sliding Mode Controller Design Subject to Asymmetric Output Constraint

Global Prescribed-Time Second-Order Sliding Mode Controller Design Subject to Asymmetric Output Constraint

Finite‐time adaptive fault‐tolerant control for output‐constrained interconnected systems with unknown control coefficients

AbstractThis article studies the adaptive finite‐time tracking control problem for a class of output‐constrained nonlinear interconnected systems with unknown actuator faults and parametric uncertainties. By integrating the lower bounds of unknown virtual control coefficients into the Lyapunov functions design, an adaptive control strategy is proposed on the basis of adaptive estimations and some smooth functions, which make the controller effectively compensate the effects of unknown virtual control coefficients, unknown interconnected terms, and other uncertain nonlinear functions. Besides, the controller can also compensate the effect of actuator faults by fusing a two‐step design method into the backstepping design framework. Simultaneously, in view of the output constraint requirements, a barrier Lyapunov function is introduced, and it is a more general one that can reckon with time‐varying asymmetric output constraints. Furthermore, in light of the finite‐time stability criterion, all the signals of system are bounded and the tracking performance can be guaranteed in a finite time. Finally, the effectiveness of the proposed method is validated by some simulation results.

Adaptive Deformation Control of a Flexible Variable-Length Rotary Crane Arm With Asymmetric Input-Output Constraints.

This article constructs two adaptive control laws to achieve deformation reduction and attitude tracking for a rotary variable-length crane arm with system parameter uncertainties and asymmetric input-output constraints. Two auxiliary systems are given to deal with the input constraints, an asymmetric-logarithm-barrier Lyapunov function is established for achieving the asymmetric output constrains, and five adaptive laws are constructed to handle system parameter uncertainties. Besides, the control design is based on a partial differential equation model, and the S-curve acceleration and deceleration method is used for regulating the arm extension speed. Both the system stability and uniform ultimate boundedness of the controlled crane arm are analyzed. Simulation results validate the effectiveness of our established control laws.

Neural adaptive finite-time dynamic surface control for the PMSM system with time delays and asymmetric time-varying output constraint

This paper presents a neural adaptive finite-time dynamic surface control for the permanent magnet synchronous motor system with time delays and asymmetric time-varying output constraint. The core challenge is how to address the time delays and asymmetric output constraint when designing a finite-time control scheme. Given this, a proper Lyapunov–Krasovskii functional is introduced to address time delays, and a nonlinear transformation function is considered to convert the output-constrained problem into an unconstrained one. Then, a neural adaptive finite-time dynamic surface control approach is devised in the finite-time backstepping framework, which applies neural networks to estimate the unknown nonlinear functions and introduces first-order filters to solve the “explosion of complexity” problems. Furthermore, it is demonstrated that all the signals of the resulting system are finite-time stable and the tracking error converges to a neighborhood of origin in finite time without violating the output constraint. Finally, the simulation results show that the integration of squared error results, the integral of time and absolute error results as well as the integration of absolute value error results of the proposed scheme is smaller than the tested scheme by 0.3458 , 22.2977 , and 2.2513 , respectively, when the time delays are considered. It further elucidates the availability and superiority of the developed method.

Prescribed-time stabilization for high-order nonlinear systems with a pre-specified asymmetric output constraint

This article considers the problem of prescribed-time stabilization for a class of uncertain high-order nonlinear systems (i.e., systems in the p-normal form) with a pre-specified asymmetric output constraint. A core ingredient, tangent-type barrier function, is proposed first by skillfully excavating and assimilating the inherent properties of system nonlinearities. Based on the barrier function, as well as a serial of nested signum functions, the celebrated technique of adding a power integrator is renovated finely to establish a new design approach by which a continuous state feedback prescribed-time stabilizer, along with a tangent-type asymmetric barrier Lyapunov function, can be constructed in a systematic fashion, thereby guaranteeing the performance of prescribed-time state convergence and ensuring the fulfillment of pre-specified output constraints surely. Benefiting from the composite characteristics of the presented tangent-type barrier Lyapunov function and the signum functions, the proposed approach further offers a unified nature in design enabling us to organize a prescribed-time stabilizer that is simultaneously valid and executable for the system undergone or free from output constraints, without the need of changing the controller structure. The effectiveness and superiority of the developed approach are illustrated by a numerical example.

Multi-output AC/DC triboelectric generator with dual rectification

Triboelectric generators (TEGs) are state-of-the-art powering solutions for low-power Internet of Things (IoT) devices. TEGs convert periodic mechanical vibrations into cyclic charge flow between electrodes, establishing an alternating current (AC). AC output requires rectification prior to charging any load, creating additional circuitry and power requirements. In recent years, the focus has shifted towards developing direct current (DC) generators as potential direct powering solutions. In this work, a DC–TEG is realized using a novel topology involving mechanical switching between a pair of metal contacts. This switching is implemented in vertical contact-separation mode and therefore does not involve any significant friction between the electrodes and metal contacts, overcoming the major limitation of wear and tear in conventional mechanical switching DC–TEGs. The proposed device can obtain each half-cycle rectified output across distinct electrode pairs, which is crucial for conditioning the asymmetric output of the TEGs. An AC output can be simultaneously harvested between the pair of bottom electrodes. The complementary AC–DC outputs of this device can supply power to multiple loads at a time, or coupled together to produce an enhanced output. Placing two complementary TEG pairs adjacent to each other in this topology has the advantage of reducing the ambient electric field, which is critical in improving the charge retention capability of the TEG layers. The proposed device achieves peak instantaneous power of 1.05 μW for the DC output and 1.12 μW for the AC output across a 7 MΩ resistance. The applications of the device in driving commercial electronics, harvesting biomechanical energy, and as progressive alarming system are demonstrated. Due to its simple, flexible topology and AC-DC dual output, this device has the potential for powering multiple loads and applications in diverse scenarios.

Distributed fuzzy adaptive event‐triggered finite‐time consensus tracking control for uncertain nonlinear multi‐agent systems with asymmetric output constraint

AbstractThis article investigates the consensus problem for uncertain nonlinear multi‐agent systems (MASs) with asymmetric output constraint. Different from BLF‐based constraint consensus tracking control, a novel approach based on nonlinear state‐dependent function is proposed to solve the asymmetric output constraint, which need not convert output constraint into tracking error bound. First‐order sliding mode differentiator is incorporated into each step of backstepping control design to reduce computation burden. Further, in combination of proposed event‐triggered mechanism based on time‐varying threshold, a distributed fuzzy adaptive event‐triggered finite‐time consensus method is developed. It can ensure that the consensus tracking error tends to a small neighbor in a finite time and the asymmetric output constraint of each subsystem is not violated. Two simulations are given to demonstrate the effectiveness of control method.

Depth and Heading Control of a Manta Robot Based on S-Plane Control

Bionic underwater robots have many advantages such as high mobility, high efficiency, high affinity, etc. They are especially suitable for tasks such as collecting hydrographic information and for detailed surveys of the marine environment. These tasks are based on their high-precision attitude control. Therefore, this paper proposes a control scheme for a bionic underwater robot—a manta robot. To improve the depth retention capability of the manta robot, a S-plane controller based on asymmetric output was designed in combination with the longitudinal motion characteristics of the manta robot. In addition, to achieve good motion control for the manta robot under conditions of large changes in the heading angle, the fuzzy controller and the heading transition target value function were combined to design the heading controller of the manta robot. Finally, the feasibility and reliability of the control system of the manta robot were verified by pool experiments. The experimental results showed that the depth control error was within ±5 cm and the heading control error was within ±5 degrees. The control scheme proposed in this paper achieves high-precision attitude control of the manta robot, providing a basis for the practical application of the manta robot.

Adaptive neural control for uncertain constrained pure feedback systems with severe sensor faults: A complexity reduced approach

This paper investigates the output tracking control problem for multi-input multi-output (MIMO) uncertain pure-feedback systems subject to time-varying asymmetric output constraints, where the states are polluted by multiplicative/additive faults. By incorporating some auxiliary functions into a backstepping-like design procedure, a smooth adaptive control scheme is constructed using neural network (NN) approximation, making the closed-loop dynamics exhibits a unique feasible solution with all the involved signals evolving within some compact sets during a finite time interval. As a result, the safety and reliability of the application of NN approximators is guaranteed in advance and the algebraic loop issue arising from the control input coupling is removed completely. Thereafter, by combining the Lyapunov stability analysis with contradiction, the boundedness of those signals over the entire time domain is established. It is shown that with the proposed control scheme, the impact of the sensor faults from all state (except for output) on the output tracking is counteracted automatically while maintaining the output constraints. Furthermore, the proposed method enlarges the pure feedback systems considered by relaxing the state-of-the-art controllability conditions. Finally, the efficacy of the approach is verified and clarified via simulation studies.