Output Dead Zone Research Articles

Fuzzy prescribed-time self-triggered consensus control for nonlinear multi-agent systems with dead-zone output

This paper investigates the fuzzy prescribed-time self-triggered consensus control for nonlinear multi-agent systems (MASs) with dead-zone output. First, by proposing a dead-zone output approximation model and utilizing Nussbaum-type functions, an adaptive compensation mechanism is developed to alleviate the effect of unknown dead-zone output. Meanwhile, the fuzzy logic systems are incorporated to approximate the unknown functions of the nonlinear MASs. Next, by adopting a time-varying scaling function, a consensus control method is recursively established to steer the consensus errors into a small range within the prescribed time. Furthermore, a self-triggered scheme is established to conserve communication resources while avoiding continuously monitoring trigger conditions. It is illustrated that boundedness of all signals and practical prescribed-time leader-following consensus can be achieved. Finally, the simulation results show the effectiveness of the proposed method.

Adaptive fault-tolerant control for a class of nonstrict-feedback nonlinear systems with unmodeled dynamics and dead-zone output using multi-dimensional taylor networks

Adaptive fault-tolerant control for a class of nonstrict-feedback nonlinear systems with unmodeled dynamics and dead-zone output using multi-dimensional taylor networks

Distributed Formation Maneuvering Quantized Control of Under-Actuated Unmanned Surface Vehicles with Collision and Velocity Constraints

This paper focuses on a distributed cooperative time-varying formation maneuvering issue of under-actuated unmanned surface vehicles (USVs). A fleet of USVs is guided by a parameterized path with a time-varying formation while avoiding collisions and preserving the connectivity in the environment with multiple obstacles. In some surface missions, due to the obstacles in the external environment, the bandwidth limitations of the communication channel, and the hardware components/performance constraints of the USVs themselves, each vehicle is considered to be subject to model uncertainty, actuator quantization, sensor dead zone, and velocity constraints. During the control design process, the radial basis function (RBF) neural networks (NNs) are utilized to deal with nonlinear terms. Based on a nonlinear decomposition method, the relationship between the control signal and the quantization one is established, which overcomes the difficulty arising from actuator quantization. A Nussbaum function is introduced to handle the unknown output dead zone problem caused by reduced sensor sensitivity. Moreover, a universal-constrained function is employed to satisfy both the constrained and unconstrained requirements during formation keeping and obstacle avoidance. The Lyapunov stability theory confirmed that the error signals are uniformly ultimately bounded (UUB). The simulation results demonstrate the effectiveness of the proposed distributed formation control of multiple USVs.

Finite-Time Prescribed Performance-Based Adaptive Fuzzy Tracking Control for Switched Nonlinear Systems with Output Dead Zone

Finite-Time Prescribed Performance-Based Adaptive Fuzzy Tracking Control for Switched Nonlinear Systems with Output Dead Zone

Global practical stabilization of the double integrator system with an imperfect sensor and subject to a bounded disturbance

This paper is concerned with global practical stabilization of the double integrator system with an imperfect sensor and subject to an additive bounded output disturbance. The imperfect sensor nonlinearity possesses the nonlinear characteristics of saturation and dead zone. Because of the presence of output dead zone and the additive disturbance, the states cannot be expected to driven into an arbitrarily small neighborhood of the origin. To solve the global practical stabilization problem, we proposes a low gain-based linear dynamic output feedback law, under which the first state enters and remains in a bounded set whose size is depended on the bound of disturbance and the range of dead zone and the second state enters and remains in a pre-specified arbitrarily small set, both in finite time. Simulation results illustrate the effectiveness of our proposed control method.

Nonsingular finite-time heading tracking control of marine vehicles with tracking error constraints

This brief copes with the finite-time heading tracking control for marine vehicles. The tracking error constraints, stochastic disturbance and dead-zone output are simultaneously taken into consideration in the controlled systems. To manage the dead-zone output issue, a smooth approximation model is brought in to offset the nondifferentiable phenomena during the control, besides, the adaptive technique and the Nussbaum function are simultaneously integrated into the design. For the tracking error constraints, the prescribed performance functions (PPF) with error transformations are designed, and novel stochastic Barrier Lyapunov functions are constructed accordingly depending on the newly tracking errors to deal with the constrained tracking control issue. To avert the “differential explosion” issue in the backstepping, the improved first-order filter technique is invoked. The proposed finite-time heading tracking control tactic renders that all variables are bounded, meanwhile, the tracking errors are kept in the pregiven boundaries. The effectiveness of theory results is substantiated via simulations.

Observer Based Control for a Class of Systems with Output Deadzone Nonlinearity

Observer Based Control for a Class of Systems with Output Deadzone Nonlinearity

Adaptive fixed-time consensus control of nonlinear multiagent systems with dead-zone output

This paper proposes a fixed-time leader-follower consensus tracking control scheme for uncertain multiagent systems with unknown dead-zone output. First, a novel approximate dead-zone model was introduced to accurately represent the dead-zone phenomenon in the output channel of nonlinear multiagent systems. Furthermore, the model presented is a smooth function, which greatly simplifies the process of fusing it with the backstepping technique. Second, we present a Nussbaum function to compensate for the uncertain gain from the output dead-zone nonlinearity. Third, unlike previous works, we first consider practical fixed-time consensus issues for multiagent systems with output dead-zone. Finally, according to the fixed-time control theory, the tracking error of the multiagent systems converges to the region around zero within a fixed time, which is confirmed by the simulations.

Neural networks-based adaptive output-feedback control design for nonlinear systems with dead zone output and uncertain disturbances

This work focuses on the issue of neural networks-based adaptive output-feedback control for nonlinear systems with dead zone output and immeasurable states. Radial basis function neural networks (RBFNN) are utilised to approximate the unknown functions and an input-driven filter is used to estimate the immeasurable states. Nussbaum function is employed to address the issue of uncertain virtual control coefficient, which is brought by the dead zone in the output mechanism, and the presented control scheme requires only one adaptive law, making the structure of the controllers very realistic. Based on the approximation capabilities of NNs and the backstepping method, an adaptive controller is designed. Based on the Lyapunov stability theory, all signals in closed-loop systems are semi-globally uniformly ultimately bounded (SGUUB), and the tracking error converges to a small area near the origin. The effectiveness of the proposed adaptive control method is proved with the help of two examples.

Event-triggered adaptive control for stochastic nonlinear systems with time-varying full state constraints and output dead-zone

This paper studies the problem of event-triggered adaptive control for a class of strictly-feedback stochastic nonlinear systems. The output dead zone and time-varying full-state constraints are considered in the controller. Firstly, time-varying barrier Lyapunov functions (TVBLFs) are introduced to address problems of time-varying state constraints. Then, the Nussbaum-type function is used to compensate for the influence of the output dead zone. The problem of state constraint is well solved when the system’s output signal is affected by the dead zone, which satisfies a wider application space. In addition, a new relative threshold of the event-triggered control (ETC) scheme is considered to save communication resources, and the Zeno behavior is excluded. This scheme makes the trigger interval more flexible and optimizes the performance of the controller. Moreover, the error compensation signals reduce the filtered errors and simplify the algorithm. Simultaneously, an adaptive tracking controller with the backstepping technique is designed so that all signals in the closed-loop system are bounded and the tracking error converges to a small residual set of the origin. Finally, two simulation examples are given to demonstrate the effectiveness of the control method.

Recursive Estimation for System With Random Transmission Loss and Censored Measurement

Censored measurements are frequently occurring phenomena in sensor systems with the characteristics of output dead-zone or sensor saturation. In censoring sensor network systems, random transmission loss is inevitable, which results in the two measurement uncertainties occurring simultaneously. In this study, we address this problem and attempt to present an efficient solution. First, the random transmission losses are described by a set of Bernoulli random variables, and then the censored random matrix and translation transformation are used to modify the traditional Tobit censored measurement model, which includes calculating the probability that the latent measurement is not censored when the transmission is lost and is only pure noise. Then, an optimal recursive filtering algorithm for a system with the two uncertain measurements is derived by using the innovation analysis method and the full expectation rule. The effectiveness and superiority of the algorithm is verified by simulating an oscillator model and comparing with other existing algorithms.

Nussbaum gain adaptive neural asymptotic tracking of nonlinear systems with full-state constraints

The adaptive neural tracking control problem of a class of strict-feedback nonlinear systems with unknown control directions (UCD) and full-state constraints is investigated in this paper. The neural network (NN) is adopted to identify the totally unknown nonlinear functions. In the meanwhile, by resorting to the Nussbaum gain technique, the effects caused by the UCD and output dead zone are counteracted. Given physical limits and safety demands, a novel barrier Lyapunov function (BLF)-based adaptive neural control scheme is devised for the strict-feedback nonlinear systems to ensure that the constraints are not violated during operations. Besides, a rigorous theoretical analysis has been given to indicate that all of the closed-loop signals are bounded and the tracking error achieves asymptotic convergence performance. Finally, the effectiveness and flexibility of our proposed scheme are illustrated by two numerical examples.

Adaptive Fuzzy PI Output Feedback Control for a Class of Switched Nonlinear Systems with Unmodeled Dynamics and Dead-Zone Output

Adaptive Fuzzy PI Output Feedback Control for a Class of Switched Nonlinear Systems with Unmodeled Dynamics and Dead-Zone Output

Approximation-Based Nussbaum Gain Adaptive Control of Nonlinear Systems With Periodic Disturbances

This article considers the Nussbaum gain adaptive control issue for a type of nonlinear systems, in which some sophisticated and challenging problems, such as periodic disturbances, dead zone output, and unknown control direction are addressed. The Fourier series expansion and radial basis function neural network are incorporated into a function approximator to model time-varying-disturbed function with a known period in nonlinear systems. To deal with the problems of the dead zone output and unknown control direction, the Nussbaum-type function is recommended in the design of the control algorithm. Applying the Lyapunov stability theory and backstepping technique, the proposed control strategy ensures that the tracking error is pulled back to a small neighborhood of origin and all closed-loop signals are bounded. Finally, simulation results are presented to show the availability and validity of the analysis approach.

Adaptive Attitude Control for Multi-MUAV Systems With Output Dead-Zone and Actuator Fault

Many mechanical parts of multi-rotor unmanned aerial vehicle (MUAV) can easily produce non-smooth phenomenon and the external disturbance that affects the stability of MUAV. For multi-MUAV attitude systems that experience output dead-zone, external disturbance and actuator fault, a leader-following consensus anti-disturbance and fault-tolerant control (FTC) scheme is proposed in this paper. In the design process, the effect of unknown nonlinearity in multi-MUAV systems is addressed using neural networks (NNs). In order to balance out the effects of external disturbance and actuator fault, a disturbance observer is designed to compensate for the aforementioned negative impacts. The Nussbaum function is used to address the problem of output dead-zone. The designed fault-tolerant controller guarantees that the output signals of all followers and leader are synchronized by the backstepping technique. Finally, the effectiveness of the control scheme is verified by simulation experiments.

Prescribed performance adaptive fuzzy control for nonstrict‐feedback nonlinear systems with dead zone outputs

SummaryThis paper presents an adaptive fuzzy control scheme for a class of nonstrict‐feedback nonlinear systems with dead zone outputs and prescribed performance. By utilizing the monotonically increasing property of system bounding functions and the Nussbaum function, the design difficulties caused by the nonstrict‐feedback structure and dead zone output are overcome. Combining backstepping technique with prescribed performance algorithm, a feasible adaptive fuzzy controller is designed to guarantee the boundedness of all signals of the closed‐loop system and the prescribed tracking performance of the system. Finally, simulation results are depicted to illustrate the effectiveness of the proposed control approach.

Adaptive control for non-affine nonlinear systems with input saturation and output dead zone

This paper disposes of the problem of adaptive dynamic surface control for non-affine nonlinear systems. The full state constraints, output dead zone and input saturation are fully considered in the controlled system. Invariant sets are used for a continuous and semi-bounded condition of non-affine functions. The “complexity explosion” issue caused by backstepping procedure is avoided via the dynamic surface control method. A Nussbaum function is used to handle the unknown control coefficient derived from the output dead zone, and barrier Lyapunov functions are employed to handle full state constraints. To counteract the effects of disturbance and uncertainty, the robust compensator is constructed. In addition, it is proved that all signals in the system are bounded and all states satisfy their constraints. Finally, the simulation results show the effectiveness of the proposed method.

Improved Adaptive Fuzzy Output-Feedback Dynamic Surface Control of Nonlinear Systems With Unknown Dead-Zone Output

This article presents an adaptive fuzzy output-feedback dynamic surface control (DSC) for the nonlinear systems with dead-zone output nonlinearity. First, a novel smooth approximation of the output dead zone is given to conveniently fuse with the backstepping technique. After that a nonlinear fuzzy state observer is constructed to estimate the unmeasurable states, by employing the fuzzy logic systems for identifying the unknown compounded nonlinear functions, which releases the limitation in the existing references that the state observer needs to be linear in the presence of dead-zone output nonlinearity. Then, to reduce the effect of unknown dead-zone coefficients, an adaptive compensation mechanism is introduced by using the characteristic of hyperbolic tangent function, which can replace the widely used Nussbaum-type function-based control strategy. Furthermore, a novel DSC method based on the nonlinear filters is proposed, which not only avoids the issue of explosion of complexity inherent in the backstepping procedure, but also improves the system control performance. Under the certain assumptions, the stability of the closed-loop system is proved by use of Lyapunov function stability theory. Finally, the applicability of the proposed control method is rigorously verified by a single-link robot arm simulation example.

Adaptive tracking control of a class of nonlinear systems with unknown dead-zone output: a multi-dimensional Taylor network (MTN)-based approach

ABSTRACT In this paper, an approximation-based adaptive multi-dimensional Taylor network (MTN) control approach is proposed for a class of nonlinear systems with unknown dead-zone output. Firstly, MTNs are utilised to approximate the unknown nonlinearity of the system. Secondly, the relation of the unknown function and the output is establish. Thirdly, integrating adaptive control method and MTNs into the backstepping design process, a novel MTN-based adaptive controller is constructed by introducing a Nussbaum function. The proposed control scheme has the advantages of simple structure and small calculation. Finally, two simulation examples are given to illustrate the effectiveness of the method proposed in this paper.

Prescribed Performance Adaptive Fuzzy Containment Control for Nonlinear Multiagent Systems Using Disturbance Observer

This article focuses on the containment control problem for nonlinear multiagent systems (MASs) with unknown disturbance and prescribed performance in the presence of dead-zone output. The fuzzy-logic systems (FLSs) are used to approximate the unknown nonlinear function, and a nonlinear disturbance observer is used to estimate unknown external disturbances. Meanwhile, a new distributed containment control scheme is developed by utilizing the adaptive compensation technique without assumption of the boundary value of unknown disturbance. Furthermore, a Nussbaum function is utilized to cope with the unknown control coefficient, which is caused by the nonlinearity in the output mechanism. Moreover, a second-order tracking differentiator (TD) is introduced to avoid the repeated differentiation of the virtual controller. The outputs of the followers converge to the convex hull spanned by the multiple dynamic leaders. It is shown that all the signals are semiglobally uniformly ultimately bounded (SGUUB), and the local neighborhood containment errors can converge into the prescribed boundary. Finally, the effectiveness of the approach proposed in this article is illustrated by simulation results.