Output Feedback Tracking Problem Research Articles

Distributed Multiagent-Based Event-Driven Fault-Tolerant Control of Islanded Microgrids.

This article proposes an observer-based event-driven fault-tolerant (OBEDFT) secondary control strategy for AC microgrids (MGs) to achieve load voltage regulation. First, the input-output feedback linearization method transforms the voltage regulation issue into an output feedback tracking problem for linear multiagent systems (MASs) with nonlinear dynamics. This transformation provides the necessary preprocessing for load voltage regulation. Then, an OBEDFT secondary control protocol that considers full-state immeasurability is proposed. The actuators of distributed generators (DGs) may experience partial loss of effectiveness (PLOE) and bias faults, and these fault parameters may be heterogeneous and time-varying. The protocol introduces adaptive techniques to avoid information related to fault parameters while using event-driven mechanisms to achieve discrete measurements of neighboring DG. Additionally, the protocol uses boundary layers to construct smooth controllers that prevent the chattering effect caused by nonsmooth controllers. Finally, simulation results confirm the effectiveness of this load voltage regulation strategy.

Prescribed-time adaptive output feedback tracking control for nonlinear systems with quantized input

This study concentrates on the prescribed-time output feedback tracking problem for nonlinear systems with unknown control gains and quantized input. A modified observer with the first-order scale function handles the unmeasurable problem of states while achieving accurate observation in the prescribed time. A simple controller is constructed by a new function transformation in which the computation burden is greatly reduced. The resulting controller not only drives that tracking error converges to zero but also ensures that all signals in the closed-loop system are bounded. Finally, the effectiveness of the control scheme is demonstrated by a numerical simulation and a practical simulation.

Trajectory tracking of two-stage anaerobic digestion process: A predictive control with guaranteed performance and saturated input, based on ultra-local model

To address the robust output feedback tracking problem for total outflow rate of two-stage anaerobic digestion (AD) process with input saturation, a novel performance guaranteed ultra-local model (ULM)-based predictive control (PG-ULMPC) is proposed. First, model of the two-stage AD process is considered which presents the characteristics of the production rate of hydrogen and methane. Then, a new control strategy is developed, which relies only on input/output data and does not require any model knowledge of two-stage AD process. Based on ULM, the designed PG-ULMPC strategy consists of time-delay estimation (TDE), prescribed performance function (PPF) as well as predictive control using feedback correction (FC). The ULM is utilized to approximate the complex model of two-stage AD process in a short sliding time window and simplify controller design. The TDE is employed to estimate the lumped disturbance. The PPF guarantees that the tracking error is limited within the performance bounds whose convergence time and precision can be artificially preset. The predictive control using FC stabilizes the closed-loop system and provides saturated control input. To demonstrate the PG-ULMPC performances, numerical simulations of two-stage AD process in presence of unknown parameter disturbances with nonlinear Proportional–Integral (PI) controller, with ULMPC and with PG-ULMPC are realized by using Matlab/Simulink. The obtained results illustrate the better performance of the proposed PG-ULMPC strategy. Moreover, under the measurement noise, by using filtered derivative technique, the proposed method can still achieve satisfactory performance and has little control chattering.

Boundary output-feedback control for a moving rigid body-liquid system in the presence of shallow-water sloshing

We solve the boundary output-feedback tracking problem for a moving rigid body-liquid system described by the linearized Saint-Venant PDEs. The control input is an external force acting on the rigid body. The control law is a boundary state-feedback type augmented with a Luenberger boundary observer, leading to a dynamic feedback law. The main complexity lies in the fact that we can only measure the position and velocity of the rigid body. To get around this limitation, the observer estimates the liquid levels at the cavity walls. We utilize a control Lyapunov functional methodology (with two different functionals for the observer and the dynamic feedback law) and achieve a slosh-free asymptotic convergence to the prespecified equilibrium point (in the sense of an appropriate norm). Finally, through a simulation, we demonstrate the feasibility of the boundary output-feedback controller for the (linear and nonlinear) system.

Output-feedback tracking control of random high-order nonlinear systems

In this paper, we investigate the output-feedback tracking problem of random high-order nonlinear systems. Compared with most of the existing results where only a wiener process is considered, we consider systems with a second-order moment process. A new high-gain homogeneous domination design technique is developed to deal with the coupling terms between nonlinear functions and the second-order moment process. A new output tracking controller is designed to ensure that the mean-square of the tracking error can be made arbitrarily small while all the states of the closed-loop system are bounded in probability. Finally, a simulation example is given to illustrate the effectiveness of our control scheme.

Nonlinear-Observer-Based Design Approach for Adaptive Event-Driven Tracking of Uncertain Underactuated Underwater Vehicles

A nonlinear-observer-based design methodology is proposed for an adaptive event-driven output-feedback tracking problem with guaranteed performance of uncertain underactuated underwater vehicles (UUVs) in six-degrees-of-freedom (6-DOF). A nonlinear observer using adaptive neural networks is presented to estimate the velocity information in the presence of unknown nonlinearities in the dynamics of 6-DOF UUVs where a state transformation approach using a time-varying scaling factor is introduced. Then, an output-feedback tracker using a nonlinear error function and estimated states is recursively designed to overcome the underactuated problem of the system dynamics and to guarantee preselected control performance in three-dimensional space. It is shown that the tracking error of the nonlinear-observer-based output-feedback control system exponentially converges a small neighbourhood around the zero. Efficiency of the resulting output-feedback strategy is verified through a simulation.

Distributed Output-Feedback Tracking of Multiple Nonlinear Systems With Unmeasurable States

In this paper, we investigate the distributed output-feedback tracking problem of nonlinear multiagent systems (MASs) where nonlinear functions of agent dynamics depend on unmeasurable states. Most existing results on output-feedback control of nonlinear MASs are concerned with nonlinear agent dynamics in output canonical form in which the nonlinear functions of agent dynamics only depend on the measurable output. When the nonlinear functions of agent dynamics depend on unmeasurable states, the problem remains open due to the difficulties in constructing distributed observers. When the leader is only available to a small portion of followers in the directed communication topology, we establish relationship between the network topology and the design of the corresponding distributed reduced order observers. Furthermore, by developing a new distributed high-gain homogeneous domination design method, distributed observers, and controllers are designed to guarantee that the errors between the followers’ outputs and the leader’s output can be made arbitrarily small while keeping all the states of closed-loop system bounded. Finally, a numerical example is employed to illustrate the effectiveness of the proposed control scheme.

Optimal output-feedback tracking of SISO stochastic nonlinear systems using multi-dimensional Taylor network

The randomness and nonlinearity of stochastic nonlinear systems increase computational complexity and impede their tracking performance. However, randomness and nonlinearity are inevitable in practical applications, and the existing methods can hardly achieve the desirable control effect, especially in real-time control. For this end, a new network control strategy based on multi-dimensional Taylor network (MTN), whose design depends only on the system output, is put forward to solve the optimal output-feedback tracking problem of the SISO stochastic nonlinear systems. The network structure of the MTN is given first, and its approximation properties are proven. Based on the quadratic cost function design learning algorithm, the tracking error is minimized to update the controller parameters, and the desired tracking performance is obtained. Using the Lyapunov stability theorem, it is proved that the corresponding closed-loop system is bounded in the sense of probability and it can be ensured that the output tracking error converges to a small residual set around the origin in the sense of probability. An example is provided to illustrate the effectiveness of the proposed design approach. Comparative simulation study reveals that the proposed solution promises desirable real-time dynamic performance.

Leader-following rendezvous for uncertain Euler–Lagrange multi-agent systems by output feedback

In this paper, we investigate the problem of output feedback tracking for a class of Euler–Lagrange multi-agent systems with unmeasurable velocity and input disturbances. By proposing a novel dynamic velocity observer, an adaptive output feedback consensus algorithm is proposed such that the tracking errors of all agents can converge to an arbitrarily small neighborhood of zero by tuning the design parameters. A numerical example is presented to illustrate the effectiveness of the controller.

Adaptive-observer-based output-constrained tracking of a class of arbitrarily switched uncertain non-affine nonlinear systems

This paper addresses an adaptive output-feedback tracking problem of arbitrarily switched pure-feedback nonlinear systems with time-varying output constraints and unknown control directions. In this work, the tracking problem of switched non-affine nonlinear systems with output constraints is transformed into the stabilization problem of switched unconstrained affine systems. The main contribution of this paper is to present a universal formula for constructing an adaptive state-observer-based tracking controller with only two adaptive parameters by using the common Lyapunov function method. These adaptive parameters in the proposed control scheme are derived using the function approximation technique and a priori knowledge of the signs of control gain functions is not required. The theoretical analysis is presented for the Lyapunov stability and the constraint satisfaction of the resulting closed-loop system in the presence of arbitrary switchings.

Global adaptive output feedback tracking for a class of multivariable nonlinear systems

In this paper, global adaptive output feedback tracking problem is considered for a class of multivariable nonlinear systems with unknown parameters and nonlinear terms linear in unmeasured states. Based on a modified high-gain observer and symmetric positive diagonal unity upper triangular factorisation of high-frequency gain matrix, an adaptive backstepping design is proposed, which is able to guarantee global stability of the closed-loop system and circumvent the over-parameterisation problem raised by using conventional backstepping method. Moreover, with the introduction of an error modification and an initialisation technique, it is proved that the tracking performance can be achieved.

Additive‐state‐decomposition‐based tracking control framework for a class of nonminimum phase systems with measurable nonlinearities and unknown disturbances

SUMMARYThis paper aims to propose an additive‐state‐decomposition‐based tracking control framework, based on which the output feedback tracking problem is solved for a class of nonminimum phase systems with measurable nonlinearities and unknown disturbances. This framework is to ‘additively’ decompose the output feedback tracking problem into two more tractable problems, namely an output feedback tracking problem for a linear time invariant system and a state feedback stabilization problem for a nonlinear system. Then, one can design a controller for each problem respectively using existing methods, and these two designed controllers are combined together to achieve the original control goal. The main contribution of the paper lies on the introduction of an additive state decomposition scheme and its implementation to mitigate the design difficulty of the output feedback tracking control problem for nonminimum phase nonlinear systems. To demonstrate the effectiveness, an illustrative example is given. Copyright © 2013 John Wiley & Sons, Ltd.

Adaptive Output Feedback Control of Multivariable Nonlinear Systems Based on Hurwitz Assumption

In this paper, global adaptive output feedback tracking problem is considered for a class of multivariable nonlinear systems with unknown parameters and nonlinear terms linear in unmeasured states. Based on a modified high-gain observer and Hurwitz assumption of high frequency gain matrix, an adaptive backstepping design is proposed, which is able to guarantee global stability of the closed-loop system and circumvent the over-parameterization problem raised by using conventional back stepping method.

Saturated output feedback tracking control for robot manipulators via fuzzy self-tuning

This paper concerns the problem of output feedback tracking (OFT) control with bounded torque inputs of robot manipulators, and proposes a novel saturated OFT controller based on fuzzy self-tuning proportional and derivative (PD) gains. First, aiming to accomplish the whole closed-loop control with only position measurements, a linear filter is involved to generate a pseudo velocity error signal. Second, different from previous strategies, the arctangent function with error-gain is applied to ensure the boundedness of the torque control input, and an explicit system stability proof is made by using the theory of singularly perturbed systems. Moreover, a fuzzy self-tuning PD regulator, which guarantees the continuous stability of the overall closed-loop system, is added to obtain an adaptive performance in tackling the disturbances during tracking control. Simulation showed that the proposed controller gains more satisfactory tracking results than the others, with a better dynamic response performance and stronger anti-disturbance capability.

TRACKING CONTROL OF A INDUCTION MOTOR: AN OUTPUT FEEDBACK APPROACH

In this work the output feedback tracking problem for the angular position of a induction motor driving a mechanical load is addressed. It is assumed that the two rotor flux variables are not measured whilst the two stator current variables and also the angular position and velocity are available for feedback. The employed approach consists of the asymptotic reconstruction of a full state feedback stabilizing control law, without using a Lyapunov function and by applying a separation principle. Firstly, a Backstepping tracking controller is considered, then, a dynamic output feedback is synthesized for the two voltage control inputs, which asymptotically recovers the Backstepping control law.

Set-point regulation of an anaerobic digestion process with bounded output feedback

This work addresses the robust output feedback tracking problem for a biological wastewater treatment process in the presence of input constraints and limited knowledge on the process parameters. The considered process is an anaerobic up-flow fixed bed biological reactor within a semi-industrial scale pilot plant for the treatment of industrial wine distillery wastewater. A mathematical model describing the dynamic behavior of the biological process is considered and nonlinear control techniques are used to design a bounded output feedback control law. Local asymptotic stability and local set-point tracking of the resulting closed-loop system is proved when the dilution rate is selected as the manipulated variable and the output methane gas flow-rate as the measured and controlled variable. Finally, the implementation of the nonlinear output feedback control law is described and experimental results are reported. It is observed that the proposed controller yields set-point regulation, rejects disturbances and preserves stability despite uncertainty on the kinetic parameters.