Output Feedback Control Approach Research Articles

Fuzzy reduced-order observer-based adaptive tracking control for a class of switched non-affine systems

This paper investigates an adaptive fuzzy tracking control problem under arbitrary switching for a class of switched non-affine systems with completely unknown nonlinear functions and unmeasurable states. Combining with dynamic surface control (DSC) method and fuzzy approximation technique, an adaptive output-feedback common control approach is presented based on a new fuzzy reduced-order observer which is independent of any switching signal. The given design method does not rely on the boundness assumption about the control gain functions raised by the mean value theorem for non-affine systems, which contributes to the less conservation of the common controller. Meanwhile, the algebraic loop problem caused by the nonstrict-feedback structure and the repeated approximation problem in existing results are also circumvented in the given common control design process. Based on Lyapunov stability theory, the designed common controller can guarantee all the signals in the resulting closed-loop switched systems are uniformly bounded and the tracking error can converge to a small neighborhood of the origin. Two examples are provided to verify the feasibility and practicability of the proposed method.

Observer-based adaptive neural tracking control for a class of stochastic nonlinear systems

In this paper, an observer-based adaptive neural tracking control approach is proposed for a class of stochastic nonlinear systems with immeasurable states. The radial basis function neural networks (RBFNNs) are used to approximate the unknown nonlinear functions, and a linear reduced-order state observer is designed for estimating the unmeasured states. Based on the designed the state observer, an adaptive neural output feedback control approach is developed via backstepping control design. It is shown that the proposed controller ensures that all signals of the closed-loop system remain bounded in probability, and the tracking error converges to an arbitrarily small neighbourhood around the origin in the sense of mean quartic value. Finally, two examples are given to illustrate the effectiveness of the proposed design approach.

Event-triggered distributed dynamic output-feedback dissipative control of multi-weighted and multi-delayed large-scale systems

This paper considers multi-weighted and multi-delayed large-scale interconnected systems, which have multiple coupling links between neighboring subsystems and all links have different coupling weights and delays. It describes an event-triggered distributed dynamic output-feedback control approach for dissipative stabilization of the concerned systems under quantization, packet dropouts and stochastic deception attacks. An event-triggered distributed dynamic output feedback controller (DOFC) is constructed and sufficient conditions are calculated for the exponential mean square stability along with a strict ( Q, S, R)-dissipative performance of the considered large-scale systems. The control gains for the subsystem controllers are determined by solving a nonlinear minimization problem using the cone complementarity linearization (CCL) algorithm. Finally, the obtained results are verified through a numerical example consisting of a continuous stirred tank reactor (CSTR) system.

Universal adaptive control for uncertain nonlinear systems via output feedback

In this paper, the universal adaptive control problem for a class of uncertain nonlinear systems is solved by the output feedback control approach. Firstly, a homogenous high-gain observer is proposed to estimate the system states based on the homogenous theory. Then, by using adding one power integrator method, a homogeneous controller is designed. It can be shown that all signals of the whole system are bounded and at the same time the system states globally asymptotically converge to the origin. In the end, we extend the proposed method to a class of upper-triangular nonlinear systems. Two examples are provided to illustrate the effectiveness of the proposed method.

Distributed output-feedback model predictive control for multi-agent consensus

We propose a distributed output-feedback model predictive control approach for achieving consensus among multiple agents. Each agent computes a distributed control action based on an output-feedback measurement of a local neighborhood tracking error and communicates information only to its neighbors, according to a communication network modeled as a directed graph. Each agent computes its distributed control action by solving a local min–max optimization problem that simultaneously computes a local state estimate and control input under worst-case assumptions on unmeasured input disturbances and measurement noise. Under easily verified controllability and observability assumptions, this distributed output-feedback model predictive control approach provides an upper bound on the group consensus error, thereby ensuring practical consensus in the presence of unmeasured disturbances and noise. A numerical example with four agents connected in a directed graph is given to illustrate the results.

Robust nonlinear H∞ output-feedback control for flexible spacecraft attitude manoeuvring

This paper presents a robust nonlinear H∞ output-feedback control approach for attitude manoeuvring of flexible spacecraft with external disturbances, inertia matrix perturbation and input constraints. By applying Lyapunov stability theory and using the generalized S-procedure and sum of squares (SOS) techniques, the robust H∞ output-feedback attitude control problem is converted into a convex optimization problem with SOS constraints when the flexible spacecraft is modelled as a polynomial state-space equation with polytope uncertainties. As a result, it overcomes the difficulty in constructing Lyapunov function and implementing numerical computation caused by the non-convexity of output-feedback H∞ control design for nonlinear systems. Moreover, it enables the state-observer and the controller to be designed independently and hence the complexity of the control algorithm is reduced remarkably. A numerical example illustrates the effectiveness and feasibility of the proposed approach.

Mitigation of Torsional Vibrations in Drilling Systems: A Robust Control Approach

Stick-slip vibrations decrease the performance, reliability, and fail safety of drilling systems. The aim of this paper is to design a robust output-feedback control approach to eliminate torsional stick-slip vibrations in drilling systems. Current industrial controllers regularly fail to eliminate stick-slip vibrations, especially when multiple torsional flexibility modes play a role in the onset of stick-slip vibrations. As a basis for controller synthesis, a multimodal model of the torsional dynamics for a real drill-string system is employed. The proposed controller design strategy is based on skewed- $\mu$ DK iteration and aims at optimizing the robustness with respect to uncertainty in the nonlinear bit–rock interaction. Moreover, a closed-loop stability analysis for the nonlinear drill-string model is provided. This controller design strategy offers several benefits compared with existing controllers. First, only surface measurements are employed, therewith avoiding the need for down-hole measurements. Second, multimodal drill-string dynamics are effectively dealt with in ways inaccessible to state-of-practice controllers. Third, robustness with respect to uncertainties in the bit–rock interaction is explicitly provided and closed-loop performance specifications are included in the controller design. Case study results confirm that stick-slip vibrations are indeed eliminated in realistic drilling scenarios using the designed controller in which state-of-practice controllers fail to achieve this.

Output-Feedback Control for Cooperative Diving of Saucer-Type Underwater Gliders Based on a Fuzzy Observer and Event-Triggered Communication

This paper presents an output-feedback control method for cooperative diving of multiple homogenous under-actuated saucer-type autonomous underwater gliders (AUGs) subjected to unmeasured velocities, model uncertainties, as well as unknown environmental disturbances. A robust cooperative diving controller is constructed by using a modular back-stepping design method. First, a fuzzy observer combined with a low-frequency learning scheme is presented to identify the unknown AUG dynamics as well as to estimate the unmeasured velocities. Second, a commanded guidance law is designed based on the observed velocities and a vertical line-of-sight guidance scheme at the kinematic level, and fuzzy dynamic control law is developed at the kinetic level. Finally, a non-periodic path update law based on an event-triggered mechanism is designed to reduce the communication frequency. By using the cascade theory, the stability of the closed-loop system is analyzed. The simulation results are provided to show the effectiveness of the proposed output-feedback control approach for saucer-type AUGs.

Adaptive output feedback formation tracking for a class of multiagent systems with quantized input signals

A novel adaptive output feedback control approach is presented for formation tracking of a multiagent system with uncertainties and quantized input signals. The agents are described by nonlinear dynamics models with unknown parameters and immeasurable states. A high-gain dynamic state observer is established to estimate the immeasurable states. With a proper design parameter choice, an adaptive output feedback control method is developed employing a hysteretic quantizer and the designed dynamic state observer. Stability analysis shows that the control strategy can guarantee that the agents can maintain the formation shape while tracking the reference trajectory. In addition, all the signals in the closed-loop system are bounded. The effectiveness of the control strategy is validated by simulation.

Simultaneous Robust, Decoupled Output Feedback Control for Multivariate Industrial Systems

This paper proposes a simultaneous robust decoupled output feedback control approach for multivariate industrial processes with parameter uncertainties. Based on the desired control performance indicators, the expected transfer function of the closed-loop multivariate system is configured as a nonsingular diagonal matrix. Using coprime factorization theory, the weighting functions are selected and the parameterization matrix and controller are designed and calculated to achieve simultaneous robust decoupled control that takes into consideration parameter uncertainties. The performance and effectiveness of the proposed approach is demonstrated using a case study based on the crown-thickness control system of the hot strip mill processes.

H∞ consensus for linear heterogeneous multi-agent systems with state and output feedback control

In this paper, the problem of H∞ consensus for linear heterogeneous multi-agent systems (HMASs) with state and output feedback control is investigated. By using the linear matrix inequality (LMI) theory, some basic properties of symmetric positive definite matrix and a series of analysis, the novel consensus conditions of using state and output feedback control and the corresponding algorithm are provided. The key idea of this paper is to convert one kind of nonlinear matrix inequality condition into a series of novel conditions that can be solved by using LMI toolbox and the most important characteristic of this paper is that the necessary and sufficient conditions of H∞ consensus are provided while only sufficient conditions were given in some existing results. First, an equivalent condition for a special kind of nonlinear matrix inequality is given. Second, by using this equivalent condition, the necessary and sufficient conditions of H∞ consensus for linear HMASs under state and output feedback control approaches are provided, respectively. Third, according to iterative algorithm and the property of trace of matrix, a feasible algorithm for calculating the controllers is proposed. Fourth, the obtained results are extended to solve the H∞ control problem for general linear system with some additional conditions. At last, two numerical examples are given to support our results.

Vehicle lateral motion regulation under unreliable communication links based on robust H∞ output-feedback control schema

Abstract This paper presents a robust control schema for vehicle lateral motion regulation under unreliable communication links via controller area network (CAN). The communication links between the system plant and the controller are assumed to be imperfect and therefore the data packet dropouts occur frequently. The paper takes the form of parallel distributed compensation and treats the dropouts as random binary numbers that form Bernoulli distribution. Both of the tire cornering stiffness uncertainty and external disturbances are considered to enhance the robustness of the controller. In addition, a robust H ∞ static output-feedback control approach is proposed to realize the lateral motion control with relative low cost sensors. The stochastic stability of the closed-loop system and conservation of the guaranteed H ∞ performance are investigated. Simulation results based on CarSim platform using a high-fidelity and full-car model verify the effectiveness of the proposed control approach.

Adaptive decentralized fuzzy output feedback tracking control for a class of nonlinear large-scale systems with input delays

In this paper, the decentralized tracking control problem of nonlinear large-scale systems with immeasurable states and unknown nonlinearities in each subsystem and their interconnections subject to input delays is proposed. Based on the universal approximation properties of fuzzy systems, a fuzzy observer is designed to estimate the inaccessible states of the system. Subsequently, by defining the appropriate change of coordinates and employing the backstepping technique, an adaptive fuzzy backstepping output feedback control scheme is proposed for nonlinear interconnected systems subject to input delays. Owing to the problem of so-called explosion of complexity that inherently arises in the design procedures of the traditional backstepping technique, an adaptive fuzzy dynamic surface output feedback control approach is also presented for this class of large-scale systems. Moreover, it is shown that the two proposed adaptive decentralized fuzzy observer-based control approaches ensure all the signals of the closed loop system uniformly ultimately bounded and the norms of the tracking errors as well as the norms of the observation errors can converge to small desired values by proper selection of the design parameters. Finally, the theoretic achievements are carried out on the chemical reactor recycle system to illustrate and compare the effectiveness of the two proposed approaches.

Decentralized Adaptive Neural Output-Feedback DSC for Switched Large-Scale Nonlinear Systems

In this paper, for a class of switched large-scale uncertain nonlinear systems with unknown control coefficients and unmeasurable states, a switched-dynamic-surface-based decentralized adaptive neural output-feedback control approach is developed. The approach proposed extends the classical dynamic surface control (DSC) technique for nonswitched version to switched version by designing switched first-order filters, which overcomes the problem of multiple "explosion of complexity." Also, a dual common coordinates transformation of all subsystems is exploited to avoid individual coordinate transformations for subsystems that are required when applying the backstepping recursive design scheme. Nussbaum-type functions are utilized to handle the unknown control coefficients, and a switched neural network observer is constructed to estimate the unmeasurable states. Combining with the average dwell time method and backstepping and the DSC technique, decentralized adaptive neural controllers of subsystems are explicitly designed. It is proved that the approach provided can guarantee the semiglobal uniformly ultimately boundedness for all the signals in the closed-loop system under a class of switching signals with average dwell time, and the tracking errors to a small neighborhood of the origin. A two inverted pendulums system is provided to demonstrate the effectiveness of the method proposed.

Amplitude-Saturated Nonlinear Output Feedback Antiswing Control for Underactuated Cranes With Double-Pendulum Cargo Dynamics

When modeling cranes, the hook and the suspended cargo are usually regarded roughly as one mass point for simplicity, i.e., the cargo swing is modeled as that of a single pendulum. However, in practice, when the hook mass is nonnegligible or the cargo has a large size, the crane always exhibits double-pendulum swing dynamics, which is much more complicated and makes most existing control methods unapplicable. In addition, all existing closed-loop controllers for (double-pendulum) cranes require full state feedback, while velocities are unavailable in most cases. Moreover, they need to linearize the nonlinear crane model and cannot respect the actuator's practical saturation constraint, which may probably lead to actuator saturation and badly degrade the control performance (even unstable). In response to these practical issues, we suggest a novel amplitude-saturated output feedback (OFB) control approach for underactuated crane systems exhibiting double-pendulum effects. We provide explicit Lyapunov-based analysis to rigorously prove that the equilibrium point of the closed-loop system is almost globally asymptotically stable, without any approximation to the original nonlinear dynamics. As far as we know, this paper presents the first closed-loop control method that can achieve control for an underactuated double-pendulum crane with merely OFB and theoretically-guaranteed saturated control efforts. Hardware experimental results demonstrate the superior performance of the proposed approach over existing methods and its strong robustness as well.

Observer-based adaptive sliding mode backstepping output-feedback DSC for spin-stabilized canard-controlled projectiles

This article presents a complete nonlinear controller design for a class of spin-stabilized canard-controlled projectiles. Uniformly ultimate boundedness and tracking are achieved, exploiting a heavily coupled, bounded uncertain and highly nonlinear model of longitudinal and lateral dynamics. In order to estimate unmeasurable states, an observer is proposed for an augmented multiple-input-multiple-output (MIMO) nonlinear system with an adaptive sliding mode term against the disturbances. Under the frame of a backstepping design, an adaptive sliding mode output-feedback dynamic surface control (DSC) approach is derived recursively by virtue of the estimated states. The DSC technique is adopted to overcome the problem of “explosion of complexity” and relieve the stress of the guidance loop. It is proven that all signals of the MIMO closed-loop system, including the observer and controller, are uniformly ultimately bounded, and the tracking errors converge to an arbitrarily small neighborhood of the origin. Simulation results for the observer and controller are provided to illustrate the feasibility and effectiveness of the proposed approach.

Bipartite consensus of multi-agent systems over signed graphs: State feedback and output feedback control approaches

Summary This paper studies bipartite consensus problems for continuous-time multi-agent system over signed directed graphs. We consider general linear agents and design both state feedback and dynamic output feedback control laws for the agents to achieve bipartite consensus. Via establishing an equivalence between bipartite consensus problems and the conventional consensus problems under both state feedback and output feedback control approaches, we make direct application of existing state feedback and output feedback consensus algorithms to solve bipartite consensus problems. Moreover, we propose a systematical approach to design bipartite consensus control laws. Copyright © 2016 John Wiley & Sons, Ltd.

Dynamic surface error constrained adaptive fuzzy output feedback control for switched nonlinear systems with unknown dead zone

This paper investigates the adaptive fuzzy output-feedback dynamic surface control (DSC) approach with prescribed performance for a class of uncertain switched nonlinear systems with unknown dead-zone. In this research, fuzzy logic systems are used to identify the unknown nonlinear functions, a fuzzy switched state observer is established to observe the unmeasured states. Based on DSC backstepping control design technique and incorporated by the predefined performance theory and the average dwell time method, a new adaptive fuzzy output-feedback control method is developed. It is proved that the proposed control approach can ensure that all the signals of the resulting closed-loop system are bounded, and the tracking errors are within the prescribed performance bounds for all times. Simulation studies illustrate the effectiveness of the proposed approach.

Simplified filtering‐based adaptive fuzzy dynamic surface control approach for non‐linear strict‐feedback systems

In this study, the problem of filtering-based adaptive fuzzy control is investigated for non-linear strict-feedback systems with unmeasured states. An input-driven filter is designed for estimating the unmeasured states. By exploiting the bound of system output, an upper bound of filtering error is obtained and it is further optimised by solving a set of linear matrix inequalities. Then under the framework of backstepping design technique incorporated by dynamic surface control approach, and by using the obtained upper bound, a novel adaptive fuzzy dynamic output feedback control approach with one adaptive parameter is developed. It is shown that all the signals of the closed-loop system are bounded, while the tracking error converges to a small neighbourhood of the origin. The main advantages of the proposed approach are that choice of design parameters is independent of the order of system such that closed-loop system can achieve a better tracking performance, and compared with the existing results, the controller has a much simpler structure embodied in its form, adaptive parameter and membership functions of fuzzy logic systems. Finally, two examples are given for showing the effectiveness of the proposed approach.

Barrier Lyapunov function-based adaptive output feedback failure compensation for a class of non-linear systems with unknown dead-zone non-linearity

In this paper, an adaptive robust output feedback control approach is proposed for a class of uncertain non-linear systems with unknown input dead-zone non-linearity, unknown failures and unknown bounded disturbances. By constructing the dead-zone inverse and applying the backstepping recursive design technique, a robust adaptive backstepping controller is proposed, in which adaptive control law is synthesized to handle parametric uncertainties and a novel robust control law to attenuate disturbances. The robust output feedback control law is developed by integrating a switching function σ algorithm at each step of the backstepping design procedure. In addition, K-filters are designed to estimate the unmeasured states and neural networks are employed to approximate the unknown non-linear functions. By ensuring boundedness of the barrier Lyapunov function, the major feature of the proposed controller is that it can theoretically guarantee asymptotic output tracking performance, in spite of the presence of unknown input dead-zone non-linearity, various actuator failures and unknown bounded disturbances via Lyapunov stability analysis. The effectiveness of the proposed approach is illustrated by the simulation examples.