Sampled-data Output Feedback Control Research Articles

Predictor-based sampled-data output-feedback control for feedforward nonlinear time-delay systems

Predictor-based sampled-data output-feedback control for feedforward nonlinear time-delay systems

Global tracking stabilisation via sampled-data output feedback for nonlinear systems with unknown measurement sensitivity

In engineering practice, sensors commonly have limited sensitivity errors, which are allowed in the design process. This paper uses a sampled-data output feedback control method so that the problem of global tracking stabilisation of a class of nonlinear systems with unknown measurement sensitivity can be solved. A sampled-data output feedback control law and a sampled-data observer are constructed with the help of output feedback domination method. By choosing the domination gain and sampling period in place, the resultant control law can globally stabilise the system even under the influence of unknown measurement sensitivity.

Symplectic discrete-time Krasovskii passivity-based control for output consensus*

In this paper, we design a sampled-data distributed output feedback controller to achieve output consensus for linear continuous-time port-Hamiltonian systems in presence of unknown disturbances. The key idea is borrowed from Krasovskii passivity-based output consensus control for continuous-time dynamics. To conceptualise this rationale to sampled control systems, we deal with a discrete-time system arising from a symplectic discretization of a continuous-time linear port-Hamiltonian system, such as the implicit midpoint method. As a preliminary step, we introduce the concept of Krasovskii passivity for discrete-time systems and further show that a discretized linear port-Hamiltonian system is Krasovskii passive in the discrete-time sense. Then, based on the discrete-time version of Krasovskii passivity, we develop a sampled-data output feedback controller to achieve output consensus. The proposed sampled-data controller can be understood as a symplectic discretization of the continuous-time output consensus controller. Finally, we illustrate the effectiveness of the main result by achieving current sharing in a DC power network.

Sample-data output-feedback parameter variable control of glucose for type 1 diabetes mellitus patients

This paper presents a new approach for nonlinear sampled-data output-feedback control of blood glucose concentration (BGC) in patients with type 1 diabetes mellitus (T1DM). The Bergman minimal model (BMM) describing the glucose-insulin regulatory system is framed as a linear parameter-varying (LPV) model that the sampled glucose measurement with the quantized insulin infusion is considered as input delay. The proposed method utilizes the input-delay approach based on a parameter-dependent Lyapunov–Krasovskii functional (LKF) and provides the parameterized matrix inequality conditions for the design of the controller. Using the slack variables, the design conditions are converted to a new form that can achieve feasible solutions by solving a limited number of bilinear matrix inequality. The proposed architecture guarantees stability in a compact set of states for all the trajectories with the parametric uncertainty to address the issue of inter-patient variability. Simulation results confirm that the proposed sampled-data static output-feedback LPV controller with only glucose measurement regulates the BGC despite the parametric uncertainty and meal disturbances in T1DM patients.

On the Finite-Time Stabilization for a Class of Interconnected Switched Nonlinear Systems via Sampled-Data Output Feedback Control

This article investigates the problem of finite-time stabilization for a class of interconnected switched nonlinear systems (ISNS) via sampled-data output feedback control. First, for each subsystem, the state observers and sampled-data output feedback controllers are constructed. Second, with a selected design parameter and a suitable sampling period, the finite-time stabilization is proved based on the homogeneous theory and related inequalities for the discussed ISNS. Finally, the effectiveness of the presented control method is verified with a numerical example and a practical example.

Sampled-Data Semi-Global Robust Output Regulation for a Class of Nonlinear Systems

This paper investigates the semi-global robust output regulation problem for a class of uncertain nonlinear systems via a sampled-data output feedback control law. What makes the results interesting is that the nonlinearities of the proposed system do not have to satisfy linear growth condition and the uncertain parameters of our system are allowed to belong to some arbitrarily large prescribed compact subset. Two cases are considered. The first case is that the exogenous signal is constant. The second case is that the exogenous signal is time-varying and bounded. For the first case, the authors solve the problem exactly in the sense that the tracking error approaches zero asymptotically. For the second case, the authors solve the problem practically in the sense that the steady-state tracking error can be made arbitrarily small. Finally, an example is given to illustrate the effectiveness of our approach.

Nonlinear lateral motion stability control method for electric vehicle based on the combination of dual extended state observer and domination approach via sampled-data output feedback

This paper studies lateral motion stability control method for an electric vehicle (EV) with uncertain disturbances. Aiming to suppress the influence of uncertain disturbances, a novel method based on the combination of dual extended state observer (dESO) and domination approach is proposed. The proposed method enables the designed controller just to adopt a smaller scaling gain than the classic domination method, which is more practical for the controller in engineering. Firstly, a dESO is proposed to estimate the uncertain disturbances of the system. Also, a sampled-data output feedback domination approach is designed to dominate the disturbances by using a scaling gain. Then, the sampled-data output feedback control law is constructed based on the dESO and domination approach called dESOD. Furthermore, the stability analysis is presented to show that the proposed sampled-data controller can guarantee the closed-loop system globally asymptotically stable. Finally, the simulations and experiment results show the effectiveness of the proposed method.

Networked sampled-data control of distributed parameter systems via distributed sensor networks

This paper studies the problem of the distributed networked sampled-data control for a class of distributed parameter systems with spatially-dependent diffusion term. In view of limited fixed sampling spatial points, the distributed sensor network is proposed to obtain the sampled-data measurements of the state which can efficaciously circumvent blind sampling area or sampling error. A distributed sampled data output feedback controller based on the distributed sensor network is designed to ensure the stabilization of the distributed parameter systems with the time delays induced by the communication network. Based on Lyapunov method, linear matrix inequality technique and time-delay approach, the global exponential stability criteria are obtained for the closed-loop distributed parameter systems under three different boundary conditions, respectively. Finally, the numerical simulation proves the effectiveness of the controller, and numerical comparison shows that the proposed control method is less conservative.

LPV Delay-Dependent Sampled-Data Output-Feedback Control of Fueling in Spark Ignition Engines

We propose a delay-dependent sampled-data output-feedback LPV control technique to address the air-fuel ratio (AFR) regulation problem in spark ignition (SI) engines. AFR control and advanced fueling strategies are essential for maximizing fuel economy while minimizing harmful exhaust emissions. The fuel path of the SI engine, as well as the three-way catalyst (TWC) simplified dynamics, have been captured by a continuous-time linear parameter-varying (LPV) system with varying time delay, where the system dynamics rely on the engine speed, defined as the system's scheduling parameter. The interconnection of the continuous-time plant and a digital controller through analog-to-digital and digital-to-analog converter devices forms a hybrid closed-loop configuration. Therefore, in order to benefit from continuous-time control synthesis tools, the input-delay method has been employed to transform the hybrid closed-loop system into the continuous-time domain with system inherent time delay and an additional delay imposed by the mapping approach. The designed sampled-data gain scheduled output-feedback controller seeks to establish the closed-loop asymptotic stability and a prescribed level of performance for the LPV system with an arbitrarily varying time delay and varying sampling time, where the synthesis results are provided in a convex linear matrix inequality (LMI) constraint setting. Finally, several closed-loop simulation scenarios are conducted, and comparisons are provided to demonstrate the proposed methodology's performance in achieving precise reference AFR tracking and disturbance attenuation.

Exponential stabilisation for nonlinear PDE systems via sampled-data static output feedback control

ABSTRACT This paper focuses on the problem of exponential stabilisation for nonlinear partial differential equation (PDE) systems by designing sampled-data static output feedback controller. First, the nonlinear PDE systems are reconstructed by Takagi-Sugeno (T-S) fuzzy model. Subsequently, the closed-loop systems are obtained by introducing sampled-data static output feedback fuzzy controller. Then, stability conditions of the closed-loop system are obtained based on Lyapunov function approach, which can guarantee that the considered systems are exponential stabilisation and satisfy dissipative performance. Finally, the effectiveness of the developed method is demonstrated by a simulation study.

Sampled-data output feedback control for a class of high-order switched nonlinear systems

In this paper, we design new sampled-data output feedback controllers to globally stabilise a class of high-order switched nonlinear systems in finite time under arbitrary switching. The switching signals exist in both the system powers and the nonlinear terms. First, we adopt the common Lyapunov function method to design a continuous-time output feedback controller. Then, the emulation method is used to construct the sampled-data output feedback controllers with a sampling period. Based on the homogeneity theory, Hölder inequality and Gronwall–Bellman inequality, the relationships between the sampling states (controllers) and the current states (controllers) are revealed. In the meantime, we obtain the sampling period for the sampled-data output feedback controllers to guarantee the global stabilisation of the closed-loop systems in finite time. Finally, a numerical example and a practical example are given to prove the efficiency of the proposed control method.

Global Decentralized Control of $p$-Normal Large-Scale Nonlinear Systems Based on Sampled-Data Output Feedback

This article considers the global decentralized sampled-data output feedback control problem for large-scale nonlinear systems in the p-normal form. To estimate unavailable states, a sampled-data observer is designed. Under suitable scaling gains and an allowable sampling period, a decentralized sampled-data control law is presented to ensure the globally asymptotically stability of the corresponding closed-loop system. Finally, simulation examples are provided to verify the validity of the designed strategy.

Filtered adaptive constrained sampled-data output feedback control for uncertain multivariable nonlinear systems

This paper synthesises a constrained sampled-data output feedback controller for uncertain multivariable nonlinear systems in the presence of various constraints, using the filtered adaptive control architecture. Nonlinear uncertainties are estimated online by solving the error dynamics between the output predictor and the real system with the neglection of unknowns, the yielded adaptive parameters are piecewise constant. In the proposed control scheme, the nonlinear uncertainties are compensated for within the bandwidth of low-pass filters, while the trade-off between tracking and constraints violation avoidance is formulated as a numerical constrained optimisation problem which is solved periodically. Priority is given to constraints violation avoidance at the cost of deteriorated tracking performance. System transformations are performed to find the sufficient conditions that can guarantee the stability of the closed-loop system with the sampled-data controller, where both the states and control inputs are held constant over the sampling period.

A New Sampled-Data Output-Feedback Controller Design of Nonlinear Systems via Fuzzy Affine Models.

This article focuses on the sampled-data output-feedback control problem for nonlinear systems represented by Takagi-Sugeno fuzzy affine models. An input delay approach is adopted to describe the sample-and-hold behavior of the measurement output. Via augmenting the system states with the control input, the resulting closed-loop system is converted into a singular system first. Based on the piecewise quadratic Lyapunov-Krasovskii functionals, some novel results on the sampled-data piecewise affine output-feedback controller design are attained by employing some convexification techniques. The simulation studies are presented to illustrate the effectiveness of the proposed scheme.

Sampled-data stabilization of a class of nonlinear differential algebraic systems via partial-state and output feedback

In this paper, the problem of sampled-data stabilization for a class of nonlinear differential-algebraic systems is considered by using partial-state and output feedback. First, based on the output feedback domination approach, a systematic design procedure for sampled-data output feedback controller is proposed without using the states of the algebraic subsystem. It is shown that the proposed sampled-data controller can ensure the whole closed-loop nonlinear differential-algebraic system is asymptotically stable by choosing appropriate scaling gains and sampling period. In addition, due to the domination nature of the proposed method, the obtained results can be extended to more general class of nonlinear differential-algebraic systems easily. Finally, simulation examples are provided to illustrate the effectiveness of the proposed control method.

Sampled-data output feedback controllers for nonlinear systems with time-varying measurement and control delays

Abstract In this paper, we propose sampled-data output feedback controllers for nonlinear systems with time-varying measurement and input delays. A state prediction is generated by chains of saturated high-gain observers with switching error-correction terms and the state prediction is used to stabilize the system with saturated controls. The observers reconstruct the unmeasurable states at different delayed time-instants, which partition the maximal variation interval of the time-varying delays. The density of these delayed time instant depend both on the magnitude of the delays and the growth rate of the nonlinearities. Our sampled-data feedback controllers are obtained as zero-order discretizations of continuous time controllers.

Networked Output Feedback Control for a Class of Uncertain Nonlinear Time-Delay Systems

This paper studies the global stabilization problem for a class of uncertain time-delay nonlinear systems by designing a sampled-data output feedback controller via network. Under a lower triangular linear growth condition, when only the output is measurable, a sampled-data output feedback network controller, whose observer and control law are both linear, is constructed to solve the stabilization problem. Using a feedback domination design approach which substantially differs from the separation principle, we explicitly construct a Lyapunov–Krasovskill functional to prove the global asymptotic stability with the help of inductive proof method. The control law is discrete-time and linear, hence simulation examples be easily implemented with computers to show the effectiveness of our proposed method.

Global sampled-data output feedback stabilization for a class of switched stochastic nonlinear systems with quantized input and unknown output gain

This paper aims at addressing the sampled-data output feedback control problem for a class of uncertain switched stochastic nonlinear systems, whose control input is quantized by a logarithmic quantizer and the output gain cannot be precisely known. We design a compensator with the quantized information. With the help of the feedback domination approach and the backstepping design method, a sampled-data output feedback controller is constructed with appropriate design parameters and a maximum sampling period to guarantee the global exponential stability in mean square of the closed-loop system under arbitrary switching. Finally, a numerical example is given to illustrate the effectiveness of the proposed scheme.

Disturbance-observer-based sampled-data adaptive output feedback control for a class of uncertain nonlinear systems

ABSTRACTIn this paper, a sampled-data adaptive output feedback controller is proposed for a class of uncertain nonlinear systems with unmeasured states, unknown dynamics and unknown time-varying external disturbances. To approximate uncertain nonlinear functions, radial basis function neural networks (RBFNNs) are employed. The state observer and the disturbance observer (DO) are constructed to estimate the unmeasured state and the external disturbance, respectively. Then, the sampled-data adaptive output feedback controller and adaptive laws are designed by using the backstepping design technique. The allowable sampling period T is derived to guarantee that all states of the resulting closed-loop system are semi-globally uniformly ultimately bounded. Finally, two simulation examples are presented to illustrate the effectiveness of the proposed approach.

Continuous-Time and Sampled-Data Stabilizers for Nonlinear Systems With Input and Measurement Delays

In this paper, we propose continuous-time and sampled-data output feedback controllers for nonlinear multi-input multi-output systems with time-varying measurement and input delays, with no restriction on the bound or serious limitations on the growth of the nonlinearities. A state prediction is generated by chains of saturated high-gain observers with switching error-correction terms and the state prediction is used to stabilize the system with saturated controls. The observers reconstruct the unmeasurable states at different delayed time-instants, which partition the maximal variation interval of the time-varying delays. These delayed time instant depend both on the magnitude of the delays and the growth rate of the nonlinearities. We also design sampled-data stabilizers as zero-order discretization of a hybrid modification (with continuous-time states and discrete-time control and innovations) of the continuous-time stabilizers.