Output Feedback Controller Design Research Articles

Inverse optimally adaptive neural output‐feedback control of stochastic nonlinear systems

SummaryIn this article, for a class of stochastic nonlinear systems with non‐strict feedback, a neural adaptive inverse optimal output feedback control design scheme is presented. First, according to the existing inverse optimal criterion, an auxiliary system is established. On this basis, a novel observer is built to evaluate the unpredictable states. Second, in the control process, neural networks (NNs) are applied to estimate the unknown functions. Based on NNs and the backstepping technology, an adaptive neural inverse optimal output feedback controller is established. It is indicated that the proposed scheme could ensure the semi‐globally uniformly ultimately bounded of the closed‐loop system and also achieve the objective of inverse optimality. Eventually, an example is applied to testify the feasibility of this scheme.

Event-triggered output feedback control design for polynomial fuzzy systems

This paper presents a novel approach to event-triggered output feedback control of polynomial fuzzy systems that exhibit unmeasurable system states. To stabilize the system state, a fuzzy model-based event-triggered control scheme is proposed based on output measurement information. The transmission frequency is modulated using a periodic event-trigger protocol, which is scheduled by resorting to a set of historically released packets to ensure better dynamic performance. The event-triggering condition uses the current samples to determine the next trigger and also takes into account the triggering time-dependent gain parameter, thereby significantly reducing network burden. The proposed approach ensures the existence of both polynomial controller gain and event-triggered parameters by satisfying sufficient conditions based on the sum-of-squares approach and the polynomial fitting approximation algorithm. The addressed system’s stability conditions are solved using MATLAB SOSTOOLS. Three numerical examples, including an invented pendulum model, illustrate the superiority and applicability of the methodology. The main contributions of this work are the introduction of a triggering instant dependent periodic event-triggered output feedback control design, which reduces network traffic and enhances dynamic performance. The approach is validated through numerical examples, which demonstrate its superiority and applicability.

Output feedback and adaptive output feedback control design for input-affine nonlinear systems based on Riemannian metrics

This paper deals with output feedback and adaptive output feedback control design for input-affine nonlinear systems on the basis of Riemannian metrics. Firstly, an observer for the considered class of nonlinear systems is proposed. Then, it is shown that by combining the introduced observer with state-feedback controllers in a suitable way, output tracking can be achieved. Moreover, the obtained results are extended to be used for adaptive output feedback stabilization of uncertain nonlinear systems. The introduced adaptive controller is more general than the existing adaptive output feedback ones because it does not assume any constraint on the structure of the system and the parametric uncertainty. Simulation results are presented to verify the effectiveness of the proposed control methods.

Improved Event-Triggered Dynamic Output Feedback Control for Networked T-S Fuzzy Systems With Actuator Failure and Deception Attacks.

This article addresses the problem of event-triggered dynamic output feedback controller design for networked Takagi-Sugeno (T-S) fuzzy systems subject to actuator failure and deception attacks. In order to save network resources effectively, two event-triggered schemes (ETSs) are introduced to test whether the measurement output and control input are transmitted under network communication. While the ETS brings advantages, it also leads to a mismatch between the premise variables of the system and the controller. To solve this problem, an asynchronous premise reconstruct method is considered, which relaxes the condition of the previous results that the premises of the plant and the controller are synchronous. Furthermore, two crucial factors, including actuator failure and deception attacks, are taken into consideration simultaneously. Then, the mean square asymptotic stability conditions of the resultant augmented system are derived by utilizing the Lyapunov stability theory. Besides, controller gains and event-triggered parameters are co-designed with the help of linear matrix inequality techniques. Finally, a cart-damper-spring system and a nonlinear mass-spring-damper mechanical system are presented to verify the theoretical analysis.

Output Feedback Control Design for Switched Systems with Unmatched Uncertainties Based on the Switched Robust Integral Sliding Mode

This paper proposes an output feedback sliding-mode control design based on a switched robust integral sliding mode for switched systems with unmatched uncertainties. First, the control task based on the observer is given while the system state information cannot be measured directly. Then, the switched robust integral sliding mode is constructed on the space of the estimated state, while the parameters of the switched robust integral sliding mode are selected ensuring that the system state in the sliding mode is robustly and exponentially stable. Linear matrix inequality conditions for the stabilization switching rule and the common Lyapunov function criterion are achieved. Consequently, the corresponding sliding-mode controller is designed based on the state estimation and the switched robust integral sliding mode. Finally, the application simulation results in a one-link manipulator with the load change validate the effectiveness and feasibility.

Asynchronous Event-Triggered Output-Feedback Control of Singular Markov Jump Systems.

This study focused on the asynchronous event-triggered output-feedback controller design problem for discrete-time singular Markov jump systems (MJSs). A hidden Markov model (HMM) was employed to estimate the system mode, which cannot always be ideally detected in practice. Because the full state is also difficult to obtain in practical scenarios, an output-feedback control scheme was used. In addition, an HMM-based event-triggered mechanism was also employed in the design of the controller to reduce the communication burden of the networked system. Sufficient conditions for the stochastic admissibility of a closed-loop singular MJS with a prescribed H∞ performance index were established using the Lyapunov functional technique. Finally, design procedures for an asynchronous event-triggered controller were summarized as a linear-matrix-inequality-based optimization algorithm. Two examples were considered to verify the effectiveness of the asynchronous event-triggered output-feedback controller design method.

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.

Adaptive optimal output regulation of unknown linear continuous-time systems by dynamic output feedback and value iteration

This paper proposes a value iteration based learning algorithm to solve the optimal output regulation problem for linear continuous-time systems, which aims at achieving disturbance rejection and asymptotic tracking simultaneously. Notably, the state is unmeasurable and the system dynamics is completely unknown, which greatly increases the challenge of solving the output regulation problem. Firstly, we present a dynamic output feedback controller design method by combining the internal model, setting a virtual exo-system, and constructing the augmented internal state without using any knowledge of system. Then, by establishing a novel iterative learning equation which requires no repeated finite window integral operations, an adaptive dynamic programming based learning algorithm with employing value-iteration scheme is proposed to estimate the optimal feedback control gain, which may lead to a reduction of the computational load. The analysis on solvability and convergence shows that the estimated control gain converges to the optimal control gain. Finally, a physical experiment on control of an unmanned quadrotor illustrates the effectiveness of the proposed algorithm.

MPPT Novel Controller Based on Passivity for the PV Solar Panel-Boost Power Converter Combination

This article deals with the MPPT controller based on passivity, which calculates through a dynamic average model between the PV solar panel and the boost power converter under uniform irradiance conditions. The MPPT algorithm is a linear controller based on the exact tracking error dynamics passive output feedback (ETEDPOF) controller design methodology. The algorithm includes suitable adaptive feedforward pre-compensation, which explicitly depends on the precisely estimated resistance of the boost converter. The MPP reaches by regulating the voltage and current of the boost converter to hold the PV maximum power in the system's input capacitor. The controller desired references such as the inductor current, the output voltage, and the average input control are in terms of the PV maximum power nominal values, which remain always fixed. A reduced-order extended state observer (RESO) develops to estimate the boost converter's output resistance, adapting to the controller's desired references. Simulation and experimental results demonstrate the effectiveness, stability, and robustness of the MPPT algorithm based on passivity as compared to the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Incremental Conductance</i> (IC) algorithm, under sudden output resistor changes, high irradiance, and high temperature in the solar panels.

Finite-Time Adaptive Fuzzy Event-Triggered Output-Feedback Containment Control for Nonlinear Multiagent Systems With Input Saturation

This paper considers the finite-time containment output-feedback control problem for a class of nonlinear multi-agent systems (MASs) with unmeasurable states and input saturation. Fuzzy logic systems (FLSs) and a smooth function are first employed to model unknown agent's subsystems and input saturation, respectively. Then, a novel fuzzy state observer is established via the intermittent output signal. By introducing first-order filter and using the sampled estimating states and triggered output signals, an event-triggered mechanism consisting of the sensor-to-controller and controller-to-actuator is formulated. Consequently, under the framework of the finite-time stability criterion and adaptive backstepping control design technique, a finite-time adaptive fuzzy event-triggered output-feedback containment control design method is proposed and the semiglobal finite-time stability of the controlled system is rigorously proved. Finally, the simulation results are given to confirm the effectiveness of the proposed control scheme.

Observer-Based Fuzzy Adaptive Tracking Control of Nonlinear Strict-Feedback Systems via a Unique Event Triggering Approach

It is known that a controller designed via backstepping contains all virtual control signals constructed at the previous design steps. When this controller is used to generate an event-triggered control signal which is discontinuous, those virtual control signals are simultaneously triggered and the corresponding triggered virtual signals are utilized for the real control operation, they are also discontinuous. This fact results in the non-continuity of the virtual error dynamics. This problem is ignored in the existing backstepping-based event-triggered control schemes, in which control design and stability analysis are implemented according to the continuous virtual error dynamics, namely, all the virtual control signals are required to be continuous. Based on this observation, this paper focuses on the backstepping-based event-triggered fuzzy adaptive control for nonlinear strict-feedback systems with unavailable states. To do so, a concurrent event generator is first introduced, furthermore, the backstepping-based output feedback control design is given according to the discontinuous virtual error dynamics. Then, the Lyapunov approach for nonlinear impulsive systems is used to provide the closed-loop performance analysis. The results show that with the event-based fuzzy adaptive controller, all the closed-loop signals meet bounded requirements and the Zeno phenomenon is prevented. Ultimately, the proposed theoretical results are supported by two simulation examples.

New Results on Nonsynchronous-Observer-Based Output-Feedback Control of Fuzzy-Affine-Model-Based Discrete-Time Nonlinear Systems

This work investigates the non-synchronous observer-based output feedback control of nonlinear systems by T-S fuzzy affine models. In order to estimate the unmeasurable system states and handle the non-synchronization issue of premise variables between the observer/controller and the original system, a new non-synchronous piecewise fuzzy affine observer is firstly synthesized to estimate the unmeasurable system state variables. Via utilizing some elegant matrix inequalities with convexification techniques, new observer-based piecewise output feedback controller design results are proposed within the framework of piecewise quadratic Lyapunov functions. Simulation studies are given to illustrate the validity of the developed approach.

Dynamic Output Feedback Quantization Control of a Networked Control System with Dual-Channel Data Packet Loss

In this paper, the design of a dynamic output feedback controller for a networked control system with dual-channel data packet loss and special discrete-time delay is studied, in which the data packet loss is described by the Markov process. In order to effectively alleviate the problem of network congestion, a quantizer was added to the sensor-to-controller channel. The transition probabilities of the Markov process are uncertain, but they exist in the convex sets of known convex polyhedron types. The mode-dependent Lyapunov function was constructed, and a sufficient condition was given to make the closed-loop system stochastically stable and satisfy the performance index. The parameters of the controller were solved by the linear matrix inequality method. Finally, an example of aircraft shows the validity of the proposed approach. A numerical example is compared with other literature, showing the superiority of the proposed approach.

Observer-Based Output-Feedback Control for Nonlinear Time-Delay Systems

In this article, the output feedback control design problem for a class of strict-feedback nonlinear systems with unknown state time-varying delays is addressed. Two output feedback control schemes are considered which are based on full-order and reduced-order observer, respectively. Lyapunov–Krasovskii functionals with static gain are used to develop novel memoryless control strategies. The above work ensures that all the signals in the closed-loop system are bounded and the system is asymptotically stable. Finally, two simulation examples illustrate the effectiveness of the two control schemes we proposed.

Output feedback control design for quadrotor using recursive least square dynamic inversion

Output feedback control design for quadrotor using recursive least square dynamic inversion

Robust Output Feedback Control Design in the Presence of Sporadic Measurements

Output feedback control design for linear time-invariant systems in the presence of sporadic measurements and exogenous perturbations is addressed. To cope with the sporadic availability of measurements of the output, a hybrid dynamic output feedback controller equipped with a holding device whose state is reset when a new measurement is available is designed. The closed-loop system, resulting from the interconnection of the controller and the plant, is augmented with a timer variable triggering the arrival of new measurements and its properties are analyzed using hybrid system tools. Building upon Lyapunov theory for hybrid systems, sufficient conditions for internal and L2 input-to-output stability are proposed. An LMI-based design methodology for the co-design of the gains of the controller and the parameters of the holding device is presented. The effectiveness of the proposed LMI-based design approach is showcased in a numerical example.

Mixed H∞ and passive resilient control for discrete-time systems with Round-Robin protocol and deception attacks

This paper focuses on the mixed [Formula: see text] and passive resilient control problem for uncertain discrete-time networked control systems subject to time delays. The measurement output is considered to be quantized by the dynamic quantizer before transmission through an unreliable communication channel, where the deception attacks may occur randomly. To further reduce the communication burden, the Round-Robin (RR) protocol is adopted to schedule sensors in a predetermined transmission order. Meanwhile, a new performance index is developed to solve restriction of the same dimension of control output and the exogenous disturbance. The S-procedure combined with a two-step uncertainties elimination method is adopted to deal with the nonlinear product terms involving multiple uncertainties. Furthermore, a decoupling method is introduced for static output feedback controller design. Sufficient conditions of the resilient controller and the dynamic quantizer are provided, which can improve the robustness of resulting closed-loop system and guarantee its stochastic stability and the specified mixed [Formula: see text] and passivity performance index. Finally, an example is given to illustrate the feasibility of the theoretical results.

A New Design of Fuzzy Affine Model-Based Output Feedback Control for Discrete-Time Nonlinear Systems

The observer-based output feedback control problem for nonlinear systems is studied through Takagi-Sugeno fuzzy affine models. A novel piecewise fuzzy affine observer is designed to estimate the unmeasurable state variables. To remove the assumptions in the existing works that the system input matrices and/or output matrices should be common, full rank and uncertainties-free, a new approach is developed for the observer-based piecewise output feedback controller synthesis by making sufficient use of Young&#x0027;s inequality with its variance. Through fuzzy piecewise quadratic Lyapunov functions, improved and less conservative results on the output feedback controller design are obtained in terms of linear matrix inequalities, and the restrictions imposed in the existing results can be relaxed. Simulation studies are provided to confirm the effectiveness and conservatism reduction of the developed method.

Near-Optimal Design of Safe Output-Feedback Controllers From Noisy Data

As we transition towards the deployment of data-driven controllers for black-box cyberphysical systems, complying with hard safety constraints becomes a primary concern. Two key aspects should be addressed when input-output data are corrupted by noise: how much uncertainty can one tolerate without compromising safety, and to what extent is the control performance affected? By focusing on finite-horizon constrained linear-quadratic problems, we provide an answer to these questions in terms of the model mismatch incurred during a preliminary identification phase. We propose a control design procedure based on a quasiconvex relaxation of the original robust problem and we prove that, if the uncertainty is sufficiently small, the synthesized controller is safe and near-optimal, in the sense that the suboptimality gap increases <i>linearly</i> with the model mismatch level. Since the proposed method is independent of the specific identification procedure, our analysis holds in combination with state-of-the-art behavioral estimators beyond standard least-squares. The main theoretical results are validated by numerical experiments.

Positivity and separation principle for observer-based output-feedback disturbance attenuation of uncertain discrete-time fuzzy models with immeasurable premise variables

We propose a robust observer-based output-feedback (OBOF) controller design method for the positive stabilization of discrete-time Takagi–Sugeno (T–S) fuzzy models subject to immeasurable premise variables, parametric uncertainties, and l∞ disturbances. The results of this paper are as follows. First, a new fuzzy OBOF controller structure is used to improve the possibility that the uncertain closed-loop system is positive. Second, the separation principle is demonstrated for the l∞–l∞ disturbance attenuation. Finally, the stability, positivity, and disturbance attenuation of T–S fuzzy closed-loop models are analyzed in the presence of the perturbation caused by the imperfect premise matching by the immeasurable premise variable.