Flexible Control Design Research Articles

Intelligent modeling of combined heat and power unit under full operating conditions via improved crossformer and precise sparrow search algorithm

The flexible operation capability of combined heat and power (CHP) unit under full operating conditions must be promoted to suppress the power grid fluctuation in large-scale integration of renewable energy. However, obtaining CHP unit model under large-scale variable loads and deep peak shaving is extremely challenging. To this end, an intelligent modeling method incorporating improved crossformer and precise sparrow search algorithm is designed to provide reference for flexible controller design. Firstly, to address the strong time dependence of CHP unit boiler-turbine coupling process, a bidirectional gated recurrent unit is employed as the position encoding to extract the hidden temporal feature. Secondly, a novel encoder structure with multi-receptive field convolution module is designed to enhance the local feature extraction capability of the network, which is conducive to the analysis of multivariable coupling characteristics of CHP unit. On this basis, a precise sparrow search algorithm with stronger search ability is formed by combining Tent chaotic mapping, osprey optimization algorithm, Cauchy mutation and quantum behavior, which provides support for the dynamic characteristics analysis of CHP unit. The time dependence, local feature information and optimal parameter settings are fully considered in the comprehensive modeling scheme, which results in an accurate identification model for CHP unit under full operating conditions. Finally, the effectiveness of the proposed method is verified based on the actual operating data of a 350 MW CHP unit. The accuracy of established model is greater than 99.4 %, which can well reflect the nonlinear characteristics of the unit. Therefore, the built model can be used for controller design and simulation analysis.

Output feedback command filtered fuzzy controller design for uncertain strict-feedback nonlinear systems with unmodeled dynamics and event-triggered strategy

An adaptive control problem is considered for a class of nth-order uncertain nonlinear systems with unmodeled dynamics. Compared with the traditional backstepping method, this article improves the systems performance by introducing command filtered and event-triggered. The former primarily enhances the flexibility of controller design, while the latter employs a variable threshold triggering mechanism to achieve savings in communication resources. Simultaneously, the estimation error is compensated by the introduced hyperbolic tangent function. In addition, fuzzy logic system (FLS) is primarily used for observer design and approximation of unknown functions. The conclusion that the closed-loop signals are bounded is obtained through stability analysis. Finally, the numerical simulation results provide a comparative validation of the effectiveness of the proposed control scheme.

Online self-learning attitude tracking control of morphing unmanned aerial vehicle based on dual heuristic dynamic programming

This article presents a partially model-free dual heuristic dynamic programming (DHP) control approach to address the online discrete-time attitude tracking problem. The DHP method learns the control policy by solving the translated optimal regulation problem of the augmented system, which is constructed with the tracking error dynamics and the desired trajectory. In the context of the augmented system, a corresponding recursive least squares (RLS) identification strategy has been devised, and the DHP update rules have been derived. The identified augmented system model not only provides dynamic information and predicted states for the actor and critic updates but also incorporates an uncertainty compensation part to reduce the learning complexity and enhance robustness of the controller. It is noteworthy that concurrent learning (CL) is employed to improve the critic convergence by utilizing an identified model-based data generation method. This approach eliminates the need for an offline learning stage and enables flexible controller design based on the available system knowledge. Based on the proposed approach, different attitude control frameworks have been designed for a morphing unmanned aerial vehicle (UAV). In addition to numerical simulations, flight tests have been conducted to further demonstrate the capabilities of the proposed approach, highlighting its effectiveness and advantages.

GWO based Robust Stabilization of DC Motor Fractional Order Speed Control System with Interval Coefficients

Robust stability analysis (RSA) is of significant concern for the robust behaviour of real-world control system applications. A stabilization strategy that assures stability and exhibits robust performance for a specified limit of system perturbations is necessary. This article presents an optimal robust stabilization method for a closed loop fractional order proportional integral derivative (FOPI^λD^µ) system involving DC motor with interval parametric uncertainty. To determine the optimum value of parameters for a FOPI^λD^µ controller to control the speed of a DC motor, Grey Wolf Optimizer (GWO), Genetic Algorithm (GA), Nelder-Mead (NM), Jaya and Whale Optimizer Algorithm (WOA) are applied with the same objective function involving ITAE criterion. FOPI^λD^µ offers two additional tuning parameters unlike a nominal PID controller and hence the former gives more flexibility in controller design than the latter in terms of transient response. The FOPID controller provides a faster closed-loop output augmented with improved robust properties of the system. Despite inherent non-linearities and time variation in system parameters, FOPI^λD^µ controllers depict enhanced performance. Using the concept of conformal mapping, robust stability analysis of fractional order polynomials is done with uncertain interval structure using Vertex and Edge theorem. Based on the value set, this paper demonstrates numerical and graphical optimal robust stability analysis of a system with variations observed in five parameters, considering the minimum argument root of the polynomial of the aforementioned closed-loop system.

A model reconstruction approach for control synthesis of Takagi-Sugeno fuzzy systems

This paper investigates the control synthesis for Takagi-Sugeno (TS) fuzzy systems via a model reconstruction approach. Firstly, based on a nonparallel distributed compensation (NPDC) control scheme and a fuzzy Lyapunov function candidate (FLF), LMI-based synthesis conditions are derived to guarantee the asymptotic stability of the closed-loop TS fuzzy system. Then the operation domain of the premise variables is divided into several subregions to provide more flexibility in control design. And a new local model reconstruction method is proposed to transform the time-derivative of the FLF into new fuzzy forms with transformed membership functions in each subregion. This enables better exploitation of information on membership functions, resulting in relaxed conditions for both stabilization and robust H∞ control. Two simulation examples are provided to verify the advantages of the proposed TS fuzzy control approach.

Global Versus Local Lyapunov Approach Used in Disturbance Observer-Based Wind Turbine Control

This contribution presents a Lyapunov-based controller and observer design method to achieve an effective design process for more dedicated closed-loop dynamics, i.e., a maximal flexibility in an observer-based controller design with a large consistency in desired and achieved closed-loop system dynamics is intended. The proposed, pragmatic approach enhances the scope for controller and observer design by using local instead of global Lyapunov functions, beneficial for systems with widely spaced pole locations. Within this contribution, the proposed design approach is applied to the complex control design task of wind turbine control. As the mechanical loads that affect the wind turbine components are very sensitive to the closed-loop system dynamic, a maximum flexibility in the control design is necessary for an appropriate wind turbine controller performance. Therefore, the implication of the local Lyapunov approach for an effective control design in the Takagi-Sugeno framework is discussed based on the sensitivity of the closed-loop pole locations and resulting mechanical loads to a variation of the design parameters.

An alternative control synthesis for stability enhancement of digital-controlled LCL-filtered grid-connected inverter

• A control synthesis is proposed to damp capacitor current resonance for LCL-filtered GCI. • The stability and performance characteristics are analyzed and compared with methods from the literature. • Intuitive design, wider stable damping region, better dynamic performance, and higher parametric robustness are the potential benefits of the proposed control synthesis. In distributed power generation systems, the inherent resonance phenomena of LCL -filtered grid-connected inverter is one of the major challenges faced by the integration of renewable energy resources with the electric power grid. In this context, the capacitor current based multi-loop active damping method is employed due to lossless resonance damping and flexible control design. However, the damping loop with the feedback compensation design suffers from relatively higher parameters sensitivity unless employed with the reduced digital delays. The filter parameters fluctuation causes a shift in resonance frequency, which leads to ineffective damping at the critical resonance frequency and may risk the control loop stability. Therefore, an alternative linear control synthesis is proposed to design the capacitor current damping loop in order to reduce the filter parameters sensitivity. The proposed design realizes the equivalent virtual impedance in an alternative manner to suppress the resonance peak and enhanced the stability characteristics of the grid-connected inverter. A detailed stability and performance analysis have been done to show the widened stable damping region and high parametric robustness of the suggested design as compared to the literature method. Simulations and experimental results are presented to confirm the theoretical findings.

Robust control of discrete minimum and non‐minimum phase systems via data‐driven virtual reference feedback tuning and IMC

AbstractIn this paper, we develop a novel robust control approach for discrete minimum and non‐minimum phase systems via a combined data‐driven virtual reference feedback tuning () and internal model control (IMC) scheme. The first step in the conventional method controller design is the selection of the closed‐loop reference model (), and selection is still an open problem. The integration of the scheme and the VRFT method provides the advantage of flexibility in controller design due to the incorporation of the filter. As a result, the proposed design method begins with the selection of and filter. Unlike the standard method, the proposed combined and design approach has the unique feature of taking into account a robustness property of dynamics, namely, maximum sensitivity () as the design specification for the and IMC filter selection. Moreover, the proposed approach includes a robustness specification that resolves the trade‐off between performance and robustness in real‐time controller design. Furthermore, the robustness guarantee with plant uncertainties and controller fragility is elucidated. The proposed approach is validated using numerical simulations and experimental validation through the temperature control process. Compared to conventional controllers, experimental and simulation results show that the proposed controllers have less tracking error, minimize control effort, and improve robustness.

Extended Dissipative Control for Interval Type-2 T-S Fuzzy Markov Jump Systems Subject to Actuator Faults

In this paper, the issue of extended dissipative control for interval type-2 fuzzy Markov jump systems subject to actuator faults is investigated. It is different from the traditional T-S fuzzy method, the interval type-2 fuzzy set is introduced to model the system, and the nonlinearity with parameter uncertainty is captured using the interval type-2 Takagi-Sugeno fuzzy model. During the implementation of the control process, the possible actuator failures are fully considered when designing a controller, and a fault-tolerant control strategy is employed to enhance the elasticity of the designed controller. It is worth noting that, in order to better adapt to the characteristics of interval type-2 fuzzy system, the designed controller follows non-parallel distribution compensation rules, that is, the designed controller does not have the same premise structure and fuzzy rules as the system model, which further improves the flexibility of controller design. Some sufficient conditions ensuring the stability with the extended dissipative performance of the systems are established. In the meanwhile, the obtained results with less conservatism are ensured owing to the boundary information of membership functions considered in the stability analysis. Finally, two numerical examples are provided to indicate the effectiveness of the proposed method.

Static Output Feedback Control for T–S Fuzzy Systems via a Successive Convex Optimization Algorithm

This article focuses on developing a static output feedback (SOF) control scheme for Takagi–Sugeno fuzzy systems. The proposed SOF controller does not share the same premise membership functions with the model, which permits enhancing the flexibility in controller design and implementation. By contrast with the state feedback case, the SOF control generally results in nonconvex design conditions. To circumvent this problem, we develop a successive convex optimization algorithm, which is based on solving a sequence of more tractable convex optimization problems obtained by approximating the nonconvex constraints with some convex ones. As a heuristic algorithm, the validity of the developed successive convex optimization algorithm is highly affected by initial conditions, and, recognizing this, we put forward an iterative procedure for determining the feasible initial condition. Finally, two illustrative examples are presented to validate the efficiency of the proposed algorithms.

Coefficient Diagram Method Based Decentralized Controller for Fractional Order TITO Systems

Fractional calculus has gained increasing attention from researchers because of providing accurate modelling and flexible controller design in control applications. More research to design controllers for Fractional Order Two-Input Two-Output (FOTITO) systems, which inherently have certain difficulties, is needed when the studies about these control applications are considered. In this study, Coefficient Diagram Method (CDM) based decentralized controllers are designed for FOTITO systems. For this, integer order approximate models of FOTITO systems are obtained and decoupled into two subsystems by using simplified and inverted decoupling configurations. Obtained high-order approximate subsystem transfer functions are reduced by a model reduction method to facilitate CDM-based decentralized controller design. Then, CDM-based decentralized controllers are designed for each subsystem, which enables to obtain the controllers of the FOTITO system. Simulation results for two different FOTITO systems, one of which is a time delay fractional order system, are demonstrated that the proposed approach exhibits successful performance.

Lyapunov-based economic model predictive control for online model discrimination

Economic model predictive control (EMPC) is a flexible control design strategy that can be modified to achieve many operating goals while also ensuring safe operation (e.g., by adding Lyapunov-based stability constraints to form Lyapunov-based EMPC, or LEMPC). Prior works have investigated LEMPC capabilities for achieving goals online beyond optimizing process economics, including aiding in model structure selection to benefit model-based control system design since the accuracy and quality of the process model are important for achieving an expected performance from such systems. This work further probes the capabilities of LEMPC to accomplish multiple objectives during process operation, including aiding in the discrimination between mechanistic models online. In particular, several rival mechanistic models may explain the existing data. To discard models from this set that do not fully represent the actual process, a new set of “online experiments” can be conducted to collect more information. However, additional experimentation may be costly and unsafe to be performed. LEMPC can aid in performing online data collection when discrimination between mechanistic models is needed, with the flexibility to ensure safety as the data is gathered and trade off the data-gathering goal for cost considerations. Motivated by this, we discuss how LEMPC can be designed to automatically and dynamically collect data that is useful for the selection of mechanistic models from among a set of possibilities. A chemical process example is used to clarify benefits and limitations of LEMPC for promoting online model discrimination.

New relaxed stability and stabilization conditions for T‐S fuzzy systems with time‐varying delays

This paper investigates the stability analysis and stabilization of T-S fuzzy systems with time-varying delays. First, a new augmented Lyapunov–Krasovskii functional is constructed, delay-dependent stability criteria in terms of linear matrix inequalities (LMIs) are obtained by combining them with the integral inequality technique and the reciprocally convex combination inequality. Based on the state space decomposition method, some piecewise membership functions are employed to approximate the membership functions. The piecewise membership functions can be locally represented in terms of the convex combinations of the supremum and infimum of some local basis functions. The boundary information of the membership functions is adequately taken into consideration in stability analysis, and then some relaxed membership-function-dependent stability results are obtained. Second, state feedback controllers for fuzzy systems with time-varying delays are presented under the imperfect premise-matching technique, whose membership functions and the number of fuzzy rules are allowed to be designed freely, consequently, the flexibility of controller design is improved. Finally, four numerical examples are given to demonstrate the effectiveness of the presented approaches.

Decentralized Control Strategies of Adjacent Building Structures Vibration under Earthquake Excitation

A decentralized control scheme can effectively solve the control problem of civil engineering structure vibration under earthquake. This paper takes a research into the decentralized control scheme of adjacent buildings when the earthquake happens. It combines overlapping decentralized control method and linear matrix inequality (LMI) with H ∞ control algorithm and puts forward the overlapping decentralized H ∞ control method. A simplified dynamical model of structural vibration control has been established considering the topology structural features of adjacent buildings. The H ∞ control algorithm is applied into each dynamically different subsystems and can be also served as the decentralized H ∞ controllers. Therefore, by contracting decentralized H ∞ controllers to original state space, overlapping decentralized H ∞ controllers are obtained. In this manner, the adjacent buildings’ structure model is analyzed in terms of simulation and calculation which provides a comprehensive insight into vibration control. The results show that the centralized control, the decentralized control, and the overlapping decentralized control, based on linear matrix inequality, can be nearly effective in cases above satisfactorily. Besides, it can also reduce the computational cost as well as increase the flexibility of controller design.

Fuzzy Functional Observer-Based Finite-Time Adaptive Sliding-Mode Control for Nonlinear Systems With Matched Uncertainties

This article is concerned with the fuzzy functional observer-based finite-time adaptive sliding-mode control for nonlinear systems with the partly unmeasurable states and some matched uncertainties. First, considering that the upper bound of the uncertain function exists but unknown, a fuzzy functional observer (FFO) with an adaptive compensator is constructed. Second, an FFO-based fuzzy integral sliding mode controller (ISMCr) is designed such that the closed-loop fuzzy systems are finite-time bounded with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H_{\infty }$</tex-math></inline-formula> performance over the reaching phase, the sliding phase, and the whole finite-time interval, respectively. To reduce the conservatism and increase the solution space of linear matrix inequality conditions, the fuzzy Lyapunov functional approach and equivalent fuzzy relaxed matrices technique are developed by introducing some relaxed matrices in the derivative of the fuzzy normalized membership function. Compared with the common Luenberger-type observer-based approach, the gain matrices of ISMCr depend on the FFO designed, which also enhances the flexibility of controller design. Finally, a simulation example with some comparison is given to show the effectiveness of the proposed method.

More relaxed stability analysis and positivity analysis for positive polynomial fuzzy systems via membership functions dependent method

In this paper, the conservatism source of the positivity and stability analysis results of positive polynomial fuzzy-model-based (PPFMB) control system are studied. Also, in order to improve the flexibility of controller design, a fuzzy controller that does not depend on the membership functions of the fuzzy model is designed. In the existing literatures, it is proved that the LCLF can reduce the conservatism of stability results. However, the LCLF generally results in non-convex conditions which is still a conservatism source. To handle the non-convex conditions, the sector nonlinear concept is applied to handle non-convex terms in stability conditions, and the obstacles caused by mismatched membership functions can be eliminated by PLMF dependent method. In addition, to relax the conservatism caused by the lack of membership functions information, the PLMF dependent positivity analysis are performed for the first time. Meanwhile, PLMF dependent method is extended to stability conditions to obtained more relaxed conditions. Finally, a simulation example is presented to verify the feasibility of this method.

Event-Triggered Synchronization for Nonlinear Multi-Agent Systems With Sampled Data

In this paper, the synchronization problem for networked nonlinear agents with time-varying control gain is studied under a synchronous periodic data sampling framework. To achieve synchronization, both the state-dependent and time-dependent event-triggered protocols are proposed. The effectiveness of these protocols is justified by Lyapunov stability theorem based on rigorous mathematical proof, facilitated by a delayed differential inequality with time-dependent parameters. Instead of specifying a lower bound for the control gain, as others usually suggested, our criteria rely on its time-average and thus allow higher flexibility in controller design. Numerical simulations are also presented to confirm the reaching of synchronization and demonstrate the wide applicability of the proposed protocols.

Stability Analysis and Estimation of Domain of Attraction for Positive Polynomial Fuzzy Systems With Input Saturation

In this paper, the stability and positivity of positive polynomial fuzzy model based (PPFMB) control system are investigated, in which the positive polynomial fuzzy model and positive polynomial fuzzy controller are allowed to have different premise membership functions from each other. These mismatched premise membership functions can increase the flexibility of controller design; however, it will lead to the conservative results when the stability is analyzed based on the Lyapunov stability theory. To relax the positivity/stability conditions, the improved Taylor-series-membership-functions-dependent (ITSMFD) method is introduced by introducing the sample points information of Taylor-series approximate membership functions, local error information and boundary information of substate space of premise variables into the stability/positivity conditions. Meanwhile, the ITSMFD method is extended to the PPFMB control system with input saturation to relax the estimation of domain of attraction. Finally, simulation examples are presented to verify the feasibility of this method.

Coordinated path-following control for networked unmanned surface vehicles

In this article, the coordinated path-following control problem for networked unmanned surface vehicles is investigated. The communication network brings time delays and packet dropouts to the fleet, which will have negative effects on the control performance of the fleet. To attenuate the negative effects, a novel networked predictive control scheme is proposed. By introducing the predictive error into the control scheme, the proposed control strategy admits some advantages compared with existing networked predictive control strategies, for example, a degree of robustness to disturbances, lower requirements for the computing capacity of the onboard processors, high flexibility in controller design, and so on. Conditions that guarantee the control performance of the overall system are derived in the theoretical analysis. At last, experiments on hovercraft test beds are implemented to verify the effectiveness of the proposed control scheme.

Distributed Control for Cooperative Manipulation With Event-Triggered Communication

Cooperative manipulation tasks can be divided into the subtasks of object trajectory tracking and grasp maintenance. Both subtasks typically pose individual requirements on the underlying control objective in terms of accuracy and robustness to disturbances. In this article, we propose a novel distributed impedance control scheme, resulting in a flexible control design to meet the individual-potentially conflicting-impedance goals of the respective subtasks. In order to achieve more efficient use of the communication resource, we propose an event-triggered strategy for the communication between the robotic agents. Robust, stability also in interaction with unknown objects and unknown environments, is guaranteed via passivity-based control design. Simulations and experimental results show that, with the proposed communication strategy, the number of transmissions between agents can be significantly reduced while maintaining the flexibility introduced by the distributed controller.