Dynamic Control Design Research Articles

On the Separation Principle in Dynamic Output Controller Design for Uncertain Linear Systems

In this paper, the problem of designing the improper dynamic output controllers for uncertain linear multi-input/multi-output continuous-time systems is considered. In the principal part, the system state coordinate transform is outlined and matrix variable structures are found, defining the solution based on the structured linear matrix inequalities and linear matrix equality. Of principal novelty is the framework related to the system uncertainties with newly introduced inequality structures and their partial factorization. The main results are shown in detail by the example to characterize potential application of the method for quadratic stabilization of uncertain linear systems by the improper dynamic output controllers.

Control-oriented dynamic modeling and thermodynamic analysis of solid oxide electrolysis system

Solid oxide electrolysis cell (SOEC) is a potential technology for increasing renewable energy penetration by turning excess power into hydrogen-based chemical energy, due to its high efficiency. In light of the intermittency of renewable energy, SOEC has to operate in a dynamic-changing environment. Therefore, dynamic modeling and thermodynamic analysis of solid oxide electrolysis systems are crucial for control design and efficient operation. In order to address various voltage losses, this paper develops a dynamic model of the solid oxide electrolysis system, in which the stack voltage is derived based on the electrochemical mechanism. The stack temperature is described by considering the dynamics of convective and radiative heat transfer between the layers. Dynamics of balance of plant (BOP) are modeled based on the mass and energy conservation laws. The model accuracy is verified by the polarization curve under different temperatures. The effects of temperature and current density on the electrolytic voltage are discussed. Three different operation modes (endothermic, thermoneutral and exothermic) are discussed in terms of different current density regions, where the balance between the electrochemical loss and reaction heat consumption varies. Steady-state energy and exergy flow diagrams are used to compare the energy consumption of different components in terms of different inlet temperatures, aiming to determine the optimal conditions with maximum system efficiency. The electrolyzer is revealed as the component with the highest energy consumption and exergy destruction. Dynamic simulation of the stack temperature is carried out in response to various control inputs and disturbances. Dynamic simulation exhibits the response rapidity of variables, strong couplings and different variation trends (monotone, overshoot or initial inverse response), due to the different time scales of electrochemical reaction, gas flow and heat transfer. This study lays a solid foundation for system optimization and dynamic control design.

A three-region movable-boundary helical coil once-through steam generator model for dynamic simulation and controller design

A simple but accurate mathematical model is crucial for dynamic simulations and controller design of helical coil once-through steam generator (OTSG). This paper presents a three-region movable boundary dynamic model of the helical coil OTSG. Based on the secondary side fluid conditions, the OTSG is divided into subcooled region (two control volumes), two-phase region (two control volumes) and superheated region (three control volumes) with movable boiling boundaries between each region. The nonlinear dynamic model is derived based on mass, energy and momentum conservation equations. And the linear model is obtained by using the transfer function and state space transformation, which is a 37-order model of five input and three output. Validations are made under full-power steady-state condition and four transient conditions. Results show good agreements among the nonlinear model, linear model and the RELAP5 model, with acceptable errors. This model can be applied to dynamic simulations and controller design of helical coil OTSG with constant primary-side flow rate.

Dynamic state feedback controller and observer design for dynamic artificial neural network models

Artificial neural networks are black-box models that can be used to model nonlinear dynamical systems. This article presents a synthesis method for full dynamic state feedback controllers and state and output observers that have guaranteed properties for systems approximated by dynamic artificial neural networks. The resulting control designs are applicable to the practical situation in which the steady-state values for the control input are not known. Dynamic artificial neural networks are written in the standard nonlinear operator form, also known in the literature as the Luré formulation. A generalized form of the Luré formulation is adopted to allow for the representation of deep ℓ-layer networks, ℓ≥1. Sufficient conditions for controller synthesis and observer design are derived in the form of linear matrix inequalities, using a quadratic Lyapunov function. The synthesis method is demonstrated for the control of pH in two tanks in series and a numerical example.

SytaB: A Class of Smooth-Transition Hybrid Terrestrial/Aerial Bicopters

This work details the design, modeling and control of SytaB, a vehicle capable of hybrid terrestrial/aerial mobility with smooth transition, where the structure embedding a bicopter is adopted for the first time. In contrast to previous hybrid terrestrial/aerial vehicles with a quadrotor embedded, SytaB not only requires less energy for the same takeoff weight, but also regulates its attitude less frequently to restrain the vibration of sensors. Three modes, the terrestrial/aerial/transitional modes, are considered <styled-content style="color:black" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">for the vehicle</styled-content> , and the dynamics modeling and controller design of each mode are <styled-content style="color:black" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">carried out</styled-content> . The transition between terrestrial and aerial locomotions is smooth compared with the case of non-inclusion <styled-content style="color:black" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">of</styled-content> transitional mode such that the bounce and shake of the vehicle are alleviated. The energy efficiency is compared between the terrestrial and aerial modes, and between the energy-saving and high-maneuverability paradigms (a choice enabled in the terrestrial mode of SytaB). Experimental results are presented to demonstrate the potential of SytaB, the effectiveness of the designed controllers, and the necessity of considering the transition process.

Adaptive neural control for coordinated rolling motion of vessels with zero speed via composite learning

An intelligent control method under the backstepping and dynamic surface control (DSC) design framework is proposed in this article. The proposed method is aiming at solving the problem of coordinated rolling motion control of the supply and receiving vessels with internal dynamic uncertainty, unknown external nonlinear disturbance, input saturation, and unmeasurable rolling angular velocity. In the control design, a neural-based state observer is constructed to generate the unmeasurable rolling angular velocity, which achieves a separate design from the control law. To compensate the input saturation effect, a second-order auxiliary dynamic system (ADS) is designed, and its states are inset into the transformation of coordinates required by backstepping. To realize the accurate reconstruction of the neural network for the dynamic uncertainty, the adaptive neural and nonlinear disturbance observer (NDO) are used to reconstruct the internal dynamic uncertainty and unknown external disturbance, respectively. And then the serial–parallel estimation model is introduced to establish a predictor of the system state, and a composite adaptive learning law is designed, which includes the velocity error and predictor error. As a result, the idea of classification reconstruction is developed. Finally, an adaptive neural composite learning coordinated rolling motion control solution is developed. Lyapunov stability analysis shows that all signals in the closed-loop control system are bounded. The effectiveness of the control solution is verified by the simulations.

Dynamic Modeling and Attitude Control of Large-Scale Flexible Parallel Multibody Spacecraft

In this paper, modeling and high-precision attitude control of large-scale flexible parallel multibody spacecraft (FPMS) are addressed. First, for describing the complex constraint relationship between each body of the multibody system, the dynamic model of flexible spacecraft in the form of differential-algebraic equations (DAEs) is established by applying the Lagrange equation of the first kind (augmented Lagrange method). Then, to facilitate numerical integration of the dynamic model and controller design, an independent coordinate method and block matrix operation-based transformation method are proposed to convert DAEs into ordinary differential equations. Based on this model, a two-timescale composite controller is designed by virtue of the singular perturbation theory to attain high-precision attitude control of the FPMS and suppress the vibration of flexible appendages. Finally, numerical simulations are conducted under two mission scenarios to validate the dynamic model and demonstrate that the accurate attitude control can be achieved.

Distributed model-free formation control of networked fully-actuated autonomous surface vehicles.

This paper presents a distributed constant bearing guidance and model-free disturbance rejection control method for formation tracking of autonomous surface vehicles subject to fully unknown kinetic model. First, a distributed constant bearing guidance law is designed at the kinematic level to achieve a consensus task. Then, by using an adaptive extended state observer (AESO) to estimate the total uncertainties and unknown input coefficients, a simplified model-free kinetic controller is designed based on a dynamic surface control (DSC) design. It is proven that the closed-loop system is input-to-state stable The stability of the closed-loop system is established. A salient feature of the proposed method is that a cooperative behavior can be achieved without knowing any priori information. An application to formation control of autonomous surface vehicles is given to show the efficacy of the proposed integrated distributed constant bearing guidance and model-free disturbance rejection control.

Adaptive Finite-Time Fault-Tolerant Control for Uncertain Flexible Flapping Wings Based on Rigid Finite Element Method

The bionic flapping-wing robotic aircraft is inspired by the flight of birds or insects. This article focuses on the flexible wings of the aircraft, which has great advantages, such as being lightweight, having high flexibility, and offering low energy consumption. However, flexible wings might generate the unexpected deformation and vibration during the flying process. The vibration will degrade the flight performance, even shorten the lifespan of the aircraft. Therefore, designing an effective control method for suppressing vibrations of the flexible wings is significant in practice. The main purpose of this article is to develop an adaptive fault-tolerant control scheme for the flexible wings of the aircraft. Dynamic modeling, control design, and stability verification for the aircraft system are conducted. First, the dynamic model of the flexible flapping-wing aircraft is established by an improved rigid finite element (IRFE) method. Second, a novel adaptive fault-tolerant controller based on the fuzzy neural network (FNN) and nonsingular fast terminal sliding-mode (NFTSM) control scheme are proposed for tracking control and vibration suppression of the flexible wings, while successfully addressing the issues of system uncertainties and actuator failures. Third, the stability of the closed-loop system is analyzed through Lyapunov's direct method. Finally, co-simulations through MapleSim and MATLAB/Simulink are carried out to verify the performance of the proposed controller.

Robust Trajectory-Tracking for a Bi-Copter Drone Using INDI: A Gain Tuning Multi-Objective Approach

This paper presents an optimized robust trajectory control system for an autonomous tiltrotor bi-copter based on an incremental nonlinear dynamic inversion (INDI) strategy combined with a set of PID/PD controllers. The methodology includes a lower level, fast attitude control action using an incremental nonlinear dynamic inversion (INDI) strategy, which is driven by a higher level, slow trajectory control action that uses nonlinear dynamic inversion (NDI). The nonlinear dynamic model of the drone is derived, and the basis of the motion and the design of the attitude and position stabilizing controllers are discussed. To develop and test the suggested controller, a circle-shaped flight profile is simulated. The linear control providing inputs to the NDI and INDI controllers is tuned via a novel multi-objective optimization auto-tuning method using the non-dominated sorting genetic algorithm II (NSGA-II). The tracking and disturbance rejection optimization is achieved via the use of the integral of time multiplied by the absolute error (ITAE) and the integral of the square of the error (ISE) objective functions, which are optimized concurrently. The simulation results reveal that the proposed control design outperforms the traditional dynamic inversion controller design and demonstrate that the developed INDI + PID/PD controller possesses exceptional accuracy and performance, enabling the tiltrotor bi-copter to track the given trajectory. Furthermore, the paper shows that the proposed controller produces 40% lower overshoot and settling time as measured with respect to previous backstepping controllers reported in the literature. The robustness of the controller is validated through diverse tests where the aircraft is subjected to external (wind gust) disturbances.

Adaptive composite dynamic surface neural control for nonlinear fractional-order systems subject to delayed input

In the article, the adaptive composite dynamic surface neural controller design problem for nonlinear fractional-order systems (NFOSs) subject to delayed input is discussed. A fractional-order auxiliary system is first designed to solve the input-delay problem. By using the developed novel estimation models, the defined prediction errors and the states of error system can decide the weights of radial basis function neural networks (RBFNNs). During the dynamic surface controller design process, the developed fractional-order filters are designed to handle the complexity explosion problem when the classical backstepping control technique is utilized. It is shown that the designed adaptive composite neural controller ensures that all the system state variables are bounded and the tracking error of the considered system finally tends to a small neighborhood of zero. Finally, the results of the simulation explain the feasibility of the developed controller. In addition, the developed controller can also be applied to single input and single output(SISO) nonlinear systems subject to a unitary input function.

A Decade of the Z-Numbers

In this article, we present a study on the development in the theory and application of the Z-numbers since its inception in 2011. The review covers the formalization of Z-number-based mathematical operators, the role of Z-numbers in computing with words, decision-making, and trust modeling, application of Z-numbers in real-world problems such as multisensor data fusion, dynamic controller design, safety analytics, and natural language understanding, a brief comparison with conceptually similar paradigms, and some potential areas of future investigation. The paradigm currently has at least four extensions to its definition: multidimensional Z-numbers, parametric Z-numbers, hesitant-uncertain linguistic Z-numbers, and Z*-numbers. The Z-numbers have also been used in conjunction with rough sets and granular computing for enhanced uncertainty handling. While this decade has seen a plethora of theoretical initiatives toward its growth, there remains a major work scope in the direction of practical realization of the paradigm. Some challenges yet unresolved are automated translation of (imprecise, sarcastic, and metaphorical) linguistic expressions to their Z-number forms, discernment of probability–possibility distributions to map real-world situations under consideration, analysis of linguistic equivalents of Z-operator results to intuitive human responses, the endogenous arousal of belief in intelligent agents, and analysis of biases embedded in expert-belief values that are primary inputs to Z-number-based expert systems. After a decade of the Z-numbers, the paradigm has proved to be of use in expert-input-based decision-making systems and initial value problems. Its applicability in high-risk, high-precision areas, such as deep-sea exploration and space science, remains unexplored.

Modeling with Beat Frequency Dynamics and Phase-Frequency Control Design for a Dual-Bridge Series Resonant Converter

Phase-frequency modulation is an effective soft-switching technique for a dual-bridge series resonant converter (DBSRC). In the literature, the DBSRC topology is investigated with phase-frequency control to increase the soft-switching region and to reduce the reactive power circulation in the high-frequency link. However, behavior of the circuit during transient was not addressed, especially when multiple input variables are involved. This article proposes a small-signal model for the DBSRC considering phase shift and switching frequency as the control parameters. The proposed model studies the dependence of the control variables. Therefore, a compensator design is proposed, which helps the two control loops to operate independently. Transient response of the DBSRC is extensively studied by providing a step load and a step change in output voltage reference. Proposed modeling accurately predicts the transient behavior of the circuit. The beat-frequency phenomenon, which is often found in resonant converters, can also be predicted in this model. The design of resonant tank parameters is also addressed in this article. Finally, the proposed model and the control method are verified in a 2.7 kW hardware prototype.

A Dynamic Controller Design for Trajectory Tracking Control of Wheeled Mobile Robot Under Stochastic Denial of Service Attacks

This brief investigates the trajectory tracking control problem for a networked wheeled mobile robot (WMR) under stochastic denial of service (DoS) attacks. The attacker presents a considerable security threat to the wireless network by sending a large amount of redundant data, resulting in communication interruption and packets dropout. In view of random properties of the DoS attack, a Markov modulated DoS attack strategy is considered, where the attacker stochastically jams the transmitted signals using a Markov model. To defend against the DoS attacks, a hybrid dynamic model is developed and a novel dynamic controller is designed to attain desired tracking performance for the WMR. Then sufficient condition of uniformly globally asymptotically stability (UGAS) is given for the hybrid system. A practical experiment is conducted for the WMR connected by the wireless network under stochastic DoS attacks, which expresses the validity and effectiveness of the proposed theoretical results.

Dynamic model-based back-stepping control design for-trajectory tracking of seabed tracked vehicles

Dynamic model-based back-stepping control design for-trajectory tracking of seabed tracked vehicles

Non-Linear Bandwidth Extended-State-Observer Based Non-Smooth Funnel Control for Motor-Drive Servo Systems

This article proposes a non-smooth funnel control (NSFC) strategy based on the extended state observer (ESO) for motor-drive servo systems with unknown dynamics. These unknown dynamics, including the modeling error, the external disturbances, and the non-linear friction, are considered as a lumped disturbance. For estimating the unknown states and the lumped disturbance, a non-linear bandwidth extended state observer (NLB-ESO) is developed. The NLB designed using the Gaussian function and the position observation error can deal with the “peaking value problem” caused by the high observer gains. Furthermore, a funnel error surface is proposed based on the non-smooth error transformation (NSET) and incorporated into the dynamic surface control design procedure, where the NSFC can prescribe the position tracking error in a funnel region with small steady-state error and overshoot and short adjustment time. The non-smooth funnel error surface also avoids the singular problem in controller design by selecting the non-smooth parameter. Moreover, the stability of the closed-loop control system with the NLB-ESO is guaranteed by the Lyapunov theory. Comparative simulations and experiments show that the proposed NSFC can prescribe the tracking error in a funnel and ensure better tracking performance.

Transportation of Payload Using Multiple Quadrotors via Rigid Connection

Due to the limited payload capability of an aerial robot, multiple quadrotors can be used to manipulate payloads in aerial transportation, construction, and assembly tasks. This paper focuses on the cooperative transportation of a payload rigidly attached to multiple quadrotor bodies. These quadrotors may have different orientations. The dynamics equation of a rigid body in 3-D space is derived to describe the motion of such a transportation system. Robust position and attitude controllers are designed to drive the system to the desired pose. To assign control signals for each quadrotor, the control command allocation method compatible with the case that partial or all quadrotors are in parallel planes is developed. Finally, experimental results are presented to validate the effectiveness of the proposed controllers and control command allocation methods. Different from classical works in this field, this paper can solve the dynamics modeling, controller design, and control command allocation problems for the transportation of a rigidly connected payload using a team of quadrotors with different orientations.

Adaptive neural network dynamic surface control of uncertain strict-feedback nonlinear systems with unknown control direction and unknown actuator fault

In this paper, the tracking control problem of a class of uncertain strict-feedback nonlinear systems with unknown control direction and unknown actuator fault is studied. By using the neural network control approach and dynamic surface control technique, an adaptive neural network dynamic surface control law is designed. Based on the neural network approximator, the uncertain nonlinear dynamics are approximated. Using the dynamic surface control technique, the complexity explosion problems in the design of virtual control laws and adaptive updating laws can be overcome. Moreover, to solve the unknown control direction and unknown actuator fault problems, a type of Nussbaum gain function is incorporated into the recursive design of dynamic surface control. Based on the designed adaptive control law, it can be confirmed that all of the signals in the closed-loop system are semi-global bounded, and the convergence of the tracking error to the specified small neighborhood of the origin could be ensured by adjusting the designing parameters. Finally, two examples are provided to demonstrate the effectiveness of the proposed adaptive control law.

Dynamic Behavior Analysis and Vector Controller Design for a Variable Speed Pumped Storage Power Plant in Generator and Motor Operating Mode

Dynamic Behavior Analysis and Vector Controller Design for a Variable Speed Pumped Storage Power Plant in Generator and Motor Operating Mode

Design of Neural Network-Based Adaptive Inverse Dynamics Controller for Motion Control of Stewart Platform

This research article attempts to design Inverse Dynamics Controller (IDC) to execute the motion tracking of Stewart Platform. In the presence of modeling uncertainties and external disturbances, the closed-loop dynamic equation of IDC with fixed gains becomes nonlinear and configuration-dependent, which compromises the motion tracking accuracy. Further, both modeling uncertainties and external disturbances are unavoidable in real life conditions. To tackle this issue, this article proposes a novel control algorithm by combining IDC with Feed-Forward Artificial Neural Network (FF-ANN) trained using PSO. The proposed modified control algorithm offers superior motion tracking accuracy in comparison with traditional IDC.