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This study develops a stabilization control algorithm for a heavy weight yaw/pitch 2-axis gimbal. First, the 6 degree-of-freedom (DOF) equation of motion is derived and then mechanical constraints are applied to establish the motion equation of the 2-axis gimbal. Second, the stabilization control algorithm is divided into an outer loop and an inner loop. The disturbance observer (DOB) and the adaptive neural network (ANN) are applied to outer loop and inner loop, respectively. The outer loop uses the angular velocity measured by the inertial measurement unit (IMU) sensor, and the inner loop uses the angular velocity measured by the encoder sensor. The angular velocity of the platform on which the 2-axis gimbal is mounted is calculated using the angular velocity measured by the IMU sensor and the angular velocity measured by the encoder sensor. The calculated value from the outer loop is converted to angular rate command of inner loop. Finally, the performance of stabilization control algorithm for heavy 2-axis gimbal is confirmed by experiment after mounting a heavy 2-axis gimbal to 6 DOF motion platform. This research is expected to have an impact on the development of turrets used in armored vehicles and tanks. [ABSTRACT FROM AUTHOR]

As the penetration of the integrated intermittent and fluctuating new energy (e.g., wind and photovoltaic power) increases, the conventional grid- following voltage source converter (VSC)-based high voltage direct current (HVDC) transmission system faces the problem of interactive instability with the grid. A novel grid-forming control strategy is proposed to overcome these issues, which adopts the dynamics of a DC capacitor to realize the function of self- synchronization with the grid. Moreover, the per-unit DC voltage can automatically track the grid frequency, acting as a phase-locked loop. Next, the small-signal model of the grid-forming VSC-HVDC system is established, and the stability of the system is analyzed using the eigen-value analysis method and the complex power coefficient method. In addition, the stabilization controller is proposed for the grid-forming (GFM) control structure, which further enhances the grid-forming VSC-HVDC system's stability and helps it operate stably under both stiff and weak grid conditions. Research results show that the VSC-HVDC system under the proposed grid-forming control can work stably in both stiff and weak grids. The grid-forming VSC-HVDC system is robust and can maintain stable operations with a large range variation of the parameters in the current and voltage control loop. Simulations are carried out on the PSCAD/EMTDC platform to verify the proposed grid-forming control strategy. [ABSTRACT FROM AUTHOR]

A cart-pendulum system is a typical underactuated mechanical system with two degrees of freedom and one control input. The fixed-time stabilization control problem for this system is studied in this paper. A fixed-time hierarchical sliding mode control method is developed to solve this problem. Firstly, the system is divided into two subsystems. And two first-level sliding mode surfaces are designed for these two subsystems respectively. Secondly, a second-level sliding mode surface is constructed by linearly combining two first-level sliding mode surfaces. After that, a hierarchical sliding mode controller is designed based on the designed sliding mode surfaces. It enables the state variables of closed-loop control system to be stabilized at the origin within a fixed-time. This means that the settling time can be limited within a range and does not depend on the initial state value of the system. Finally, a numerical example is given to show the effectiveness of the developed control method. [ABSTRACT FROM AUTHOR]

This study presents a simulation-based investigation into the application of a Cascade Proportional-Integral-Derivative (PID) combined with Linear Quadratic Regulator (LQR) control scheme for managing the complexities of a Nonlinear Flexible Inverted Pendulum System (NFIPS). The NFIPS, characterized by nonlinear dynamics and structural flexibility, demands a sophisticated control strategy to achieve stable and precise performance. The proposed Cascade PID-LQR scheme integrates the advantages of PID for addressing nonlinearities and LQR for optimizing linearized dynamics. Through comprehensive simulations, the effectiveness of the proposed control scheme is evaluated, emphasizing its potential in enhancing stability, response speed, and robustness. The study contributes valuable insights into the application of advanced control methodologies in handling nonlinear and flexible systems, paving the way for further exploration and practical implementations in related domains such as robotics and mechatronics.

The monocular camera can both perceive the surroundings and measure the motion states of an unmanned surface vehicle (USV) by imitating the functions of human vision, such that the monocular visual servo is an important means of improving the navigation perception and control autonomy of USVs. Starting from basic principles, the classification of visual servo methods, perspective projection model of the camera and mathematical model of the USV are briefly described so as to provide a fundamental framework for the literature review. According to task complexity, the research progress and challenges of monocular visual servo-based USV control are then summarized in four typical scenarios, namely course control, stabilization control, trajectory tracking control and swarm control. Finally, the future trends of the monocular visual servo-based autonomous control of USVs are systematically summarized.

The cart–inverted pendulum system (CIPS) is a typical example of underactuated mechanical systems. For the CIPS with friction and disturbances, a gain-scheduled model predictive control method is proposed to achieve the upright stabilization objective of the single inverted pendulum (SIP) while controlling the cart to reach a desired new position. To this end, first, a dynamic equation of the CIPS with friction and disturbances is formulated based on the Newton–Euler equation. On the basis of the dynamic equation of the CIPS, its motion characteristics and control process are analyzed. Next, the given dynamic equation of the CIPS is linearized to obtain a series of linearized models at seven different pendulum angles. Then, seven model predictive controllers (MPCs) are designed based on the above-linearized models, respectively. Introducing the idea of the gain-schedule, a gain-scheduled MPC (GSMPC) is designed to switch one of these seven MPCs to realize the control objective of the CIPS, according to the actual pendulum angle of the SIP during the control process. Finally, multi-group simulations that consider the friction and disturbances of the CIPS are implemented to demonstrate the effectiveness of the proposed gain-scheduled model predictive control method. [ABSTRACT FROM AUTHOR]

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This article investigates an adaptive output feedback control problem for a non-holonomic system with integral input-to-state stable inverse dynamics and output constraints. A tan-type barrier Lyapunov function is utilized to handle asymmetric time-varying output constraints, and the full-order observer is constructed to estimate the unmeasurable state. The dynamic uncertainty is eliminated by changing the supply rate of the integral input-to-state stability. It is demonstrated that the closed-loop system is asymptotically stable, and the output does not violate the asymmetric time-varying constraints under this control scheme. A simulation example validates the effectiveness of the proposed controller. [ABSTRACT FROM AUTHOR]

Our study focuses on the analysis of asymptotic stability and the development of asymptotic stabilization control methods for fractional vehicle suspension systems (FVSS). We begin by constructing a mathematical model for FVSS using the state-space equations of Caputo fractional calculus. Initially, we utilize the fractional Routh–Hurwitz criterion to derive the necessary conditions for asymptotic stability and instability in the open-loop system of the fractional vehicle suspension. Subsequently, we propose a novel control strategy for FVSS and establish an associated asymptotic stabilization criterion by combining a new vector Lyapunov function with the M -matrix method. Moreover, we extend the fractional-order vehicle suspension model to include time delay resulting from the interactions between different variables in the real system, thus creating a FVSS with time delay. Based on the vector Lyapunov function, M -matrix measure, and Razumikhin interpretation, we develop a control strategy specifically tailored for FVSSs with time delay. Lastly, we compare two numerical simulations of the FVSS, one with time delay and one without, to demonstrate the accuracy, effectiveness, and applicability of the proposed method presented in our paper. [ABSTRACT FROM AUTHOR]

In this study, we investigated a novel asymptotic stabilization control method for a fractional-order HIV-1 infection model. First, we constructed a mathematical model of the fractional-order HIV-1 infection using the state-space equations of Caputo fractional calculus. Subsequently, a new control strategy was designed for the fractional-order HIV-1 infection model, and the corresponding asymptotic stabilization criterion was proposed by combining a novel vector Lyapunov function with the M-matrix method. Additionally, we incorporated a time delay, which was generated by the interaction between different variables in the actual system, into the fractional-order HIV-1 infection model, forming a system with a time delay. Based on the vector Lyapunov function associated with the M-matrix measure and Razumikhin interpretation, a control strategy was developed for the fractional-order HIV-1 infection model with a time delay. Finally, we show the results of two numerical simulations of the fractional-order HIV-1 infection model, with and without time delay, to illustrate the accuracy, usefulness, and universality of the proposed measure in our paper. [ABSTRACT FROM AUTHOR]

Flying insects exhibit outperforming stability and control via continuous wing flapping even under severe disturbances in various conditions of wind gust and turbulence. While conventional linear proportional derivative (PD)-based controllers are widely employed in insect-inspired flight systems, they usually fail to deal with large perturbation conditions in terms of the 6-DoF nonlinear control strategy. Here we propose a novel wing kinematics-based controller, which is optimized based on deep reinforcement learning (DRL) to stabilize bumblebee hovering under large perturbations. A high-fidelity Open AI Gym environment is established through coupling a CFD data-driven aerodynamic model and a 6-DoF flight dynamic model. The control policy with an action space of 4 is optimized using the off-policy Soft Actor–Critic (SAC) algorithm with automating entropy adjustment, which is verified to be of feasibility and robustness to achieve fast stabilization of the bumblebee hovering flight under full 6-DoF large disturbances. The 6-DoF wing kinematics-based DRL control strategy may provide an efficient autonomous controller design for bioinspired flapping-wing micro air vehicles. [ABSTRACT FROM AUTHOR]

This paper investigates the observer-based adaptive stabilization control problem for a class of time-delay nonlinear systems with unknown control gain using an echo state network (ESN). In order to handle unknown functions, a new recurrent neural network (RNN) approximation method called ESN is utilized. It improves accuracy, reduces computing cost, and is simple to train. To address the issue of unknown control gain, the Nussbaum function is used, and the Lyapunov–Krasovskii functionals are used to address the delay term. The backstepping strategy and command filtering methodology are then used to create an adaptive stabilization controller. All of the closed-loop system's signals are predicted to be confined by the Lyapunov stability theory. Finally, a simulation example is used to demonstrate the effectiveness of the suggested control mechanism. [ABSTRACT FROM AUTHOR]

In this work, an adaptive control strategy is proposed for simultaneous tracking and stabilization of nonholonomic mobile robot with uncertainties. The tracking controller is consisted of two subcontrollers: position controller and direction angle controller. The hyperbolic tangent function is employed to design the position control law, and the adaptive laws of the uncertainties are obtained by Lyapunov direct method, so as to guarantee the global stability of the tracking controller. An adaptive sliding mode controller is designed for control of the direction angle, and the influence of direction angle error on the stability of the position tracking system is considered. By adjusting the control gain coefficients of the two control subsystems, the convergence speed of the direction angle control subsystem is far greater than that of the position control subsystem, so as to assure the asymptotic convergence of the whole robot control system. Furthermore, the stabilization problem is converted into an equivalent tracking problem through the selection of appropriate reference velocities. The asymptotic convergence of pose errors for mobile robot is proved by the Lyapunov stability theory. Simulations and experiments are carried out to verify that the designed control method can simultaneously handle the problems of trajectory tracking and stabilization of mobile robot. [ABSTRACT FROM AUTHOR]

ObjectivesAiming at the accurate posture stabilization problem of an under-actuated unmanned surface vehicle (USV) in GPS-denied environments, a monocular visual servo stabilization control scheme is proposed based on homography.MethodsBy virtue of the homography decomposition technique, posture errors with an unknown scale factor are directly reconstructed from current and desired images, which thoroughly removes the calibration of extrinsic camera parameters and priori information on visual targets; with respect to the under-actuation constraint, a periodic function to persistently excite the yaw angle is incorporated into the continuous time-variant output feedback controller, allowing the USV to be stabilized in the absence of image depth, movement velocities and model parameters.ResultsUnder the framework of the Lyapunov theory, the closed-loop visual servo system of the USV is rigorously proven to be asymptotically stable by Barbalat lemma.ConclusionsBy installing an onboard monocular camera, USV posture errors can be precisely stabilized with the aid of the proposed visual servo strategy, providing significant technique support for practical applications including docking, berthing, dynamic positioning, etc.

This study presents the bifurcation analysis and stabilization via the impulsive control of a fractional-order gene regulatory network with time delay. First, the author chooses the transcription rate k as the bifurcation parameter and obtains the Hopf bifurcation condition by analyzing its characteristic equation. The research shows that Hopf bifurcation occurs when the transcription rate k exceeds a critical value. This bifurcation behavior may destabilize the system. Subsequently, the author designs an impulsive controller to stabilize the system. Finally, simulation examples are used to verify our theory. [ABSTRACT FROM AUTHOR]

The cart–inverted pendulum system (CIPS) is a typical example of underactuated mechanical systems. For the CIPS with friction and disturbances, a gain-scheduled model predictive control method is proposed to achieve the upright stabilization objective of the single inverted pendulum (SIP) while controlling the cart to reach a desired new position. To this end, first, a dynamic equation of the CIPS with friction and disturbances is formulated based on the Newton–Euler equation. On the basis of the dynamic equation of the CIPS, its motion characteristics and control process are analyzed. Next, the given dynamic equation of the CIPS is linearized to obtain a series of linearized models at seven different pendulum angles. Then, seven model predictive controllers (MPCs) are designed based on the above-linearized models, respectively. Introducing the idea of the gain-schedule, a gain-scheduled MPC (GSMPC) is designed to switch one of these seven MPCs to realize the control objective of the CIPS, according to the actual pendulum angle of the SIP during the control process. Finally, multi-group simulations that consider the friction and disturbances of the CIPS are implemented to demonstrate the effectiveness of the proposed gain-scheduled model predictive control method.

To cope with the weak grid stability issue of grid-tied voltage source converters (VSCs), this article proposes an artificial neural network (ANN) based approach for online stabilization control of the grid-tied VSC with the pole-tracking feature. First, an ANN is adopted to establish the mapping between the control parameters and the closed-loop poles of the grid-VSC system, serving as a computationally light model surrogate that is favorable for real-time control applications. Then, an online parameter search algorithm enabling simultaneous tuning of multiple controllers and parameters is developed, by which the system's poles under a new grid condition can be pulled to the reference ones, i.e., achieving the pole-tracking-based stabilization control of this article. Finally, the efficacy of the proposed method along with its stabilization effect is verified by MATLAB simulations and experimental results. [ABSTRACT FROM AUTHOR]

This paper investigates and discusses the interaction stability issues of a wind farm with weak grid connections, where the wind turbines (WTs) are controlled by a new type of converter control strategy referred to as the voltage source (VS) control. The primary intention of the VS control method is to achieve the high-quality inertial response capability of a single WT. However, when it is applied to multiple WTs within a wind farm, its weak-grid performance regarding the stability remains concealed and needs to be clarified. To this end, a frequency domain model of the wind farm under the VS control is first developed. Based on this model and the application of a stability margin quantification index, not only the interactions between the wind farm and the weak grid but also those among WTs will be systematically assessed in this paper. A crucial finding is that the inertial response of VS control has negative impacts on the stability margin of the system, and the dominant instability mode is more related to the interactions among the WTs rather than the typical grid-wind farm interaction. Based on this knowledge, a stabilization control strategy is then proposed, aiming for stability improvements of VS control while fulfilling the demand of inertial responses. Finally, all the results are verified by time-domain simulations in power systems computer aided design/electromagnetic transients including DC(PSCAD/EMTDC). [ABSTRACT FROM AUTHOR]

In this study, we investigated a novel asymptotic stabilization control method for a fractional-order HIV-1 infection model. First, we constructed a mathematical model of the fractional-order HIV-1 infection using the state-space equations of Caputo fractional calculus. Subsequently, a new control strategy was designed for the fractional-order HIV-1 infection model, and the corresponding asymptotic stabilization criterion was proposed by combining a novel vector Lyapunov function with the M-matrix method. Additionally, we incorporated a time delay, which was generated by the interaction between different variables in the actual system, into the fractional-order HIV-1 infection model, forming a system with a time delay. Based on the vector Lyapunov function associated with the M-matrix measure and Razumikhin interpretation, a control strategy was developed for the fractional-order HIV-1 infection model with a time delay. Finally, we show the results of two numerical simulations of the fractional-order HIV-1 infection model, with and without time delay, to illustrate the accuracy, usefulness, and universality of the proposed measure in our paper.

Flying insects exhibit outperforming stability and control via continuous wing flapping even under severe disturbances in various conditions of wind gust and turbulence. While conventional linear proportional derivative (PD)-based controllers are widely employed in insect-inspired flight systems, they usually fail to deal with large perturbation conditions in terms of the 6-DoF nonlinear control strategy. Here we propose a novel wing kinematics-based controller, which is optimized based on deep reinforcement learning (DRL) to stabilize bumblebee hovering under large perturbations. A high-fidelity Open AI Gym environment is established through coupling a CFD data-driven aerodynamic model and a 6-DoF flight dynamic model. The control policy with an action space of 4 is optimized using the off-policy Soft Actor–Critic (SAC) algorithm with automating entropy adjustment, which is verified to be of feasibility and robustness to achieve fast stabilization of the bumblebee hovering flight under full 6-DoF large disturbances. The 6-DoF wing kinematics-based DRL control strategy may provide an efficient autonomous controller design for bioinspired flapping-wing micro air vehicles.

This paper investigates the observer-based adaptive stabilization control problem for a class of time-delay nonlinear systems with unknown control gain using an echo state network (ESN). In order to handle unknown functions, a new recurrent neural network (RNN) approximation method called ESN is utilized. It improves accuracy, reduces computing cost, and is simple to train. To address the issue of unknown control gain, the Nussbaum function is used, and the Lyapunov–Krasovskii functionals are used to address the delay term. The backstepping strategy and command filtering methodology are then used to create an adaptive stabilization controller. All of the closed-loop system’s signals are predicted to be confined by the Lyapunov stability theory. Finally, a simulation example is used to demonstrate the effectiveness of the suggested control mechanism.

Throughout this article, a novel control strategy for fractional-order gene regulation networks (FOGRN) of all categories is designed by using the vector Lyapunov function in combination with the M-matrix measure. Firstly, a series of puzzles surrounding the asymptotic stability of two-dimensional FOGRN are studied, and a new asymptotic stability control strategy is formulated based on the vector Lyapunov function in combination with the M-matrix measure, ensuring that the controlled FOGRN has a strong robust stability. In addition, the corresponding asymptotic stability criterion is deduced. On this basis, the problem of asymptotic stability of a three-dimensional FOGRN is studied. Based on the new method, a stabilization control strategy is also formulated with the corresponding asymptotic stability criterion deduced, ensuring that the controlled FOGRN has a strong robust stability as well. Finally, this novel method's effectiveness and generality are authenticated via simulation experiments. [ABSTRACT FROM AUTHOR]

A spring-coupled three-link horizontal manipulator (STHM) is an underactuated mechanical system that possesses two control inputs and three degrees of freedom (DOF). This paper discusses the stabilization control problem for this multi-DOF underactuated system. By using an energy-absorbing idea, we design two types of virtual friction controllers: PsD controller and PD controller. Additionally, the stability of the control system is analyzed based on Lyapunov theory and LaSalle's invariance principle. The design of the stabilizing controller in this paper makes good use of the physical characteristics of the STHM system. The design process of the whole control system is simple. Numerical examples demonstrate the validity and superiority of our developed control strategy. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper studies the prescribed finite time stabilization control problem of multi-input linear control systems subject to uncertainties and state constraints. Different from finite-time and fixed-time control methods, a novel switching nonsingular coordinate transformation function based on the block decomposition technique is developed, resulting in a solution that the control action is continuous and bounded based on the transformed system. Moreover, the boundedness of closed-loop system states is ensured with prescribed finite time performance. Furthermore, we incorporate the barrier Lyapunov function into the stabilization control design to restrain the amplitudes of system states. Numerical examples are provided to verify the effectiveness of the proposed prescribed finite time control strategy.

Summary: With the rapid development of renewable energy, inverters have been widely used in the distributed power system. For the safety of the distributed power system, the stable operation of the inverter is vital. Hence, this paper detects slow‐scale instability fast‐scale instability and in the double‐loop proportional‐integral (PI) control single‐phase full H‐bridge inverter with inductive impedance load by the discrete‐mapping model firstly. Next, the mechanism of the mechanism slow‐scale and fast‐scale behaviors are explained by state‐space average model and Filippov method, respectively. Then, passive damping method and sine wave compensation techniques are extended to the system to suppress the nonlinear behaviors. Finally, the PLECS simulation and experiments are utilized to verify the correctness of the investigations. [ABSTRACT FROM AUTHOR]

This paper considers the stabilization problem for under-actuated rotary inverted pendulum systems (RotIPS) via a fuzzy-based continuous sliding mode control approach. Various sliding mode control (SMC) methods have been proposed for stabilizing the under-actuated RotIPS. However, there are two main drawbacks of these SMC approaches. First, the existing SMCs have a discontinuous structure; therefore, their control systems suffer from the chattering problem. Second, a complete proof of closed-loop system stability has not been provided. To address these two limitations, we propose a fuzzy-based (continuous) super-twisting stabilization algorithm (FBSTSA) for the under-actuated RotIPS. We first introduce a new sliding surface, which is designed to resolve the under-actuation problem, by combining the fully-actuated (rotary arm) and the under-actuated (pendulum) variables to define one sliding surface. Then, together with the proposed sliding surface, we develop the FBSTSA, where the corresponding control gains are adjusted based on a fuzzy logic scheme. Note that the proposed FBSTSA is continuous owing to the modified super-twisting approach, which can reduce the chattering and enhance the control performance. With the proposed FBSTSA, we show that the sliding variable can reach zero in finite time and then the closed-loop system state converges to zero asymptotically. Various simulation and experimental results are provided to demonstrate the effectiveness of the proposed FBSTSA. In particular, (i) compared with the existing SMC approaches, chattering is alleviated and better stabilization is achieved; and (ii) the robustness of the closed-loop system (with the proposed FBSTSA) is guaranteed under system uncertainties and external disturbances.

Unmanned helicopters are widely used in military and civil fields. One of the most important applications is flying with an underslung load, but the pendulum-like behavior of the load can cause damage or even forced landing to the helicopter. To solve this problem, a control strategy to stabilize the helicopter/load system based on active disturbance rejection control (ADRC) and improved sliding mode control (ISMC) algorithms is proposed in this paper. First, the helicopter/load system is modelled using Newton-Euler equations according to the multi-body dynamics theory. Then a manipulation strategy which can reduce the swing angle of the load and an overall control strategy for the helicopter/load system are presented. Specifically, ADRC is applied to attitude control due to its ability to regard the pendulum-like behavior as the internal uncertainties of the system, meanwhile ISMC to position control. Within ISMC, two sliding surfaces with adjustable weights are constructed by employing the position of the helicopter as well as the swing angle of the load. In addition, a real-time beetle antennae search algorithm is designed to online modify the weights by taking the minimum error at current time as the optimization objective. Besides, the radial basis function neural network is introduced to approach the uncertainty coefficients considering the system's complexity. At last, relevant simulations are carried out and the results indicate that the system is capable of not only controlling the attitude and position of the helicopter precisely but also stabilizing the underslung load rapidly with ADRC and ISMC. [ABSTRACT FROM AUTHOR]

Throughout this article, a novel control strategy for fractional-order gene regulation networks (FOGRN) of all categories is designed by using the vector Lyapunov function in combination with the M-matrix measure. Firstly, a series of puzzles surrounding the asymptotic stability of two-dimensional FOGRN are studied, and a new asymptotic stability control strategy is formulated based on the vector Lyapunov function in combination with the M-matrix measure, ensuring that the controlled FOGRN has a strong robust stability. In addition, the corresponding asymptotic stability criterion is deduced. On this basis, the problem of asymptotic stability of a three-dimensional FOGRN is studied. Based on the new method, a stabilization control strategy is also formulated with the corresponding asymptotic stability criterion deduced, ensuring that the controlled FOGRN has a strong robust stability as well. Finally, this novel method’s effectiveness and generality are authenticated via simulation experiments.

Flying insects perform active flight control with flapping wings by continuously adjusting their wing kinematics in stabilizing the body posture to stay aloft under complex natural environment. While the Proportional Derivative (PD) / Proportional Integral Derivative (PID)-based algorithms have been applied to examine specific single degree of freedom (DoF) and/or 3 DoF flight control associated with insect flights, a full 6 DoF flight control strategy remains yet poorly studied. Here we propose a novel 6 DoF PD controller specified for flight stabilization in flapping flights, in which proportional and derivative gains are optimized to facilitate a fast while precise flight control by combing Laplace transformation and root locus method. The vertical position, yaw, pitch and roll are directly stabilized by tuning the wing kinematics while the forward/backward position and lateral position are indirectly stabilized by controlling the pitch and roll, respectively. Coupled with a recently developed flight dynamic model informed by high-fidelity CFD simulation (Cai et al. 2021), this methodology is proven to be effective as a versatile and efficient tool to achieve fast flight stabilization under both small and large perturbations for bumblebee hovering. The 6 DoF PD flight control strategy proposed may provide a useful bioinspired flight-controller design for flapping-wing micro air vehicles (FWMAVs).

Event-triggered optimal stabilization problem is investigated for macro–micro composite stage system with dynamical perturbation and interconnections. By using a novel cost function and considering the interconnected subsystems as a whole system, stabilization problem of the unknown interconnected system converts to control problem with optimality for the corresponding nominal system. On the side, the update frequency of the controller can effectively be decreased by employing an event-triggered condition. Furthermore, an optimal controller is derived by solving the modified Hamilton–Jacobi–Bellman equation via single critic neural network with asymptotically stable structure. And then the uniformly ultimately bounded stability issue of closed-loop system is analyzed in this article. Finally, the effectiveness of the developed event-triggered stabilization control scheme is verified by using simulation results for macro–micro composite stage system. [ABSTRACT FROM AUTHOR]

In this paper, we present the design for a decentralized control method comprising a series of local state feedback controllers for a class of linear fractional composite systems. In addition, the corresponding asymptotic stabilization criterion is derived. First, we design the local state feedback controllers for each subsystem of the linear fractional composite system. Then, based on the vector Lyapunov function, we combine these local state feedback controllers into a single decentralized controller through which the asymptotic stabilization criterion is proposed for the class of linear fractional composite system. Finally, numerical simulation of a class of linear fractional composite systems is used to verify the accuracy and effectiveness of the decentralized control method. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new class nonlinear hybrid controller (NHC) for swinging-up and stabilizing the (under-actuated) rotary inverted pendulum system. First, the swing-up controller, which drives the pendulum up towards the desired upright position, is designed based on the feedback linearization and energy control methods. Then, the modified super-twisting sliding mode control is proposed based on the new sliding surface to stabilize both the fully-actuated (the rotary arm) and under-actuated (the pendulum) state variables. In the proposed NHC, around the upright position, the stabilization controller is applied, and in different circumstances aside from the upright position, the swing-up controller is used. We show that with the proposed NHC: (i) in the swing-up stage, the pendulum is able to reach the desired upright position; and (ii) in the stabilization stage, the closed-loop rotary inverted pendulum is asymptotically stable. We demonstrate the effectiveness of the proposed NHC through extensive experiments. In particular, (i) the faster swing-up under the similar control effort is obtained, compared with the existing fuzzy logic swing-up controller; (ii) the better stabilization control performance for the convergence of the angular positions of the rotary arm and pendulum is attained and the chattering is alleviated, compared with the existing sliding mode stabilization controllers; (iii) the better stabilization control accuracy with the faster convergence time and lower peak overshoot is accomplished, compared with the existing Fuzzy-LQR controller; and (iv) the good robustness against sudden external disturbances is achieved. [ABSTRACT FROM AUTHOR]

A spring-coupled three-link horizontal manipulator (STHM) is an underactuated mechanical system that possesses two control inputs and three degrees of freedom (DOF). This paper discusses the stabilization control problem for this multi-DOF underactuated system. By using an energy-absorbing idea, we design two types of virtual friction controllers: PsD controller and PD controller. Additionally, the stability of the control system is analyzed based on Lyapunov theory and LaSalle’s invariance principle. The design of the stabilizing controller in this paper makes good use of the physical characteristics of the STHM system. The design process of the whole control system is simple. Numerical examples demonstrate the validity and superiority of our developed control strategy.

This paper addresses a three-dimensional (3D) trajectory tracking problem of underactuated autonomous underwater vehicles (AUVs) subjected to input saturation and external disturbances. The proposed controller can achieve practical convergence of tracking errors for general reference trajectories, including persistently exciting (PE) time varying trajectories and fixed points. At first, a modified error state formulation is introduced to tackle the situation that desired velocities do not satisfy PE condition. Then, on the basis of the backstepping technique and a Nussbaum-type even function, a saturated controller is designed so that the tracking errors can converge to a bounded neighborhood of the origin. The stability analysis based on Lyapunov theory shows that the tracking errors are globally ultimately uniformly bounded. Finally, some simulation results illustrate the effectiveness and robustness of the proposed control strategy. [ABSTRACT FROM AUTHOR]

Rotary Inverted Pendulum (RIP) is an under-actuated mechanical system which is inherently nonlinear and unstable. For decades, it has been widely used as an experimental setup to explain and test different kinds of control algorithms. The main control objectives of RIP are: Swing-up control, stabilization control, switching control and trajectory tracking control. All these control objectives are described in this study. State-of-the art works proposed for each control objective have also been reviewed. These comprise the linear, nonlinear time invariant, self-learning and adaptive nonlinear controllers. Moreover, different kinds of nonlinear dynamic models of the RIP together with the developed linear models in the literature have been analyzed. This is because most of the proposed controllers applied on RIP are found to be model dependent since they are mainly based on integral and/or invariant motion. Other types of RIP are also reported along with their advantages. Future research opportunities and challenges of the previous approaches in this area of research are presented. We believe that expert researchers can use this paper as starting point for further advancement while graduate scholars can use it as an initial point. [ABSTRACT FROM AUTHOR]