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This paper addresses the path-tracking problem for flexible needle control systems using a stochastic linear parameter varying (LPV) and model predictive control (MPC) strategy. Flexible needles operating in dynamic environments with non-uniform tissue density often deviate from ideal assumptions, resulting in non-standard models. The bicycle kinematics model for flexible needle motion control is transformed into an LPV model, improving accuracy and enabling more efficient control. The proposed stochastic LPV MPC approach aims to mitigate uncertainties arising from modelling errors and dynamic environmental factors, ensuring accurate trajectory tracking for the flexible needle. The sample and removal method is utilized to reformulate the probabilistic-constrained optimization problem for implementation. The contributions of this work lie in the application of stochastic LPV MPC to address the trajectory tracking problem in the presence of uncertainties. The simulation results illustrate the superior robustness of the stochastic LPV MPC approach, as evidenced by significantly smaller tracking errors across various scenarios. [ABSTRACT FROM AUTHOR]

This paper presents an innovative formation control method using the leader–follower strategy for a marine vehicle system, addressing the computing power disparity between the leader Unmanned Surface Vehicle (USV) and the follower Autonomous Underwater Helicopters (AUHs). Accounting for model uncertainties and external disturbances affecting the leader USV, we develop a control law for trajectory tracking, which ensures precise parameterized path tracking. A finite-time distributed observer based on the consensus principle is designed for the follower AUHs. The observer estimates the leader's position within communication constraints, ensuring timely and accurate information sharing. To achieve effective tracking and formation of the follower AUHs relative to the leader USV, we propose a formation control method with low complexity and predefined performance constraints. This method introduces a novel prescribed performance function and a Nussbaum function to manage tracking errors and address unknown propeller control directions. Finally, we validate the system's stability using Lyapunov stability theory and demonstrate the method's effectiveness through detailed MATLAB simulations. The results show that our approach significantly improves tracking accuracy and formation stability under various conditions. [ABSTRACT FROM AUTHOR]

Trajectory mapping techniques have widespread applications in diverse fields, including robotics, localization, smart environments, gaming, and tracking systems. However, existing free devices encounter challenges in representing trajectories, thereby limiting the effectiveness of applications such as robotics, localization, and tracking systems. The imprecise mappings generated by these methods lead to suboptimal performance and unreliable results. The proposed approach leverages WiFi sensing through channel state information (CSI), triangulation techniques, and a fine-tuning mechanism to enhance trajectory precision within indoor environment trajectory mapping. The proposed solution employs a domain adapter fine-tuning technique to enable location-independent tracking via CSI, minimizing errors. The use of CSI MIMO signals for trajectory mapping offers enhanced spatial resolution, robust multipath handling, and improved accuracy in tracking movement by leveraging multiple antenna channels and exploiting the rich information embedded in signal reflections and scattering, while triangulation aids in accurately determining the location of objects or targets. Furthermore, incorporating a fine-tuning mechanism refines the generated trajectories. The findings demonstrate substantial enhancements in mapping precision, with an accuracy of 95.5% in tracking 13 paths within the new domain. These results underscore the effectiveness of the proposed approach in overcoming the limitations of existing methods and achieving highly accurate trajectory mapping. [ABSTRACT FROM AUTHOR]

A parametric Adaptive Model Predictive Controller (AMPC) based on Particle Swarm Optimization-Back Propagation (PSO-BP) neural network has been developed in this paper, the primary focus is on improving the trajectory tracking performance of autonomous vehicles under varying road conditions. The PSO-BP neural network is employed for real-time adjustment of the controller's predictive horizon and sampling time. A vehicle dynamics model is established and an improved tracking control algorithm involving road curvature feedforward is proposed. In the design of AMPC, the real-time update of tire lateral stiffness is achieved through the adoption of the Recursive Least Squares (RLS) method, ensuring the precision of trajectory tracking for the vehicle under varying operating conditions. The simulation platform, which combines Carsim and Simulink, was employed for validating the proposed approach. The findings reveal that the proposed controller can promptly adjust the predictive horizon and sampling time according to the vehicle's state. Through the employed estimation strategy, real-time adjustments of tire lateral stiffness are achieved, allowing for dynamic alterations of vehicle speed and front wheel angle in response to road curvature. As a result, this approach significantly enhances control accuracy and lateral steering stability, especially in large curvature conditions. [ABSTRACT FROM AUTHOR]

Linear Quadratic Regulator (LQR) is one of the most prevalent methods used in the control of unmanned aerial vehicles. LQR controllers are commonly employed in the control of both linear and non-linear systems due to their advantages such as easy-to-apply and high-performance structure. However, there is one main difficulty that plays a significant role in the manner of determining the gain for the control signal: choosing appropriate weighting matrices. The selection of these matrices that directly affect the controller performance is generally performed by trial and error, which is laborious and time-consuming. Accordingly, various optimization algorithms have been utilized to determine the weighting matrices of the LQR controller. In this paper, the weighting matrices of the designed LQR controller were obtained using Standard Genetic Algorithm, Differential Evolution, Particle Swarm Optimization, and Grey Wolf Optimization algorithms. The obtained weighting matrices of the LQR controller were tested on an unmanned aerial vehicle simulation, and the performance of optimization algorithms were presented comparatively. [ABSTRACT FROM AUTHOR]

This paper solves a fixed-time trajectory tracking control problem for a marine surface vessel in the presence of model uncertainties, external disturbances, input saturation and prescribed performance constraints. Firstly, a fixed-time disturbance observer (FXDO) is designed, which not only realizes fixed-time stability, but also solves the design method problem in the existing disturbance observer. Secondly, a fixed-time nonsingular terminal sliding mode manifold (FXNTSMM) with simple structures is designed, whereby the designed FXNTSMM reduces the calculation burden of the system. Thirdly, a fixed-time trajectory tracking control scheme in the presence of model uncertainties, external disturbances, input saturation and prescribed performance constraints is proposed based on the FXDO, the FXNTSMM and a prescribed performance function, and the entire constrained closed-loop control system is with fixed-time stability, simplicity and nonsingularity. Finally, several numerical simulation results are presented to demonstrate the effectiveness of the proposed trajectory tracking control scheme. [ABSTRACT FROM AUTHOR]

We propose continuous integral-type sliding mode tracking control (CIT-SMTC) for a class of under-actuated gantry/bridge cranes. Compared to the available sliding-mode-based approaches for crane systems, the notable improvements of the proposed CIT-SMTC include: (i) the assurance of the soft start through the trajectory tracking control mode; (ii) the enhancement of system performance in terms of convergence time and control accuracy; (iii) the continuity of the control action; and (iv) the complete stability analysis of the overall closed-loop system. In the proposed control structure, an integral-type sliding surface is first designed such that during the sliding phase, the stability of the closed-loop system is guaranteed and the control performance of crane systems is enhanced. Then, by employing the introduced integral manifold and the super-twisting-like algorithm, the CIT-SMTC is proposed such that the states are restricted to the sliding surface in finite time and the continuous control signal is imposed. Rigorous analysis is provided to prove the stability of the overall closed-loop system. Finally, experimental results are shown to verify the superiority of the proposed CIT-SMTC. [ABSTRACT FROM AUTHOR]

This article addresses the problem of speed and steering control of unmanned sea surface vehicles operating under unknown ocean environments affected by complex nonlinearities and uncertain hydrodynamic coefficients. An inertial delay control (IDC)-based sliding mode control (SMC) is proposed. The proposed controller is robust against the system's nonlinearities, parametric uncertainties, external disturbances like a strong wind, complex disturbances due to wind-generated and ocean currents, etc. The proposed controller uses IDC to estimate these effects mentioned above, which makes the proposed SMC independent of the bound of uncertainties and disturbances, and provides chatter-free control. The effectiveness of the proposed controller is confirmed by considering the highly nonlinear model of Cypership-II using various performance indices. [ABSTRACT FROM AUTHOR]

A hydrodynamic control model is established in attention to coupling relationships among cable, propellers and robot body of a tethered underwater robot system. In this model the governing equations of umbilical cable and the robot are firstly introduced, then supplementary conditions are coupled into the equations to forming the dynamic mathematical model; finally a hybrid control strategy based on feed-forward negative feedback method for the cable and PID rule for the propellers are integrated in the mathematical model for composing the whole hydrodynamic control model. Both the mathematical model and the control algorithms are proved to be effective and reliable through comparing simulation with the experimental data in existed references. Based on the numerical model constructed in this paper, trajectory tracking of a tethered underwater robot system in different motion combinations are numerically simulated through computational fluid dynamics method. In the numerical simulations, finite difference method is used to solving the kinematic parameters of the mathematical model, while finite volume method is applied on calculating the hydrodynamic forces under a hybrid control manipulations. The robot motion in vertical direction is determined primary by feed-forward negative feedback strategy of adjusting the cable length, while the horizontal movement of the robot is controlled mainly through PID algorithm; The hydrodynamic loading on the robot body are influenced by the flow fields around the robot. Article Highlights: Established the coupling equations with considering dynamic behaviors among propellers, umbilical cable and robot body. Discovered the conversion relation which unrelated to objective factors between the rotating speeds of propellers and the corresponding thrusts. Analyzed the changing rules of hydrodynamic loading on the underwater robot under a hybrid control strategy. [ABSTRACT FROM AUTHOR]

In this article, an updated dynamic model of mobile manipulators is presented, which incorporates the effects of wheel slipping and skidding in the mobile base motion equations. This modified model is then used to design an improved control algorithm. The interaction between the robot's mobile platform and manipulator often leads to inaccuracies when using traditional control methods. To address this, a modified control strategy is proposed. Unlike previous research, which faced difficulties directly measuring wheel slippage and traction forces, this work implements a disturbance observer to estimate these unknown parameters. The control algorithm is then designed using the feedback from the observer estimates. First, kinematic control is used to guide the robot along the desired trajectory. Next, dynamic control augmented with the disturbance observer enables robust tracking. Specifically, a disturbance observer-based sliding mode controller is developed for dynamic control of the system. This is further optimized using the state-dependent Riccati equation method. Lyapunov analysis proves system stability and guarantees disturbance estimation errors converge to zero. Simulations on a model of a mobile manipulator confirm the effectiveness of the proposed method. When combining the disturbance observer and sliding mode control, the consumed torque of the wheels and arms is reduced on average by 0.42 Nm and 0.04 Nm, respectively. Defining a cost function and optimizing the torques with the optimal sliding mode control approach further decreases the required torque to 5.17 Nm compared to the basic SMC, while also reducing tracking error by 4.9 mm. Despite platform slippage, the controller performance keeps end effector errors small and within allowable bounds. Experimental validation on a Scout robot demonstrates the feasibility of implementing this method on physical systems. The robot is able to track desired trajectories with acceptable errors. The tracking error in experiments is approximately 54 mm, compared to 13 mm in simulation. [ABSTRACT FROM AUTHOR]

This research aims to design controllers based on the hedge-algebras (HA) theory to control differential robots that track reference trajectories. First, the HA-based controller (denoted as HA controller) is synthesized by selecting a suitable qualitative control rule base for the investigated model as a rule-based optimization problem. Then, the optimal HA-based controller (denoted as oHA controller) is established based on the problem of simultaneously optimizing the rule base, the reference interval of variables, and the fuzzy parameters of the variables. Optimization problems aim to minimize the distance between the robot and the reference trajectory. The optimization problems in this study use the Balancing composite motion optimization (BCMO) algorithm. A controller based on fuzzy set theory (denoted as FC controller) with the same parameters as the HA controller is also included for comparison. The simulation results show that the HA and oHA controllers demonstrate many advantages over the FC controller regarding reference trajectory tracking ability, calculation time, and control robustness. The main contribution of this work consists of (i) The development of a novel HA, oHA approaches to control a mobile robot to follow reference trajectories accurately; (ii) Providing optimal global-based BCMO in terms of minimal tracking error with computational efficiency; (iii) The investigation of one control rule base for HA and oHA controllers, which is effective for many different reference orbits; (iv) The development of a robust controller that adapts to the robot's geometric parameters changes; (v) The proposed controllers have superior performance results compared to controllers based on fuzzy set theory in terms of position error between the robot and the reference trajectory, control action calculation time, and robust ability to change robot parameters. [ABSTRACT FROM AUTHOR]

A tracking control algorithm for autonomous vehicles using state expansion is proposed. This algorithm, based on a three degrees of freedom (3-DOF) vehicle lateral dynamics model, extends the state variables of the traditional model predictive control (MPC) algorithm to an input-output dual feedback form based on the state expansion method. A dual feedback model predictive control (DF-MPC) algorithm was constructed. Based on this, taking the current speed and the curvature of the reference trajectory as the system input, and using the fuzzy control algorithm to dynamically adjust the prediction range of the DF-MPC trajectory tracking controller in real time, a variable predictive horizon double feedback model predictive control (VDF-MPC) trajectory tracking control method for autonomous vehicle was established. Through MATLAB/Simulink-CarSim joint simulation, the reliability of the established VDF-MPC trajectory tracking control method was verified. [ABSTRACT FROM AUTHOR]

As a bridge between planners and actuators, trajectory tracking is a critical and essential part of robot navigation, autonomous driving and some other fields. While traditional trajectory tracking methods generally have disadvantages such as poor tracking accuracy, high modeling requirements, and heavy computational load. This paper proposes an end-to-end trajectory tracking method based on reinforcement learning, and an information encoding network and a reinforcement learning policy network are constructed. A multi-task dense reward function for trajectory tracking is designed. For efficient encoding of local trajectory information, a self-attentive mechanism is developed. A virtual simulation environment is constructed for model training by modeling the trajectory tracking task. Comparing with model predictive control and pure pursuit in the tracking experiments of several reference trajectories, the obtained results show that the proposed method has significant advantages in terms of lateral tracking. [ABSTRACT FROM AUTHOR]

A bioinspired approach to immune systems with a novel set of fuzzy rules is proposed in this paper to provide adaptability to sliding mode control. As multi-robotic systems have attracted the attention of researchers due to their variety of applications, as well as relevant control issues in practical demands, trajectory tracking and its extension to formation control are addressed. Despite its robustness, some disadvantages of first-order sliding mode control must be addressed, such as chattering and the need for a priori knowledge of the bounds of uncertainties and disturbances. Considering that an artificial immune system is a bioinspired approach provided with adaptive regulation mechanisms, this conception motivates the study and investigation of an adaptive strategy inspired by immunity. The leader–follower framework is used, and the nonholonomic differential-drive wheeled mobile robot model is considered. A fuzzy system based on the effect of the immunological reaction is designed, and an adaptation law is proposed to adjust the gain online. A fuzzy boundary layer method is also employed in replacement for the discontinuous control signal. Lyapunov's theorem proves the stability. The simulation and experimental results show the effectiveness of the proposed control strategy, which is also compared with another control scheme of recent research. [ABSTRACT FROM AUTHOR]

The bridge crane, as an efficient transportation tool, isss widely used in various fields. Currently, research on bridge cranes mainly focuses on the single-pendulum domain. However, in the actual transportation process, the mass of the hook cannot be ignored, and it can cause relative oscillations of the crane, similar to a double-pendulum model. Therefore, many experts and scholars have researched double-pendulum bridge cranes. In the operation of a double-pendulum bridge crane with load hoisting/lowering, uncertainties such as frictional resistance and air resistance cannot be accurately fed back to the controller input, posing a significant challenge in designing anti-swing control strategies. In this paper, an adaptive coupled tracking anti-swing control strategy is proposed to address the above issues. Specifically, based on the principle of energy dissipation, more system parameter information is incorporated into the design of coupled trajectories. By combining the S-shaped trolley motion reference trajectory, an error-tracking trajectory for the trolley is designed. Considering the uncertainties of air resistance and frictional resistance, an adaptive rate is used to estimate the resistance coefficients online and feedback to the system input, ultimately designing an adaptive trajectory tracking coupled controller. The asymptotic stability of the equilibrium point of the closed-loop system is proven using the Lyapunov method and Barbalat lemma. Extensive experimental simulations are conducted to demonstrate the good control performance of the proposed control strategy. [ABSTRACT FROM AUTHOR]

This paper develops a novel hierarchical control strategy for improving the trajectory tracking capability of aerial robots under parameter uncertainties. The hierarchical control strategy is composed of an adaptive sliding mode controller and a model-free iterative sliding mode controller (MFISMC). A position controller is designed based on adaptive sliding mode control (SMC) to safely drive the aerial robot and ensure fast state convergence under external disturbances. Additionally, the MFISMC acts as an attitude controller to estimate the unmodeled dynamics without detailed knowledge of aerial robots. Then, the adaption laws are derived with the Lyapunov theory to guarantee the asymptotic tracking of the system state. Finally, to demonstrate the performance and robustness of the proposed control strategy, numerical simulations are carried out, which are also compared with other conventional strategies, such as proportional-integralderivative (PID), backstepping (BS), and SMC. The simulation results indicate that the proposed hierarchical control strategy can fulfill zero steady-state error and achieve faster convergence compared with conventional strategies. [ABSTRACT FROM AUTHOR]

Trajectory tracking is a key technology for controlling the autonomous vehicles effectively and stably to track the reference trajectory. How to handle the various constraints in trajectory tracking is very challenging. The recently proposed generalized exterior point method (GEP) shows high computational efficiency and closed-loop performance in solving the constrained trajectory tracking problem. However, the neural networks used in the GEP may suffer from the ill-conditioning issue during model training, which result in a slow or even non-converging training convergence process and the control output of the policy network being suboptimal or even severely constraint-violating. To effectively deal with the large-scale nonlinear state-wise constraints and avoid the ill-conditioning issue, we propose a model-based reinforcement learning (RL) method called the actor-critic objective penalty function method (ACOPFM) for trajectory tracking in autonomous driving. We adopt an integrated decision and control (IDC)-based planning and control scheme to transform the trajectory tracking problem into MPC-based nonlinear programming problems and embed the objective penalty function method into an actor-critic solution framework. The nonlinear programming problem is transformed into an unconstrained optimization problem and employed as a loss function for model updating of the policy network, and the ill-conditioning issue is avoided by alternately performing gradient descent and adaptively adjusting the penalty parameter. The convergence of ACOPFM is proved. The simulation results demonstrate that the ACOPFM converges to the optimal control strategy fast and steadily, and perform well under the multi-lane test scenario. [ABSTRACT FROM AUTHOR]

Flexible manipulators offer several advantages over rigid manipulators, including light-in-weight, high payload-to-weight ratio, lower power consumption, and the ability to operate at high speeds. However, these manipulators are susceptible to structural vibrations, which can be effectively suppressed by implementing an appropriate controller. The paper presents a performance evaluation of such a controller for tracking a complex trajectory that combines curved and linear paths, in contrast to existing literature-focused on pure linear or circular trajectories without considering any payload. Before controlling, the dynamics of a two-flexible manipulator system are modeled using a hybrid Euler-Lagrangian formulation and DeNOC matrices, with validation. After that, to achieve precise trajectory tracking while suppressing vibrations, a hybrid controller is designed, integrating command shaping techniques with a proportional-derivative (PD) feedback controller. The results demonstrate the effectiveness of command shaping and its comparison to unshaped input commands. The controller's performance is evaluated using a semicircular trajectory within a 3-second timeframe, considering both payload and non-payload cases. The proposed control scheme effectively suppresses vibrations in both payload scenarios. Detailed analysis of tip deflections for both links with shaped and unshaped input commands is provided, along with the quantification of vibration suppression along the trajectory. The impact of different configurations and payloads on the natural frequency during trajectory tracking is presented. The study also shows the tracking of B-Splines trajectories, highlighting the requirement of higher gains for such trajectories and evaluating the effect of link flexibility by tracking error analysis. Vibration suppression is achieved by implementing the controller while tracking such trajectories. [ABSTRACT FROM AUTHOR]

At present, the splicing of steel arches for open-type TBM suffers from the problems of labor-intensive, time-consuming, low efficiency and greater potencial risk to workers. Rock-fall and collapse caused by untimely support is still one of the main construction accidents. In this paper, a novel steel arch splicing manipulator is developed for unmanned and automated steel arch splicing, and a backstepping method based cascade control strategy is proposed to improve the trajectory tracking control performance. Firstly, the inner-loop controller is designed to compensate the flow coupling between each joint-driven hydraulic cylinder based on dynamic analysis and feedback linearization. Secondly, the adaptive robust controller is adopted for outer-loop controller design to deal with parametric uncertainties and external disturbances. Finally, the system stability is proved by Lyapunov function, then comparative experiments are conducted to verify the effectiveness and superiority of the proposed control scheme. It can be concluded that the proposed controller has a better trajectory tracking control performance, while the control input is much smoother than that of traditional PID controller. [ABSTRACT FROM AUTHOR]

This paper investigates the high-precision sliding mode based tracking control of robot manipulators with uncertain dynamics and external disturbances. Different from most currently used fast nonsingular terminal sliding mode surfaces (FNTSMs) which use linear sliding mode (LSM) to avoid singularity, a new recursive terminal sliding mode surface (RTSM) is firstly constructed with a recursive structure to avoid the singularity problem in this paper. The RTSM can improve convergence precision and speed near the equilibrium point compared with FNTSMs. Then a sliding mode based controller has been designed to stabilize the closed-loop system. To cope with model uncertainties, frictions and external disturbances, a novel adaptive sliding mode disturbance observer (ASMDO) has been constructed, which can estimate lumped uncertainties and feed them to the controller to achieve disturbance-rejection control. Compared with traditional disturbance observer with asymptotic stability, the observation error of ASMDO can be driven into a sufficiently small set centered on zero within a predefined fixed time, which means fast observation speed and high observation accuracy. The upper bounds of uncertainties and their derivatives are not needed in observer design. Abundant simulations and experiments also verified the effectiveness and superior properties of the proposed scheme. [ABSTRACT FROM AUTHOR]

In this work, a robust trajectory tracking control strategy is developed for the quadrotor unmanned aerial vehicle (QUAV) with unknown external disturbance. The nonlinear dynamic model of QUAV is established. Different from the traditional disturbance observer and adaptive estimation method, this paper adopts the interval observer technique to approximate the upper and lower bounds of the unknown disturbance. In this way, the proposed interval observer can cover its all possible values and show its scope in real time, especially before the disturbance error estimation converges. Combining with the backstepping approach, a robust tracking control approach is proposed to ensure that the desired trajectory can be successfully followed. Finally, comparative numerical simulation results are provided to evaluate the presented robust tracking controller. [ABSTRACT FROM AUTHOR]

Accuracy and efficiency are two important performances of model predictive control in trajectory tracking and they are seriously affected by the control parameters of model predictive control. To make the model predictive control with high accuracy and efficiency simultaneous, the paper proposed a strategy to tune the control parameters for model predictive control. The proposed strategy converts the tuning problem to a multi-objective optimization problem and employs non-dominated sorting genetic algorithm (NSGA-II) to solve it. The proposed strategy is employed to tune the control parameters for a classical model predictive control in a typical trajectory tracking condition. The simulation results show that the comprehensive performances of model predictive controller tuned by the proposed method are better than other tuning methods. The proposed tuning strategy is validated and it can be applied to tune the control parameters for model predictive control in trajectory tracking. [ABSTRACT FROM AUTHOR]

Automation of berthing maneuvers in shipping is a pressing issue as the berthing maneuver is one of the most stressful tasks seafarers undertake. Berthing control problems are often tackled by tracking a predefined trajectory or path. Maintaining a tracking error of zero under an uncertain environment is impossible; the tracking controller is nonetheless required to bring vessels close to desired berths. The tracking controller must prioritize the avoidance of tracking errors that may cause collisions with obstacles. This paper proposes a training method based on reinforcement learning for a trajectory tracking controller that reduces the probability of collisions with static obstacles. Via numerical simulations, we show that the proposed method reduces the probability of collisions during berthing maneuvers. Furthermore, this paper shows the tracking performance in a model experiment. [ABSTRACT FROM AUTHOR]

An active disturbance rejection controller for a 2-DOF underwater manipulator is proposed in this study. The manipulator system has the characteristics of non-linearity, strong coupling and uncertainties within its model. Its operation is often disturbed by unknown water currents, so the anti-disturbance control of underwater manipulators has always been a challenge. The proposed controller in this study basically does not rely on the precise mathematical model of the object, and the model even can be decoupled. This method can eliminate the influence of model errors, time-varying parameters and external interference on the control effect. First, the entire manipulator treats different joints as several subsystems. For each joint subsystem, hydrodynamic forces, coupling terms between joints and unknown environmental disturbances are regarded as total disturbances. Subsequently, an extended state observer was designed to estimate and compensate for the total interference. In order to improve the disturbance observation effect of the extended state observer, the inertia matrix of the system is used to decouple the static part. Finally, the effectiveness of the proposed method is verified by both simulation and experiments. From the comparisons, it is confirmed that the quality of our controller in the presence of a certain inertial matrix error is better than traditional PD and continuous sliding mode control in terms of accuracy, dynamic characteristics as well as robustness. [ABSTRACT FROM AUTHOR]

The design of a simple and adaptive flight controller is a real challenge in aerial robotics. A simple flight controller often generates a poor flight tracking performance. Furthermore, adaptive algorithms might be costly in time and resources or deep learning based methods may cause instability problems, for instance in presence of disturbances. In this paper, we propose an event-based neural learning control strategy that combines the use of a standard cascaded flight controller enhanced by a deep neural network that learns the disturbances in order to improve the tracking performance. The strategy relies on two events: one allowing the improvement of tracking errors and the second to ensure closed-loop system stability. After a validation of the proposed strategy in a ROS/Gazebo simulation environment, its effectiveness is confirmed in real experiments in the presence of wind disturbance. [ABSTRACT FROM AUTHOR]

Zebrafish have become a widely accepted model organism for biomedical research due to their strong cortisol stress response, behavioral strain differences, and sensitivity to both drug treatments and predators. However, experimental zebrafish studies generate substantial data that must be analyzed through objective, accurate, and repeatable analysis methods. Recently, advancements in artificial intelligence (AI) have enabled automated tracking, image recognition, and data analysis, leading to more efficient and insightful investigations. In this review, we examine key AI applications in zebrafish research, including behavior analysis, genomics, and neuroscience. With the development of deep learning technology, AI algorithms have been used to precisely analyze and identify images of zebrafish, enabling automated testing and analysis. By applying AI algorithms in genomics research, researchers have elucidated the relationship between genes and biology, providing a better basis for the development of disease treatments and gene therapies. Additionally, the development of more effective neuroscience tools could help researchers better understand the complex neural networks in the zebrafish brain. In the future, further advancements in AI technology are expected to enable more extensive and in-depth medical research applications in zebrafish, improving our understanding of this important animal model. This review highlights the potential of AI technology in achieving the full potential of zebrafish research by enabling researchers to efficiently track, process, and visualize the outcomes of their experiments. [ABSTRACT FROM AUTHOR]

This study presents a cutting-edge approach to design iterative learning control (ILC) in micro-robotic deposition systems, utilizing nonlinear time-frequency analysis through adaptive Fourier decomposition (AFD). While ILC has demonstrated its effectiveness in achieving precise trajectory tracking, achieving a balance between robustness and convergence can be challenging. To address this challenge, we introduce a novel nonlinear time-frequency ILC design from a signal processing perspective, which exploits an advanced version of Fourier decomposition called AFD. By employing adaptive basis functions, AFD enables fast energy convergence during the control process. To reduce noise amplification and system delay, we propose a phase-lead ILC algorithm with zero amplitude attenuation. Additionally, we introduce a tunable bandwidth L-Q filter to achieve an optimal trade-off between robustness and convergence. The filter's bandwidth is adaptively adjusted based on the frequency content of the system, with a narrower bandwidth for low-frequency signals to accelerate convergence and a wider bandwidth for high-frequency signals to enhance robustness. Simulation results demonstrate the exceptional performance of the proposed ILC design in a micro-robotic deposition system. [ABSTRACT FROM AUTHOR]

This paper deals with a novel direct state-dependent Riccati equation (SDRE) controller designed for trajectory tracking of underactuated autonomous underwater vehicles (AUVs) in the presence of parameter perturbation. Despite the traditional SDRE regulator control, the proposed closed-loop SDRE controller design chiefly consists of two parts. First, by selecting a virtual reference point in front of the AUV system as the tracking output, the error variable control model in the earth-fixed reference frame is described. Second, the position errors are driven to the origin by introducing an integral model of first-order fed by the tracking error. The main advantage of the proposed control scheme is that the controller has a unified structure. Moreover, the algorithm is able to provide robustness with parameter perturbation because of its intrinsic robustness capability. Within the SDRE framework, the asymptotic stability of the closed-loop tracking system is also guaranteed. The robustness and effectiveness of the proposed methodology are verified by performing simulation experiments on an underactuated AUV. [ABSTRACT FROM AUTHOR]

In this paper, an intelligent nonholonomic wheeled mobile robot (WMR) for real-time trajectory tracking and dynamic obstacle avoidance is developed. The tracking controller of the WMR is designed based on the Takagi–Sugeno (T–S) fuzzy model. A handheld device adopted as the tracking object of the WMR is developed to measure the distance between the WMR and tracking object. The linear and angular velocities of the tracking object are incorporated into the dynamic model of the WMR; subsequently, the state equation of the attitude error of the WMR can be obtained. For obstacle avoidance, the deep image algorithm is utilized to estimate the distance between the WMR and obstacle. Next, the fixed-point tracking algorithm is used for object avoidance in indoor and outdoor environments. The simulation results reveal that the attitude error between the WMR and tracking object for dynamic tracking can converge to zero after a few seconds. Experimental results are provided to demonstrate the effectiveness and feasibility of the designed WMR and handheld device. [ABSTRACT FROM AUTHOR]

This paper investigates the trajectory tracking control problem for the autonomous surface vehicles with unknown dynamics and environmental disturbances. A novel adaptive fuzzy constrained control approach is put forward to deal with this problem. Instead of the exponentially decaying functions, a new-type performance envelop is designed to offer a way to explicitly and freely preassign the settling time and the accuracy of trajectory tracking. Moreover, in lieu of involving the approach angle or azimuth angle, a new idea for tackling underactuation is proposed that yields a singularity-free continuous control solution. The fuzzy logic systems are employed to address the unknown and lumped nonlinearities in the closed-loop system. The resulting control system possesses inherent robustness to the external disturbances and the second derivative of the reference trajectory, thus without the need for employing disturbance observers or assuming the available derivative information. It is demonstrated by the constraint analysis based on dialectic by contradiction that the developed control achieves arbitrarily fast and accurate trajectory tracking for the vehicle. Finally, the effectiveness and superiority of our approach are verified by a comparative simulation. [ABSTRACT FROM AUTHOR]

Efficient trajectory tracking approaches can enable autonomous vehicles not only to get a smooth trajectory but to achieve a lower energy dissipation. Since vehicle model plays an important role in trajectory tracking, this paper investigates and compares the performance of two classical vehicle models for trajectory tracking of autonomous vehicles using model predictive control (MPC). Firstly, a two-degree-of-freedom kinematic model and a three-degree-of-freedom yaw dynamic model are established for autonomous vehicles. Meanwhile, in order to carry out tracking control more effectively and smoothly, the tire slip angle has been taken into account by the dynamic model. Then, we design two MPC controllers for trajectory tracking, which are based on the kinematic model and the dynamic model, respectively. The performances of two MPC controllers are evaluated and compared on the Carsim/Matlab joint simulation platform. Experimental results demonstrated that, under low speed working conditions, both two MPC controllers can follow the reference trajectory with high accuracy and stability. However, under high speed working conditions, the tracking error of the kinematic model is too large to be used in the real trajectory tracking problem. On the contrary, the controller based on the dynamic model still performs a good tracking effect. In addition, this study offers a guidance on how to select a suitable vehicle model for autonomous vehicles under different speed working conditions. [ABSTRACT FROM AUTHOR]

Given limited terrain adaptability, most existing multirobot cooperative transportation systems (MRCTSs) mainly work on flat pavements, restricting their outdoor applications. The connectors’ finite deformation capability and the control strategies’ limitations are primarily responsible for this phenomenon. This study proposes a novel MRCTS based on tracked mobile robots (TMRs) to improve terrain adaptability and expand the application scenarios of MRCTSs. In structure design, we develop a novel 6-degree-of-freedom passive adaptive connector to link multiple TMRs and the transported object (the communal payload). In addition, the connector is set with sensors to measure the position and orientation of the robot with respect to the object for feedback control. In the control strategy, we present a virtual leader–physical follower collaborative paradigm. The leader robot is imaginary to describe the movement of the entire system and manage the follower robots. All the TMRs in the system act as follower robots to transport the object cooperatively. Having divided the whole control structure into the leader robot level and the follower robot level, we convert the motion control of the two kinds of robots to trajectory tracking control problems and propose a novel double closed-loop kinematics control framework. Furthermore, a control law satisfying saturation constraints is derived to ensure transportation stability. An adaptive control algorithm processes the wheelbase uncertainty of the TMR. Finally, we develop a prototype of the TMR-based MRCTS for experiments. In the trajectory tracking experiment, the developed MRCTS with the proposed control scheme can converge to the reference trajectory in the presence of initial tracking errors in a finite time. In the outdoor experiment, the proposed MRCTS consisting of four TMRs can successfully transport a payload weighing 60 kg on an uneven road with the single TMR’s maximum load limited to 15 kg. The experimental results demonstrate the effectiveness of the structural design and control strategies of the TMR-based MRCTS. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the trajectory tracking control problem of marine surface vessels (MSVs) subject to time-varying output constraints, unknown external disturbances, and model uncertainties. To this end, a novel log-type universal barrier Lyapunov function (BLF) with prescribed performance is proposed to ensure the predefined dynamic performance of output tracking errors and good tracking accuracy. Then, a neural network considering approximation errors is designed to handle complex uncertainties. Furthermore, a relative threshold event-triggered mechanism is introduced in the controller design to reduce the actuator execution rate. With the aid of the BLF and the event-triggered mechanism in combination with the adaptive neural network technique and backstepping design tool, a novel adaptive neural network event-triggered trajectory tracking control scheme is presented. A rigorous stability analysis verifies that all signals in the closed-loop system are semi-globally uniformly ultimately bounded, and the prescribed time-varying constraints on the tracking errors would not be violated. At the same time, the Zeno behavior can be avoided. The numerical comparative simulation experiments validate the effectiveness and superiority of the proposed trajectory tracking control scheme. [ABSTRACT FROM AUTHOR]

In this work, we pay attention to investigating fixed-time trajectory tracking with prescribed performance for a multi-degree-of-freedom manipulator system subjected to unknown dynamics and input saturation. The radial basis function neural network (RBFNN) is applied to online compensate for the unknown dynamics of the system. In order to guarantee the transient and steady-state performance of the trajectory tracking control, a prescribed performance function (PPF) is used to transform the tracking error. Based on the transformed error, a fixed-time auxiliary system is proposed to compensate for the input saturation impact. Using the compensation error, a non-singular terminal sliding surface is designed, and the corresponding fixed-time control scheme is also proposed. By Lyapunov theorem, it is proved that the reaching phase of the sliding manifold can be completed in finite time, and the stability of the closed-loop system is analyzed. Experimental results verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

When the equipment is working, it is very important to avoid the occurrence of malignant accidents by providing a highly reliable safety protection means. In this paper, for multiplayer mixed zero-sum games, an optimal safety tracking control scheme based on adaptive dynamic programming (ADP) is proposed, and a control barrier function (CBF) is introduced into the value function of the system to ensure that the system operates within its safe region. Firstly, through system transformation, the original tracking problem is transformed into a state tracking error problem. Secondly, an augmented Hamilton-Jacobi-Bellman (HJB) equation is derived from the improved augmented error system and the value function. Different from traditional methods, this paper uses a single critic neural network (NN) instead of the actor-critic NN to approximate the Nash equilibrium solution of the system, and introduces a concurrent learning technique that can relax the traditional continuous excitation condition into a simplified condition of recording data. Then, according to the Lyapunov theory, the stability of the system is analyzed in detail. Finally, two simulation examples are used to verify the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

Remotely operated underwater vehicles are usually equipped with four horizontal thrusters that form an X-shaped actuation configuration. Yet, thruster's inherent dead-zone may possibly result in strong chatter of moment inputs and motion tracking of underwater vehicles. This paper proposes a two-layer cascade tracking controller together with a dead-zone compensator, in order to achieve simplified and effective surge-heading control of underwater vehicles equipped with an X-shaped horizontal actuation configuration. For the sake of brevity, the surge and heading dynamics are firstly unified as a second-order dynamic system where the known and unknown parts are separated, respectively. Based on this model, a feedback linearization control law with a combined error measure is designed in the first-layer cascade system for the simplified dynamics tracking. Then, a reduced-order extended state observer without using any priori knowledge of uncertainties is utilized in the second-layer cascade system to estimate the complex uncertainty of the dynamics. It is noted that this two-layer tracking controller has only two gains to be adjusted, ensuring a simple calculation and microprogramming. Subsequently, a dedicated dead-zone compensator is proposed for the X-shaped actuation configuration and the input-to-state stability of the whole tracking system is analyzed. Finally, comparative numerical cases are provided to demonstrate the adaptivity and robustness of the designed surge-heading tracking controller, i.e., up to 56% reduction of the maximum surge tracking error owing to this dead-zone compensator and less than 0.03 ∘ of the heading steady state error against different initial states. [ABSTRACT FROM AUTHOR]

In order to overcome the influence of model uncertainty and external disturbance on the trajectory tracking accuracy of four-wheel omnidirectional mobile robot (FM-OMR), a new adaptive trajectory tracking control scheme based on interval type 2 fuzzy neural network approximator (IT2FNNA) is proposed in this paper. Based on the kinematics and dynamics model of FM-OMR, a dual loop trajectory controller is constructed. To improve the adaptive ability of IT2FNNA, an adaptive adjustment method for the network output is proposed. In addition, to improve the approximation accuracy of the IT2FNNA, a bias strategy is proposed based on the FM-OMR model. Finally, the circle track and "8" track are simulated and tested. The simulation and experimental results show that the bias strategy and adaptive factor really improve the approximation ability and accuracy of IT2FNNA, and the proposed adaptive control scheme has smaller fluctuations, higher accuracy and better steady-state performance. [ABSTRACT FROM AUTHOR]

This paper introduces a frequency response based extremum seeking controller (FRB-ESC) that utilizes the natural frequencies and antiresonances of the system in the design of the FRB-ESC. Starting from a natural frequency and transitioning in a continuous way onto the antiresonance of the system allows the minimization of vibrations at the free end. Designing control schemes for flexible-link manipulators is rather complex due to the system distributed nature that is modeled by infinite degrees of freedom, where for control purposes there is only a finite number of sensors and actuators, with a finite bandwidth. The control objective consists on simultaneous trajectory tracking and vibration minimization at the free end of the flexible manipulator employing a single actuator, an optical encoder at the hub of the flexible manipulator and an accelerometer at the free end. Unlike conventional control strategies that add damping to the system, the FRB-ESC minimizes the vibration at the free end. To illustrate the capabilities of the control to minimize vibrations in the most extreme case the reference trajectory is a sinusoidal signal whose frequency is equal to the first mode of vibration of the system. The FRB-ESC achieves attenuation of the free end vibrations and decreases the angular error in the transient state with the use of a single actuator at the hub of the system. The FRB-ESC results are then compared to a PID and a positive position feedback (PPF) controllers achieving up to 42% reduction of the second derivative of the elastic deflection. [ABSTRACT FROM AUTHOR]

In this work, the problem of low-thrust transfer between planar multi-revolution orbits at libration points near the secondary body in the Earth–Moon elliptic restricted three-body problem (ERTBP) is studied. Due to the presence of lunar orbital eccentricity, the ERTBP is closer to the real system than the circular restricted three-body problem (CRTBP). The stable and unstable invariant manifolds, associated with the libration point orbits in the CRTBP are used for the transfer trajectory planning. Different classes of heteroclinic connections are identified by the Poincaré section technique, which are used as initial trials for the calculation of energy-optimal low-thrust transfers. The trajectories are then switched to the ERTBP using continuous algorithms. We propose a computational strategy to match the non-autonomous dynamics of the ERTBP by coordinating the endpoints with the flight times of the transfer trajectories. Finally, a model predictive controller is designed for trajectory tracking in the real ephemeris that takes into account the actual lunar orbital eccentricity. The studies presented in this paper are more closely related to the requirements of practical lunar mission designs. [ABSTRACT FROM AUTHOR]

The main challenge for the adoption of autonomous driving is to ensure an adequate level of safety. Considering the almost infinite variability of possible scenarios that autonomous vehicles would have to face, the use of autonomous driving simulators is becoming of utmost importance. Simulation suites allow the used of automated validation techniques in a wide variety of scenarios, and enable the development of closed-loop validation methods, such as machine learning and reinforcement learning approaches. However, simulation tools suffer from a standing flaw in that there is a noticeable gap between the simulation conditions and real-world scenarios. Although the use of simulators powers most of the research around autonomous driving, and is generally used within all domains it is divided into, there is an inherent source of error given the stochastic nature of activities performed in real world, which are unreplicable in computer environments. This paper proposes a new approach to assess the real-to-sim gap for path tracking systems. The aim is to narrow down the sources of error between simulation results and real-world conditions, and to evaluate the performance of the simulation suite in the design process by employing the information extracted from gap analysis, which adds a new dimension of development against other approaches for autonomous driving. A real-time model predictive controller (MPC) based on adaptive potential fields was developed and validated using the CARLA simulator. Both the path planning and vehicle control systems where tested in real traffic conditions. The error between the simulator and the real data acquisition was evaluated using the Pearson correlation coefficient (PCC) and the max normalized cross-correlation (MNCC). The controller was further evaluated on a process of sim-to-real transfer, and was finally tested both in simulation and real traffic conditions. A comparison was performed against an optimal-control ILQR-based model predictive controller was carried out to further showcase the validity of this approach. [ABSTRACT FROM AUTHOR]

Due to their inherent deformability, controlling continuum robots (CRs) comes with many challenges, including strong nonlinearity, uncertainty, and disturbance. In this paper, a planar CR is modeled as a general multi-input multi-output (MIMO) system with internal uncertainty and external disturbance. To improve the trajectory tracking performance, an active disturbance rejection control (ADRC)-based control strategy for the CR is proposed. The proposed method can regard the internal uncertainty and external disturbance of the CR as an overall disturbance. In the ADRC framework, a linear extended state observer (ESO) is designed to timely estimate the overall disturbance, and a closed-loop control with disturbance compensation and feedback control law is implemented. The convergence of the designed ESO and the stability of the ADRC-based controller are studied. Simulations and experiments are performed to verify the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

With the in-depth development of high-tech industries, especially in the fields of production, manufacturing, aviation, and medical care, most of the work needs to be accomplished with the help of machines. As a high-tech product, a robotic arm plays an irreplaceable role in high-risk and high-precision engineering, such as arc welding, spraying, and assembly. At the same time, with the increasing requirements of scientific and technological production processes, robotic arm tasks are becoming increasingly complicated. Robotic arm trajectory tracking control in industry also has increasingly higher standards. Furthermore, external interference sources invariably affect the robotic arm control system when it is in operation. Therefore, existing manipulator control systems can no longer meet the requirements of industrial production. This paper aims to realize the tracking control of the trajectory of a robotic arm through a neural network algorithm. This research offers an adaptive neural network control method to solve the manipulator trajectory tracking control problem. To increase the control effect and overall performance of the manipulator, a neural network is employed to address the uncertainty in the control system as well as the interference of external elements. Experiments reveal that a neural network-based manipulator trajectory control and tracking system can effectively regulate the manipulator's operation and improve its overall performance. [ABSTRACT FROM AUTHOR]

This paper develops a novel control method for solving the challenging problem of simultaneous collision avoidance and target tracking for an unmanned ground vehicle with velocity and heading rate constraints. The problem is formulated as an optimization problem where the trajectory tracking is treated as a soft constraint via control Lyapunov functions and obstacles avoidance is treated as hard constraint via control barrier functions. Two constraints are naturally unified in the context of quadratic programs which ensure that the hard constraint, soft constraint, velocity and heading rate constraints are satisfied simultaneously. Under the proposed strategy, an unmanned ground vehicle can avoid the stationary and moving obstacles while tracking the desired trajectory precisely and smoothly. Finally, three representative simulations are provided to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, a novel global sliding mode controller (GSMC) with a comprehensive observer is used to solve the problems of time-varying coefficients, modeling errors and disturbance with unknown upper bound in the trajectory tracking control of the air-cushion vehicle (ACV). Considering the multiple resistance coefficients that change with the motion state in the ACV's dynamic model, a comprehensive observer is used to estimate and compensate for the model uncertainty in real time. An adaptive disturbance estimation law without disturbance upper bound is given to compensate for the total disturbance. On the basis of real-time compensation of model uncertainty by adaptive estimation law, a new GSMC with hyperbolic tangent function is proposed to track the ACV's trajectory. The interconnected system of observer and trajectory tracking controller is shown to have enhanced robustness. Finally, simulations verify the effectiveness of the proposed control approaches. [ABSTRACT FROM AUTHOR]

An immune algorithm and a technique of sliding mode variable structure control (VSC) are proposed to achieve real-time and accurate control of wheeled robot in view of uncertainties for time-varying, strong coupling and nonlinear characteristics of wheeled robots, coupled with change of load and influence of external disturbance in traditional control algorithm based on classical control theory. First, the VSC parameters are adjusted online by the immune algorithm, which overcomes limitation that the parameters of reaching law need to be set in advance in conventional VSC. Secondly, the algorithm not only retains advantages of the traditional reaching law, but also effectively improves control quality and eliminates chattering of the system. The experimental results show that the control technique makes the wheeled robot move on sliding surface in an ideal way, for main concern of this paper is to provide an effective systemic for control of wheeled robot. [ABSTRACT FROM AUTHOR]

On 20 July 2021, northern Henan Province in China experienced catastrophic flooding as a result of an extremely intense rainstorm, with a record-breaking hourly rainfall of 201.9 mm during 0800–0900 UTC and daily accumulated rainfall in Zhengzhou City exceeding 600 mm ("Zhengzhou 7.20 rainstorm" for short). The multi-scale dynamical and thermodynamical mechanisms for this rainstorm are investigated based on station-observed and ERA-5 reanalysis datasets. The backward trajectory tracking shows that the warm, moist air from the northwestern Pacific was mainly transported toward Henan Province by confluent southeasterlies on the northern side of a strong typhoon In-Fa (2021), with the convergent southerlies associated with a weaker typhoon Cempaka (2021) concurrently transporting moisture northward from South China Sea, supporting the rainstorm. In the upper troposphere, two equatorward-intruding potential vorticity (PV) streamers within the planetary-scale wave train were located over northern Henan Province, forming significant divergent flow aloft to induce stronger ascending motion locally. Moreover, the converged moist air was also blocked by the mountains in western Henan Province and forced to rise so that a deep meso-β-scale convective vortex (MβCV) was induced over the west of Zhengzhou City. The PV budget analyses demonstrate that the MβCV development was attributed to the positive feedback between the rainfall-related diabatic heating and high-PV under the strong upward PV advection during the Zhengzhou 7.20 rainstorm. Importantly, the MβCV was forced by upper-level larger-scale westerlies becoming eastward-sloping, which allowed the mixtures of abundant raindrops and hydrometeors to ascend slantwise and accumulate just over Zhengzhou City, resulting in the record-breaking hourly rainfall locally. [ABSTRACT FROM AUTHOR]

In order to improve trajectory tracking accuracy and disturbance-rejection performance of the ball and plate system, a zero steady-state error tracking control strategy based on the principle of internal mode was proposed, and the backstepping controller with a low-pass filter was designed to suppress the system's internal disturbance. Firstly, a linear model of the ball and plate system is established and the common instability model for reference input and disturbance signals is derived. Secondly, the zero steady-state error tracking control strategy composed of three controllers which have respective function is proposed. Thirdly, the solving procedure of each controller is given and the stability of the closed-loop system is guaranteed by Lyapunov stability theory. Finally, simulation results show the presented control scheme has better dynamic performance and disturbance rejection ability, compared with other methods including H∞ control, sliding mode control, and LQR control. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a dynamic controller that dedicates fixed-time trajectory tracking for uncertain robotic manipulators. First, a new nonlinear function is designed and applied to the fixed-time sliding mode (FSM) surface. The derivative of the proposed nonlinear function is continuous, which can ensure the continuity and smoothness of the control torque, and it makes the sliding surface facilitate fast convergence rates of the system tracking error. Then, a FSM control scheme is designed using the proposed sliding mode surface. Combined the Lyapunov stability theory, we show that the proposed controller has fixed-time convergence independent of the initial state of the system. Moreover, the proposed control scheme is advantageous in that the control torque is continuous without transient change. This eliminates the chattering of uncertain robotic manipulators and makes the control torque smoother. Simulation results show that a faster error convergence rate can be obtained with a smaller control torque range. Finally, the above-mentioned effectiveness and superiority of the proposed control scheme are validated using simulations and experimental results. [ABSTRACT FROM AUTHOR]

This article presents a fuzzy adaptive control law (FACL) designed for tracking the trajectory of a low-scale unmanned aerial vehicle (UAV), based on a new fuzzy adaptive neural proportional integral derivative (FANPID) controller. FACL estimates the angles of rotation, if the reference trajectory is proposed, applying the adaptivity of the new FANPID-Lyapunov controller. UAV parameters were previously identified using the fuzzy adaptive neurons (FAN) method and experimental aerodynamic data. FANPID-Lyapunov controller optimizes trajectory tracking and stability analysis is performed. The FACL simulation results obtained in Matlab®/Simulink show the effectiveness, adaptivity and optimization of the flight control system, because it self-tunes the angles satisfactorily, adapts the gains and parameter for the FANPID-Lyapunov-Fuzzy controller, and reduces the error considerably compared to the controllers PID-Fixed gains, PID-Fuzzy adaptive gains, PID-Lyapunov-Fixed gains, and FOPID-Lyapunov-Fuzzy adaptive gains and parameters. [ABSTRACT FROM AUTHOR]

With the development of the new generation of information technology, artificial intelligence, cloud computing and big data are gradually becoming powerful engines of the smart grid. In recent years, people have been exploring how to reduce the dependence on human experience in the field of transmission line inspection. Therefore, transmission line inspection has attracted wide attention because of its high intelligence, flexibility and reliability. In this paper, we would like to present a survey on the intelligent transmission line inspection based on unmanned aerial vehicle (UAV). Firstly, the origin and development of intelligent electric power inspection are reviewed, and then the process of intelligent transmission line inspection and three key issues, i.e., path planning of UAV, trajectory tracking, and fault detection and diagnosis are presented in details. Finally, the challenges and future solutions are pointed out for power inspection. [ABSTRACT FROM AUTHOR]