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This paper presents a linear parameter varying (LPV) interpolation modeling method and modal-based pole placement (PP) control strategy for the ball screw drive (BSD) with varying dynamics. The BSD is modeled as a global LPV model with position-load dependence by selecting position and load as scheduling variables. The global LPV model is obtained from local subspace closed-loop identification and LPV interpolation modeling. A modal-based global LPV model is obtained through the similarity transformation. Based on this model, a modal-based LPV PP control strategy is proposed to achieve various modal control. Specifically, a state feedback control structure with an LPV state observer is designed to realize online state estimation and real-time state feedback control of modal state variables which cannot be measured directly. The steady-state error is minimized by introducing an error state space (SS) model with the integral effects. Moreover, the stability of the closed-loop system is analyzed according to the controllable decomposition and principle of separation. It is experimentally demonstrated that the proposed modal-based LPV PP control strategy can effectively achieve precise tracking and outstanding robustness meantime. • A global LPV model of the BSD is built by interpolation modeling. • A modal-based LPV pole placement control is proposed to achieve modal control. • The proposed modeling and control achieve precise tracking and excellent robustness. [ABSTRACT FROM AUTHOR]

In the realm of autonomous robotics, navigating differential drive mobile robots through unknown environments poses significant challenges due to their complex nonholonomic constraints. This issue is particularly acute in applications requiring precise trajectory tracking and effective obstacle avoidance without prior knowledge of the surroundings. Traditional navigation systems often struggle with these demands, leading to inefficiencies and potential safety risks. To address this problem, our studies propose an algorithm that integrates machine learning and control concepts, especially through the synergistic software of a Q-learning set of rules and a (PID) controller. This technique leverages the adaptability of Q-learning pathfinding and the precision of PID control for actual-time trajectory adjustment, aiming to beautify the robotics' navigation skills. Our comprehensive technique includes growing a country-area version that integrates Q-values with the dynamics of differential power robots, employing Bellman's equation for iterative coverage refinement. This version enables the robotics' capacity to dynamically adapt its navigation techniques in reaction to instant environmental feedback, thereby optimizing efficiency and protection in actual time. The effects of our full-size simulations exhibit a marked improvement in trajectory-tracking accuracy and impediment-avoidance competencies. These findings underscore the capability of combining machine learning algorithms with traditional methods to increase autonomous navigation technology in robotic systems. Our effects, derived from full-size simulations, suggest that the integration of Q-learning with PID controller markedly improves trajectory tracking accuracy, reduces tour times to targets, and complements the robotics' ability to navigate round barriers. This incorporated method demonstrates a tremendous advantage over conventional navigation systems, providing a sturdy way to the challenges of autonomous robot navigation in unpredictable environments. [ABSTRACT FROM AUTHOR]

This paper proposes a novel adaptive variable power sliding mode observer-based model predictive control (AVPSMO-MPC) method for the trajectory tracking of a Mecanum-wheeled mobile robot (MWMR) with external disturbances and model uncertainties. First, in the absence of disturbances and uncertainties, a model predictive controller that considers various physical constraints is designed based on the nominal dynamics model of the MWMR, which can transform the tracking problem into a constrained quadratic programming (QP) problem to solve the optimal control inputs online. Subsequently, to improve the anti-jamming ability of the MWMR, an AVPSMO is designed as a feedforward compensation controller to suppress the effects of external disturbances and model uncertainties during the actual motion of the MWMR, and the stability of the AVPSMO is proved via Lyapunov theory. The proposed AVPSMO-MPC method can achieve precise tracking control while ensuring that the constraints of MWMR are not violated in the presence of disturbances and uncertainties. Finally, comparative simulation cases are presented to demonstrate the effectiveness and robustness of the proposed method. • kinematic and dynamic constraints are handled efficiently. • The improved observer has a fast convergence rate without chattering. • The method can improve tracking performance. [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]

Wheeled Mobile Robots (WMRs) are systems with applications in diverse fields such as transportation, civilian services, military use, and space exploration. Then, their use will continue increasing, making WMRs an essential research topic that deserves further study. To this end, this work presents a novel observer-based finite-time controller for trajectory tracking control of WMRs disturbed by kinematic disturbances. In the proposed approach, the kinematic model of the WMR is transformed into a set of two decoupled second-order systems. Then, the proposed controller is divided into two parts. The first one employs an observer to estimate the effect of the kinematic disturbances. The second part consists of a finite-time controller designed to achieve finite-time convergence of the tracking error. A detailed synthesis procedure theoretically demonstrates the feasibility of the proposed controller. Subsequently, the proposed scheme is compared against finite-time, feedback, and H ∞ controllers. Exhaustive numerical simulations show that the proposed new control methodology achieves the trajectory tracking objective despite kinematic disturbances and outperforms the other control procedures. Finally, some comments and numerical results are given to clarify how the proposed control methodology can be used to design new controllers for trajectory tracking in WMRs and demonstrate that the new proposal remains to have a good performance when the system's coordinates are corrupted by measurement noise. [Display omitted] • A new observer-based finite-time control for wheeled mobile robots is proposed. • The new control scheme permits achieving the trajectory-tracking task efficiently. • The novel methodology outperforms existing finite-time, feedback, and H ∞ methodologies. • Excellent performance is obtained even in the presence of kinematic disturbances and measurement noise. • The proposed scheme permits the design of new controllers for WMRs easily. [ABSTRACT FROM AUTHOR]

This paper investigates the model predictive control (MPC) for the trajectory tracking of the unmanned vehicle system with bounded disturbances and actuator saturation based on the unscented Kalman filter (UKF). In order to obtain accurate system state, the linear matrix inequality (LMI)-constrained UKF is addressed by solving the LMI optimization problem. Moreover, by expressing the saturating linear feedback law as convex hull and describing the stability of the vehicle kinematics model with bounded disturbance via the quadratic bounded theorem, a model predictive controller to achieve trajectory tracking is proposed by solving the infinite horizon optimization problem. The effectiveness of the proposed approach is verified by the co-simulation platform of Matlab/Simulink and Carsim. The results of simulations show that this approach can improve the accuracy of state estimation as well as the trajectory tracking control. [ABSTRACT FROM AUTHOR]

To improve the transient response, accuracy and robustness of trajectory tracking control for cable-driven continuum robots (CDCRs), a recursive integral terminal sliding mode control combined with an adaptive disturbance observer (ADO-RITSMC) is proposed. The recursive integral terminal sliding mode control (RITSMC) guarantees fast transient response and high tracking accuracy with a fast zero convergence of the tracking error without chattering. To attenuate the effect of uncertain dynamics, an adaptive disturbance observer (ADO) is constructed to derive uncertain dynamics. Particularly, an improved grey wolf optimizer (IGWO) is merged into the ADO to enhance the estimating accuracy of uncertain dynamic factors. Simulation and experiment results demonstrate the superiority of the ADO-RITSMC in enabling fast transient response, high accuracy and strong robustness of trajectory tracking control. • The RITSMC achieves a fast zero convergence and high-precision trajectory tracking control without chattering. • Assigning a starting value for the integral term of the RISMC function to improve the transient response of the controller. • An ADO is designed to estimate uncertain dynamic factors and compensate for the control input. • An improved grey wolf optimizer (IGWO) is introduced into ADO for more accurate estimation of uncertain dynamic factors. [ABSTRACT FROM AUTHOR]

Accurate tracking of ball trajectories on a platform using a 2-DOF balancer system poses significant challenges in the existing literature due to its inherent nonlinearity and instability. This research addresses these challenges through a two-fold approach. Firstly, we focus on designing a robust 2-DOF ball balancer system model. Secondly, we perform a comparative study of three control techniques: Linear Quadratic Regulator-based Proportional (LQR_P), Full State Feedback-based Proportional (FSF_P), and Classical Proportional Derivative-based Proportional (PD_P) control. To evaluate the effectiveness of the designed controllers, we conduct both simulation and experimental tests using MATLAB Simulink integrated with Quarc software and the 2DOF ball balancer system Quanser hardware. The results demonstrate that the Linear Quadratic Regulator-based proportional control exhibits superior performance in terms of transient response, including percentage overshoot, settling time, and peak time. Moreover, it showcases excellent steady-state response, achieving a minimum steady-state error of 0.641mm, outperforming the other techniques investigated in this study. [ABSTRACT FROM AUTHOR]

Autonomous routing through curved path for point-to-point mobile robot is a well-established challenging problem in the area of intelligent path navigation. Point-to-point collision-free locomotion of AGV (Automated Guided Vehicle) requires precise differentiation of movable path from other environmental visual noise. As these path navigating depend on various visual cues and precepts, naturally the semantic meaning of path in contrast to other closely available objects on the visual window requires significant amount of understanding of the input from precepts. In this paper, it is experimentally established that semantic segmentation has proved to be a concise method in making comparative better segregation of paths from midway structured obstacles that are with high computational cost. The claims are also evident from surveyed works. The challenge of reducing high computational performance with efficient segmentation of movable area has been addressed in this research work with a focus on deriving better filtered accessible paths with a degree of algorithmic confidence. Further, this segmented result is taken for curved trajectory tracking by the AGV. A systematic percept mediated deep based Semantic-region Aware Model Predictive Trajectory Tracking(SAMP-TT)Algorithm has been proposed to solve the problem of precise obstacle detection on curved path trajectories. RGB (Red, Green Blue) image of outdoor multi-sectioned area has been collected by Intel Realsense D455 which worked as input feed to the respective neural network models. The performance of the proposed technique has been benchmarked with existing segmentation models including ENET (Efficient Neural Network), U-NET to confirm the considerable reliability and accuracy of the presented algorithm. It has been followed by the state-of-art comparative visual result analysis of different taken models with the proposed architecture. [ABSTRACT FROM AUTHOR]

For robot manipulators composed of Lagrange subsystems driven by direct current (DC) motors under stochastic environments, multi-stage trajectory tracking is investigated in this paper. The main challenge is how to achieve the end-effector drive of manipulators from a given initial state to a final state. First, the inverse kinematics method and the partition of the task space are adopted to tackle multi-stage trajectory planning. Second, the adaptive backstepping technique is used to design tracking controller for stochastic Lagrangian subsystems. Then, based on the state-dependent switching signal, a multi-stage switched controller is designed for trajectory tracking of robot manipulators. All signals in the close-loop error switched system are bounded in probability, and the tracking error in mean square can be made arbitrarily small enough by parameters-tuning The effectiveness of the proposed control method is illustrated by simulation results. • The multi-stage trajectory planning is proposed by the partition of the task space. • The adaptive backstepping controller is designed to track the pre-planned trajectory. • The multi-stage switched controller is proposed for performing a grab task. [ABSTRACT FROM AUTHOR]

We propose a self-organising kernel extreme learning machine (KELM) adaptive controller for nonaffine nonlinear systems. Literature survey reveals that neural network (NN) is extensively used to design adaptive controllers for nonlinear systems. When conventional NN, like multilayer feedforward NN and radial basis function NN (RBFNN), is used for controller design, the parameters of these networks converge slowly. Researchers have overcome this shortcoming by using extreme learning machine (ELM). The motivation to use KELM for controller design in our research is to utilise the advantages of ELM and radial basis function kernels. The structure of neural networks is seldom altered during training, resulting in unnecessarily small or large networks. The self-organising nature of our proposed controller caters to solving this problem. The structure of the self-organising KELM updates itself based on a threshold value set for the normalised change in the output weight. In our work, the control input meets three objectives : feedback linearisation, stabilisation of the linearised system and providing immunity to process and measurement noises. The update law for the hidden layer parameters of the KELM is obtained using the Lyapunov technique to ensure the overall stability of the system. A comparative analysis of different performance criteria is performed for trajectory tracking control, in the presence of process and measurement noises, for a numerical example and the Duffing–Holmes chaotic nonlinear system. The simulation results of these analyses demonstrate the superiority of the self-organising KELM compared to ELM and RBFNN based adaptive controllers. The experimental results with a rotary servo system validate the efficacy of the proposed controller in real-time systems. Furthermore, the robustness of the self-organising adaptive controller is verified with the results obtained for the servo system on varying the system parameter and operating condition. • Nonaffine nonlinear system controller design performed with extreme learning machine. • Controller structure varies with the amount of nonlinearity present in system model. • Tight tracking of reference trajectory. • Drastic reduction in training time. • Real-time implementation of proposed controller. [ABSTRACT FROM AUTHOR]

In this study, a three degrees of freedom (3 DOF) rigid-link robotic manipulator (RLM) has been simulated by using the Simscape model and the mathematical model derived by Lagrange method. The robot arm has been regulated by an Optimized PID Controller to achieve better tracking performance and reasonable robustness against disturbances and payload uncertainty. To optimize the controller, a novel nature-inspired Golden Jackal Optimization (GJO) algorithm has been used due to its efficient exploration that increases the diversity of the released solutions and its exploitation schemes which enhance the best-explored solutions. The tuning process has utilized a Lyapunov stability function as the objective function (OF) and the efficacy of the proposed algorithm is evaluated through a comprehensive comparison with various state-of-the-art metaheuristic techniques such as Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Jellyfish Search Optimizer (JSO), Whale Optimization Algorithm (WOA), Arithmetic Optimization Algorithm (AOA) and Sine Cosine Algorithm (SCA). The assessment has been conducted on benchmark error-based functions, providing rigorous testing and validation of the algorithm's performance. Furthermore, the performance evaluation has focused on the system's robustness against disturbances, noise, and variations in the payload mass, particularly in the context of Pick and Place (PNP) industrial tasks. The results of simulation have demonstrated that the optimized system, employing the Lyapunov function, demonstrated superior performance in minimizing the objective function value compared to other benchmark functions. [Display omitted] • Efficient 3-DOF robotic model using MATLAB Simscape, enabling precise representation and efficient simulation. • Utilizing the new approach of Lyapunov function as the objective function in the optimization process. • Comparing the performance of the GJO algorithm with other recent optimization algorithms commonly used in the literature. • Comprehensive performance analysis to offer valuable insights into the controller's adaptability in real-world scenarios. [ABSTRACT FROM AUTHOR]

This paper proposes a new adaptive super-twisting global integral terminal sliding mode control algorithm for the trajectory tracking of autonomous robotic manipulators with uncertain parameters, unknown disturbances, and actuator faults. Firstly, a novel global integral terminal sliding mode surface is designed to ensure that the tracking errors of autonomous robotic manipulators converge to zero in finite time and the global robustness of the system is also enhanced. Then a new adaptive method is devised to deal with the adverse effect of nonlinear uncertainty. To suppress the chattering phenomenon, the adaptive super-twisting algorithm is used in this paper, which can ensure that the control torque is a continuous input signal. Based on the adaptive mechanism, the adaptive super-twisting global integral terminal sliding mode controller is developed to provide superior control performance. The stability analysis of the system is demonstrated by using the Lyapunov method. Ultimately, the effectiveness of the control scheme is confirmed by a simulation study. • A novel adaptive finite-time global integral terminal sliding mode control scheme is designed. • A novel global integral terminal sliding mode surface is proposed to ensure faster convergence and better global robustness. • A novel adaptive law is designed to adjust the estimation parameters according to the change of system state to avoid overestimation. • The actuator fault is considered to make the proposed control logic more suitable for practical applications. [ABSTRACT FROM AUTHOR]

This paper aims to tackle the three-dimensional trajectory tracking problems of small fixed-wing unmanned aerial vehicles subject to nonlinearities, uncertainties and wind disturbances via adaptive techniques. The control objective is to efficiently control the thrust and the deflections of control surfaces to ensure the unmanned aerial vehicle arrives at a specified location within a given time frame. However, achieving this goal for small fixed-wing unmanned aerial vehicles can be challenging because the precise dynamic model and several parameters are not accessible, making most existing control strategies unworkable. Motivated by these facts, based on feedback linearization techniques, we derive linear models with equivalent disturbances to describe the translational dynamics without requiring precise aerodynamic force model information. To deal with the dilemmas where the norm bounds of equivalent disturbances depend on control inputs, system states, and unknown disturbances, a novel robust adaptive control strategy is designed for position control. Based on the assumption of two-time separation, the control scheme incorporates two parts, namely, a position controller containing the horizontal-plane and altitude parts and a robust filter-based attitude regulator. Also, to prevent chattering issues, we design a practical and robust adaptive position controller under which the tracking error is ultimately bounded The overall closed-loop stability is theoretically investigated based on the Lyapunov arguments. Hardware-in-loop simulation experiments are performed to testify our developed control scheme. • A novel robust adaptive control strategy is proposed to deal with the uncertainties related to not only system states but also to control inputs. • A cascade control strategy is employed to solve the trajectory tracking problems of fixed-wing UAVs. • A robust adaptive three-dimensional trajectory tracking controller is designed. • Hardware-in-Loop simulations are conducted. [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]

The design of fixed-time trajectory tracking controller for nonholonomic wheeled mobile robots (WMRs) subject to spatial constraint is addressed in this paper. Firstly, a tan-type Barrier Lyapunov Function (BLF) that equates to the classical one for unconstrained systems is exploited. Then, by employing the adding a power integrator technique and switching control strategy, a state feedback controller is successfully constructed to forces the error dynamic states of the closed-loop system (CLS) to zero in a given fixed time without violation of the constraint. Finally, simulation results are given to confirm the efficacy of the presented control scheme. [ABSTRACT FROM AUTHOR]

In this paper, we propose an adaptive sliding mode-based fault tolerant control for mobile robots. While a mobile robot is tracking a given trajectory, several fault cases may occur, such as sensor model and controller faults, changes in the dynamic equation due to robot body shape or weight changes, and loss of actuator effectiveness. Disturbance signals are caused by the actuator faults and, for various reasons, can be considered the primary issue for the robots. In real-time applications, the Sliding Mode Controller (SMC) is insufficient if the robot parameters are unknown, the robot model is non-linear, and the overall system is subject to disturbances. An adaptive law is used to support the SMC to maintain the sliding surface and solve the problems of unknown system parameters, actuator faults, and disturbances. Besides SMC, the kinematic controller is also used, and its gain values are optimized using a neural network and a kinematic controller. The stability of the overall system is proven by using the Lyapunov theory. Besides actuator faults, the system is disturbed by defining a disturbance signal, which is added to the control signals. To show the effectiveness of the proposed controller, it is compared with traditional SMC and PID. [ABSTRACT FROM AUTHOR]

In order to solve the trajectory tracking problem for robotic manipulators with dynamic uncertainty, external disturbance and input saturation, a novel second-order sliding mode control scheme based on neural network is proposed in this paper. First of all, a model-based second-order non-singular fast terminal sliding mode controller (SONFTSMC) is designed to overcome the chattering problem under the consideration of uncertain parameters. Then attention is focused on the scenario that all those nonlinear uncertainties are unknown, and a new fuzzy wavelet neural network (FWNN) is designed to estimate those unknown uncertainties via lumping them into one compounded uncertainty. In addition, all parameters in FWNN are adjusted autonomously by using an adaptive method. The proposed second-order non-singular fast terminal sliding mode (SONFTSM) control method not only improves the convergence speed and tracking accuracy of the robotic manipulator, but also enhances its robustness. Finally, the advantages of SONFTSM control strategy over existing sliding mode control methods are verified with comparative simulations. • A new second-order non-singular fast terminal sliding mode controller (SONFTSMC) based on neural network is designed. • The lumped uncertainty is estimated by using an improved fuzzy wavelet neural network (FWNN). • The input saturation problem is considered, which makes the proposed control logic more suitable for practical applications. • By modifying the second-order NFTSM surface, the fixed-time convergence of the sliding mode variable r is guaranteed. [ABSTRACT FROM AUTHOR]

This paper presents a new Global Fast Non-singular Terminal Sliding Mode Controller (GFNTSMC) that delivers high-precision tracking of high-frequency trajectories when applied to a piezo-driven nanopositioner. The control scheme is realized by combing inverse hysteresis model and global fast non-singular terminal sliding mode compensation. The inverse Bouc–Wen hysteresis model is used to calculate the required hysteresis-compensating feedforward control voltage according to the reference signal. The key uniqueness of the proposed control strategy is it's red global fast convergence, achieved with high accuracy and high bandwidth. The stability of the reported GFNTSMC controller is proved with the Lyapunov theory. Its performance is verified through experimentally recorded tracking results, and its superiority over three benchmark control approaches, namely the Proportional–Integral–Derivative (PID), the Positive Position Feedback with integral action (PPF+I) and the conventional linear high-order sliding mode controller (LHOSMC) is demonstrated through comparative tracking error analysis. Its wide-band stability as well as its significant robustness to parameter uncertainty is also showcased. • A high-order global fast nonsingular terminal sliding mode controller is proposed. • The stability and robustness of the controller are proved with the Lyapunov theory. • The system state can quickly reach the equilibrium point from any initial state. • Experimental results demonstrate significant accuracy improvement and superior robustness. [ABSTRACT FROM AUTHOR]

This paper proposes a disturbance observer-based prescribed performance super-twisting sliding mode control (DOB PPSTSMC) for trajectory tracking of the autonomous surface vessels (ASVs) subject to unknown external disturbances and modeling errors. Both unknown external disturbances and system modeling errors are approximated by the disturbance observer, thus eliminating most of the effect caused by lumped disturbances in the control performance. Based on this, a prescribed performance super-twisting sliding mode controller is explored to further suppress the residual error of disturbance compensation. Prescribed performance constraints are considered in the coming up with the super-twisting sliding mode controller, so that ASVs not only achieve effective tracking of time-varying desired trajectories, but also improve the transient performance of the control system. In addition, the controller is continuous and chatter-free, which has greater practical value. The excellence of the control project design is highlighted through the simulation and comparison results. • Based on the ASVs model, the DOB PPSTSMC is proposed. • The derivatives of the considered lumped disturbances are square-integrable. • The disturbance observer effectively compensates for the lumped disturbance. • The transient response of the system is improved. • The control signal is smooth and continuous without chatter. [ABSTRACT FROM AUTHOR]

In this paper, event-triggered model predictive control (EMPC) with adaptive artificial potential field (APF) is designed to realize obstacle avoidance and trajectory tracking for autonomous electric vehicles. An adaptive APF cost function is added to achieve obstacle avoidance and guarantee stability. The optimization problem for MPC is feasible by considering a special obstacle avoidance constraint. An event-triggered mechanism is proposed to reduce computational burden and ensure effectiveness of obstacle avoidance. Input and state constraints of autonomous electric vehicles are considered in both feasibility and stability by a robust terminal set. Effectiveness of both obstacle avoidance and trajectory tracking is shown by experimental results on autonomous electric vehicles. • A terminal set is designed to ensure stability under constraints and disturbance. • An adaptive force coefficient is designed to achieve stable tracking and avoiding. • An event-triggered mechanism considering obstacles is used to reduce computation. • A special constraint is developed to ensure the optimization feasibility. • Experimental results are given to show effectiveness of the EMPC strategy. [ABSTRACT FROM AUTHOR]

Among the UAVs, quadrotors are now the most popular and studied drones. These drones are called upon to perform various tasks, particularly the transport of increasingly heavy instruments or parcels, while keeping a structure as light as possible. The arms will inevitably deform under these loads, altering the flying machine's dynamic behavior. Precise drone flight control systems require rigorous dynamic modeling, taking into account the effect of deformation. In this study, we propose and evaluate an original design of a flexible quadcopter that will optimize its maneuverability and controllability in tight and complex spaces, even when carrying relatively heavy loads. The principle is based on the Lagrange method. The introduction of deformation is done through additional degrees of freedom. The effects of elastic deformation, rigid body motion, and coupling between them are all included in the final model. Based on the dynamic model built, we propose a new non-linear controller, which takes into account the arms deformation. The proposed controller adapts well to disturbances due to the effect of deformation, improving the trajectory tracking capability of the system. We will demonstrate the exponential stability of a compensated tracking error. [ABSTRACT FROM AUTHOR]

This paper studies the trajectory tracking problem of unmanned surface vehicle subject to unmeasurable velocities and unknown disturbances. By combining a fixed-time extended state observer (FESO) and a fixed-time differentiator, a fixed-time sliding mode control (FTSMC) law is proposed, in which a saturation function is adopted to make the terminal sliding mode surface leave the singularity area. The value of this paper can be described: first, this paper designs a novel guidance law that can converge in a fixed time to reduce the convergence time of the error. Then, unmeasurable velocities and lumped disturbances are estimated by applying a FESO. Meanwhile, a fixed-time differentiator is used to obtain real-time differential signals, thus reducing the difficulty of controller design. Subsequently, a novel auxiliary dynamic system is designed to address actuator saturation. According to Lyapunov's theory, the entire closed-loop control system has uniformly global fixed-time stability (UGFTS). The superiority of the designed controller is demonstrated through numerical simulations. • A novel fixed time guidance law is developed, which makes position errors reach zero within a fixed time. • A FESO is designed to estimate unknown dynamics within a fixed time, which does not require precise model parameters. • A novel auxiliary dynamic system is designed to handle input saturation, which can ensure that the state converges in finite time. • The fixed-time differentiator is introduced to achieve satisfactory filter and differential performance. • A novel FTSMC strategy is proposed for underactuated USV. Finally, the system proved to be uniformly global finite-time stable. [ABSTRACT FROM AUTHOR]

One of the challenges facing robot tracking is the difficulty of controlling variety types of robots in known and unknown environments. This paper proposes a new strategy of path tracking for a non-holonomic mobile robot subject to kinematic disturbances using model prediction control and H 2 / H ∞ control by Gain-scheduled control law. In this regard, the problem of tracking a mobile robot is expressed in a way that its kinematic error is in the form of linear parameter variable with bounded disturbance. The purpose of the design is to stabilize the controller by solving the linear matrix inequality (LMI) conditions for error tracking model. The following stability conditions are then formulated in such a way that the upper bounds are considered as LMI auxiliary variables. Also with the help of more free matrices, it can reduce conservatism. The target tracking is first designed with the assumption that there is no obstacle in the environment, and then the obstacle avoidance mode is added to the tracer controller, which is done by modifying the optimal state vector definition. In both cases, the system stability check is also performed. Simulation is performed on several predetermined paths, indicating that this strategy is feasible. [ABSTRACT FROM AUTHOR]

One of the challenges facing robot tracking is the difficulty of controlling variety types of robots in known and unknown environments. This paper proposes a new strategy of path tracking for a non-holonomic mobile robot subject to kinematic disturbances using model prediction control and H 2 / H ∞ control by Gain-scheduled control law. In this regard, the problem of tracking a mobile robot is expressed in a way that its kinematic error is in the form of linear parameter variable with bounded disturbance. The purpose of the design is to stabilize the controller by solving the linear matrix inequality (LMI) conditions for error tracking model. The following stability conditions are then formulated in such a way that the upper bounds are considered as LMI auxiliary variables. Also with the help of more free matrices, it can reduce conservatism. The target tracking is first designed with the assumption that there is no obstacle in the environment, and then the obstacle avoidance mode is added to the tracer controller, which is done by modifying the optimal state vector definition. In both cases, the system stability check is also performed. Simulation is performed on several predetermined paths, indicating that this strategy is feasible. [ABSTRACT FROM AUTHOR]

The paper proposes fuzzy and non-fuzzy controllers as two control techniques for trajectory tracking of a differential drive mobile robot. The first approach relies on fuzzy logic. Fuzzy logic systems represent knowledge via fuzzy rules. The large number of possible rules complicates the controller and has an impact on timing decision. Logic methods like Karnaugh maps and Quine McCluskey's algorithm have been offered as ways to adapt and reduce the amount of fuzzy rules. An approach based on Karnaugh maps to reduce the number of fuzzy rules without binary coding has been proposed. The second technique, on the other hand, is a non-fuzzy approach that employs the same rules as the reduced fuzzy controller, namely, non-fuzzy (ordinary) If–then rules. Simulation tests and a comparison of fuzzy and non-fuzzy controllers were used to test the efficiency of the proposed controllers. [ABSTRACT FROM AUTHOR]

Continuous monitoring of indoor human activities is crucial for the healthcare of elder people or patients. The self-powered noncontact triboelectric nanogenerator (TENG) sensor has been recently developed as a promising noncontact sensing tool with many advantages, but its capability of detecting object motion in a far distance still remains inferior. Herein, by implementing multiple strategies of micro-structuring triboelectric surface, introducing dielectric layer, and using functional nano fillers, we develop a single-electrode-mode noncontact TENG sensor by constructing a triboelectric cobalt nanoporous carbon/Ecoflex nanocomposite layer with microstructured square-loop surface on top of a dielectric copper calcium titanate/Ecoflex nanocomposite layer with colossal permittivity. After in-depth optimization of the material selection, component composition and structure design, the developed sensor with effective electrode area of 6.25 cm2 can deliver a high output voltage of 29 V at 1 cm, and is capable of detecting motions at an extremely far distance of 6 m. Towards practical applications, the TENG unit for single-motion detection of mouth breathing, human walking, and basketball bouncing are demonstrated. Furthermore, multiple TENG units can be assembled into array to realize the complex trajectory tracking for the use of proximity touch pad, indoor human activity monitoring, and garage car parking. This work for the first time extends the TENG noncontact sensing capability to several meters, which is potential in the proximity motion detection and indoor activity monitoring. [Display omitted] • A self-powered noncontact triboelectric nanogenerator is developed for contactless perception. • Surface of cobalt nanoporous carbon electrode is microstructured for better triboelectric property. • High-permittivity copper calcium titanate is added as electron blocking layer for better stability. • An extremely far detection range of 6 m is achieved by a small effective electrode area of 6.25 cm2. • Sensor unit for human motion detection and sensor array for trajectory tracking are demonstrated. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of practical fixed-time trajectory tracking for wheeled mobile robots (WMRs) subject to kinematic disturbances and input saturation. Firstly, considering the under-actuated characteristics of the WMR systems, the WMR model under kinematic disturbances is transformed into a two-input two-output interference system by using a set of output equations. Then, the tracking error state equation with lumped disturbances in the acceleration-level pseudo-dynamic control (ALPDC) structure is established. The lumped disturbances are estimated by a designed fixed-time extended state observer (FESO) without requiring the differentiability of the first-time derivatives of the kinematic disturbances. Meanwhile, a practical fixed-time output feedback control law is developed for trajectory tracking. By resorting to the Lyapunov stability theorem, the fixed-time stability analysis of the closed-loop WMR system in the presence of input saturation is conducted. Finally, simulation results are presented to show the effectiveness of the proposed approach. • The ESO technique is utilized to deal with the fixed-time trajectory tracking problem of WMRs with unknown kinematic disturbance. • The proposed practical fixed-time trajectory tracking control approaches are proposed without the requirement of the PE conditions. • The input compensation technique is utilized to achieve the bounded output feedback control. [ABSTRACT FROM AUTHOR]

This correspondence deals with the trajectory tracking control of an un-crewed helicopter during hover/low-speed flights. A multi-loop architecture is used in which the inner-loop holds the fast changing dynamics and the outer-loop establishes the trajectory tracking. The inner-loop is closed with a constrained H ∞ based controller which is cautiously designed to address actuator saturation, atmospheric wind disturbance, and parametric uncertainty. The outer-loop adaptive composite nonlinear control comprises of a linear part and a nonlinear part. A novel adaptive controller is proposed as the nonlinear part which improves the tracking performance by adaptively adjusting the damping ratio. The linear control element of the outer-loop is framed similar to inner-loop. Utilization of output feedback instead of full state feedback makes the flight control design simple and practically feasible. The closed loop stability and robustness property of the proposed scheme are analyzed. Simulation studies are performed to establish the hovering, station keeping, and trajectory tracking performance of the suggested control structure. Further, the performance of the proposed scheme is compared with a constrained static output feedback controller and a model reference adaptive proportional integral controller to confirm its superiority. • Trajectory tacking of a small scale unmanned helicopter in hover/low speed flights. • Design of a constrained H ∞ controller for inner-loop dynamics. • Formulation of inner-loop controller as a convex optimization problem. • Design of an adaptive composite nonlinear output feedback controller for outer-loop. • Simulation of different flight tasks with the proposed scheme. [ABSTRACT FROM AUTHOR]

The present investigation suggests a novel control technique that merge the advantage of the adaptive Neuro-Fuzzy inference system (ANFIS) with the proportional integral derivative (PID) controller, abbreviated as (ANFIS-PID), for dealing with the dynamics of the of wheeled mobile robot (WMR). A comparative study of various meta-heuristic optimization algorithms is made. The results revealed the highest efficiency of the suggested ANFIS-PID technique compared to seven designed PID controllers, in terms of settling and rise time, overshoot, and the integral square error (ISE) as a cost function. Various cases, study (circular path, diamond path, zigzag path) have highlighted the over performance of mentioned controller. Moreover, this hybrid technique is fused with backstepping approach for the kinematic control. A lemniscate and a square trajectory were performed to clarify the capability of the mentioned controller. [ABSTRACT FROM AUTHOR]

This work is largely concerned with trajectory tracking in linear complementarity systems (LCS). The main analytical tool for stability analysis and control design is passivity and the associated linear matrix inequalities (LMIs) for feedback passification. Cases with and without state-jumps, with and without parametric uncertainties, are analyzed. Theoretical findings are illustrated with examples from circuits with set-valued, nonsmooth electronic components, networks with unilateral interactions, and mechanical system with unilateral spring. [ABSTRACT FROM AUTHOR]

This paper proposes a robust cooperative trajectory tracking control scheme for an unactuated floating object with multiple vessels under environmental disturbances. The object and multiple vessels are connected by using towlines. The proposed control scheme consists of three parts: a virtual controller for the object, a control allocation algorithm and a distributed robust time-varying formation controller for vessels. The virtual controller is first designed to obtain the control forces of the object to track the reference trajectory. To compute the optimal tension of each towline, the control allocation algorithm is introduced. Then, the time-varying relative positions from the object to vessels are gained by using a nonlinear towline model and the towline attachment geometry. Furthermore, the distributed robust time-varying formation controller is devised for vessels based on dynamic surface control technique, an adaptive law and graph theory. It is proved that the tracking errors of the object and vessels are bounded. Simulations substantiate that the proposed method can achieve good cooperative control performance and robustness, and the unactuated object can track the reference trajectory with high accuracy. • Cooperative tracking control for an unactuated object and multi-vessel is studied. • Time-varying formation is gained via a towline model and towline attachment geometry. • The proposed control scheme is robust against environmental disturbances. [ABSTRACT FROM AUTHOR]

This paper considers the trajectory tracking problem of an underactuated underwater vehicle actuated by control moment gyros (CMGs) in three-dimensional (3D) space, with the constraints from input saturation, partial parameter uncertainty and unknown external disturbance. First, utilizing a physical translation of the motion equations, the overall system can be decomposed to an input decoupling system. Then a modified virtual velocity guidance law is derived to transform the tracking error signals into the controllable velocity signals. Subsequently, the Gaussian error function is employed to update the common saturation model. To avoid complex derivations of the virtual control signals, first-order sliding mode differentiator is explored in the dynamic control layer. Then, the adaptive neural network (NN) control method is introduced into the backstepping procedure to account for nonlinear uncertainties and bounded disturbances. Among this, the constrained steering law is used to steer the CMG system to avoid its inherent singularity and fulfill the global tracking control. It is proved that the proposed controller can guarantee all closed-loop signals converge to a small neighborhood of the origin. Finally, two case studies are presented to illustrate the tracking performance of the proposed design. • This paper investigates the 3D trajectory tracking control for a CMG-actuated AUV.. • A physical translation and an improved velocity guidance law are introduced. • Adaptive NN control method is introduced to evaluate the compound error. • Two case studies are provided to demonstrate the effective of the proposed method. [ABSTRACT FROM AUTHOR]

A design of an optimal backstepping fractional order proportional integral derivative (FOPID) controller for handling the trajectory tracking problem of wheeled mobile robots (WMR) is examined in this study. Tuning parameters is a challenging task, to overcome this issue a hybrid meta-heuristic optimization algorithm has been utilized. This evolutionary technique is known as the hybrid whale grey wolf optimizer (HWGO), which benefits from the performances of the two traditional algorithms, the whale optimizer algorithm (WOA) and the grey wolf optimizer (GWO), to obtain the most suitable solution. The efficiency of the HWGO algorithm is compared against those of the original algorithms WOA, GWO, the particle swarm optimizer (PSO), and the hybrid particle swarm grey wolf optimizer (HPSOGWO). The simulation results in MATLAB–Simulink environment revealed the highest efficiency of the suggested HWGO technique compared to the other methods in terms of settling and rise time, overshoot, as well as steady-state error. Finally, a star trajectory is made to illustrate the capability of the mentioned controller. [ABSTRACT FROM AUTHOR]

Aiming at the problems of low control precision and poor anti-disturbances ability of permanent magnet spherical actuator (PMSA), an active disturbance rejection trajectory tracking control method based on nonlinear extended state observer (NESO) is proposed in this paper. Firstly, the multivariable and strong coupling mathematical model of the PMSA is obtained by rigid body rotation coordinate transformation and the Lagrange dynamic equation. Then, the tracking differentiator is used to reduce the influence caused by the expected trajectory jump. In addition, NESO is designed to observe and calculate coupling of the system, external disturbance, and model error online. Finally, the nonlinear control law is used to compensate for the observed disturbance, and the stability is proved based on the Lyapunov equation. Simulation and experimental results show that the proposed control scheme can achieve decoupling and tracking control of the complex system in the presence of model error, random disturbance, and other uncertain factors, has good control accuracy and response speed, and has strong robustness to uncertain disturbances. [ABSTRACT FROM AUTHOR]

Obtaining a control algorithm capable of navigating the system both in forward and backward motions is one of the control objectives for tractor-trailer wheeled robots (TTWRs). In this paper, a relatively general structure is presented for both forward and backward control of an n-trailer wheeled mobile robot (NTWMR) in the presence of wheel slip effects. To keep better overall performance and track the reference trajectories in forward and backward motions, the NTWMR is intended to be controlled in the presence of slip effects. A control algorithm accompanied by a slip compensation procedure is proposed for the system simultaneously. First, the mathematical model of the system in the presence of slip effects is obtained. A novel physically motivated algorithm is proposed for the tracking control in the presence of unknown uncertainties (longitudinal and lateral slips) for both forward and backward motions. By estimating the slip effects at any instant, the control inputs are produced to compensate for their destructive effects on tracking control of the NTWMR. Then the stability of the closed-loop system is evaluated using the Lyapunov theory. The potential of the proposed controller was verified through several case studies, including comparative results and experimental validation in various motion control manoeuvers for a vehicle with trailers. The proposed method is the first algorithm that can cover a broad range of TTWR motion tasks (forward and backward trajectory tracking, slip attenuation, and global stability), which are required to be developed in NTWMRs. • Proposing a control algorithm for forward and backward trajectory tracking. • Designing the controller in the configuration space without using transformations. • Investigating a slip estimator to attenuate the effects of unknown uncertainties. • Analyzing the stability of the control algorithm using the Lyapunov method. • Presenting various case studies and comparative results to show the effectiveness. [ABSTRACT FROM AUTHOR]

This paper proposes an adaptive and robust adaptive control strategy based on type-2 fuzzy neural network (T2FNN) for tracking the desired trajectories of a quadrotor. The designed methods can control both the position and the orientation of a quadrotor. Contrary to common sliding mode controllers (SMCs), the robust adaptive trajectory tracking scheme presented here is based on SMC with exponential reaching law; which helps reduce the phenomenon of chattering. Moreover, parameters including the gains of sliding surfaces, are optimized by cuckoo optimization algorithm (COA), and the results are compared with those obtained by genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization (ACO). The designed methods in this study are called adaptive T2FNN controller and the exponential SMC (ESMC)-T2FNN. The law for updating the T2FNN is obtained online by using the Lyapunov stability theory. Considering undesired factors such as uncertainties, external disturbances and control signal saturation, the results of our controllers are compared with those of the adaptive type-1 fuzzy neural network controller (T1FNN) and ESMC-T1FNN. The extensive simulations demonstrate the effectiveness of the proposed COA-based ESMC-AT2FNN approach compared to the other tested techniques (i.e. GA, PSO and ACO) in terms of the improved transient and steady-state trajectory-tracking performance. The mean and standard deviation values concerning the COA are obtained through statistical analyses at 0.00006173 and 0.000092, respectively. This paper also examines the complexity of COA in optimizing the trajectory tracking control of quadrotor and investigates the effects of COA parameters on optimization results. The stable performance of the cuckoo algorithm is demonstrated by varying its parameters and analyzing the obtained results. These results also show the convergence of COA for the considered problem. • Trajectory tracking of a quadrotor is proposed using a type-2 fuzzy neural controller. • Strong robustness is provided against the structured and unstructured uncertainties. • Exponential sliding mode reaching law is introduced for a smooth asymptotic stability. • Controller design parameters are obtained optimally through Cuckoo optimization algorithm. [ABSTRACT FROM AUTHOR]

To achieve better trajectory tracking of the autonomous ground vehicle (AGV), a dual-motor autonomous steering structure and control method are proposed. The model predictive control (MPC) algorithm is utilized to calculate the optimal front wheel angle in real time based on the vehicle information, and dual-motor steering system follows the target angle to achieve trajectory tracking. In this process, to improve the stability of the angle motor, a coordinated steering controller of the angle motor and torque motor is proposed, and the type-2 fuzzy logic is designed based on the target front wheel angle and vehicle speed to optimize coordination coefficient. The front wheel angle step input and double-lane change trajectory tracking conditions are simulated in the carsim and simulink joint simulation platform, the results show that dual-motor steering system has good trajectory tracking ability, compared with no coordinated control, the current and velocity of angle motor are more stable under the coordinated steering control, which greatly reduces the steering load of the angle motor under different working conditions. The final hardware-in-loop test is put forward to verify the effectiveness of the MPC controller and coordinated steering controller. [ABSTRACT FROM AUTHOR]

The proxy-based sliding mode control (PSMC) is a previously-proposed controller for the position control of robotic manipulators. It is an alternative approximation of a simple sliding mode control (SMC) of which the set-valuedness is relaxed in a particular differential-algebraic manner, and has been found useful for many applications especially in robotics. This paper presents the results of frequency-domain analysis and simulations to clarify the mathematical nature of PSMC subjected to measurement noise and disturbance. The results show that, in the presence of measurement noise and disturbance, the sliding motion realized by PSMC is closer to that of the ideal SMC than those of conventional implementations of SMC with the boundary-layer approximation and low-pass filtering. Comparisons in terms of the chattering intensity and the sliding motion accuracy are also presented. Moreover, it is shown that PSMC's response to disturbance in the sliding motion is quite different from that of the conventional implementations. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology 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 develops a trajectory tracking control design algorithm to be applied in unmanned aerial vehicles (UAVs). The strategy is simple but effective and it is based on linear algebra theory. The proposed approach reforms the column space of a system of linear equations at each sampling time to ensure the tracking objective when environmental disturbances appear. This new formulation ensures a uniform signal without affecting the error convergence to zero (demonstration available), which is one of the main contributions of this work. A statistical method is used to tune the system control minimizing a pre-defined cost function. In addition, the convergence to zero of the tracking errors is demonstrated in this work. Finally, the controller's effectiveness is tested through several simulations in realistic test scenarios in the presence of disturbances. • The trajectory tracking control in UAV under uncertainties is addressed. • The controller design for trajectory tracking is based on linear algebra. • The approach is applied in a nonlinear system with additive uncertainty. • The zero convergence of tracking error under polynomial uncertainties is demonstrated. • Several simulations in realistic scenarios were performed. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology 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.)

Traditional articulated vehicles exhibit poor steering performance since there exist no direct rotating moment for the underactuated trailers, which cause such vehicles nearly cannot work well in the narrow space. To overcome this shortcoming associated with the traditional articulated vehicles, an active articulated structure is proposed, in which a steering motor is introduced at the articulated joint of tractor and trailer, in addition to utilizing mecanum wheels as trailer wheels. On the basis of this structure, a coordinated control method is designed to ensure the vehicle kinematics constraints and dynamic maneuverability. The coordination control consists of two level controllers. On the level of kinematics, model predictive control (MPC) is adopted as posture controller, which can solve the non-holonomic problem of the whole active articulated tractor-trailer vehicle system; On the dynamic level, a sliding mode control (SMC) is introduced to design a dynamic controller to track the desired velocities generated online, which can increase the robustness of the system. The simulation results show that the proposed active articulated tractor-trailer vehicle system has better maneuverability compared with the traditional one, and the proposed control strategy can ensure the required control effect. [ABSTRACT FROM AUTHOR]

The Active Disturbance Rejection Control (ADRC) is an object of the ever growing interest in the latest years due to its limited requirements concerning knowledge of a plant's mathematical model. In this paper, a problem of system stability in presence of modelling uncertainties is investigated. Precisely, imperfect knowledge of the manipulator dynamics and an inertia matrix is considered by the means of the Lyapunov analysis and conditions are formulated which guarantees convergence of the errors in the system to some finite boundary. Influence of the unknown inertia matrix is thoroughly investigated. It is shown, that if the third derivative of the desired trajectory is small enough then an intentional increase of the input matrix estimation error may lead to a reduction of the tracking error boundary. Obtained results are supported by presented simulations and experiments. • The ADRC is practically stable at least for some range of modelling inaccuracy. • Asymptotical stability can be guaranteed for constant input gain and disturbance. • Overestimation of model's inertia matrix can lead to increase of tracking quality. • High-precision practical experiments confirm previous remarks. [ABSTRACT FROM AUTHOR]

The main goal of this study is developing an adaptive controller which can solve the trajectory tracking for a class of quadcopter unmanned aerial system (UAS), namely a quadrotor. The control design introduces a new paradigm for adaptive controllers based on the implementation of a set of differential neural networks (DNNs) in the consequence section of a Takagi–Sugeno (T–S) fuzzy inference system. This dynamic fuzzy inference structure was used to approximate the UAS description. The particular form of interaction between neural networks and fuzzy inference systems proposed in the present work received the name of dynamic neural fuzzy system (DNFS). An adaptive controller based on this DNFS form was the main solution attained in this study. This DNFS controller was focused on the estimation and compensation of the uncertain section of the Quadrotor dynamics and then, forced the UAS to perform a hover flight while the tracking of desired angular positions succeeded, which results in tracking a desired trajectory in the X-Y plane. The control design methodology supported on the Lyapunov stability theory guaranteed ultimate boundedness of the estimation and tracking errors simultaneously. Several experimental tests in an outdoor environment by using a real Quadrotor platform was performed by using an RTK-GPS (Real Time Kinematic) system to determine the position of the vehicle in the X-Y plane. The experimental results confirmed the superior performance of the proposed algorithm based on the combination of DNNs and T–S techniques with respect to classical robust controllers. • A neuro fuzzy adaptive controller is designed to solve trajectory tracking problem for a Quadrotor unmanned aerial system. • The control algorithm uses a neuro-fuzzy structure by aggregating differential neural networks (DNNs) with a T–S inference system. • An adaptive compensation was applied by using the estimation of the uncertain section of the Quadrotor dynamics. • This adaptive neuro-fuzzy controller enforces the Quadrotor UAS to perform a hover flight. • Experiments confirmed the superior performance of the proposed neuro-fuzzy controller with respect to classical controllers. [ABSTRACT FROM AUTHOR]

This paper discusses the problem of adaptive trajectory tracking control for remotely operated vehicles (ROVs). Considering thruster dynamics, a third-order state space equation is used to describe the dynamic model of ROVs. For the problem of unknown dynamics and partially known input gain, an adaptive sliding mode control design scheme based on RBF neural networks is developed using a backstepping design technique. Because of the saturation constraints of the thrusters, a first-order auxiliary state system is applied, and subsequently, a saturation factor is constructed for designing adaptive laws to ensure the stability of the adaptive trajectory tracking system when the thrusters are saturated. The proposed controller guaranteed that trajectory tracking errors are uniformly ultimately bounded (UUD). Finally, the effectiveness of the proposed controller is verified by simulations. • The thruster dynamics and saturation constraints were considered in controller design. • A third-order state space equation was constructed to describe the dynamic of ROVs. • The unknown dynamics and partially known gain were considered in controller design. • A saturation factor was constructed for the design of the adaptive laws. [ABSTRACT FROM AUTHOR]

This paper presents the dynamic modelling and control technique for a tilt-rotor aerial vehicle operating in bi-rotor mode. This kind of aircraft combines two flight envelopes, making it ideal for scenarios that require hovering, vertical take-off/landing and fixed-wing capabilities. In this work, a detailed mathematical model is derived using Newton-Euler formalism. Based on the obtained model, a new control scheme that incorporates six Proportional-Derivative (PD) controllers is proposed for the attitudes (roll (φ), pitch (θ), yaw (ψ)) and the positions (x, y, z) of the aircraft. Then, intelligent Particle Swarm Optimization (PSO) and conventional Reference Model (RM) techniques are applied for optimal tuning of the controllers' parameters. The stability analysis is developed using the Lyapunov approach and its application to the tilt-rotor system in the case of intelligent and conventional PD controllers. Numerical results of two scenarios prove the efficiency of the controllers tuned using the PSO method. Indeed, its ability to track the desired trajectories is demonstrated through 3D path tracking simulations, even in the presence of wind disturbances. Finally, experimental tests of stabilization and trajectory tracking are carried out on our prototype. These testing showed that our tilt-rotor was stable and suitably follows the imposed trajectories. [ABSTRACT FROM AUTHOR]

As far as complex contact-based manufacturing tasks are concerned, humans outperform machines. Indeed, conventionally controlled robotic manipulators are limited to basic applications in close to ideal circumstances. However, tedious work in hazardous environments, make some tasks unsuitable for humans. Therefore, the interest in expanding the application-areas of robots arose. This paper employs a bottom-up approach to develop robust and adaptive learning algorithms for trajectory tracking: position and torque control in the presence of uncertainties and switching constraints. The robotic manipulators mimicking the human behavior based on bio-inspired algorithms, take advantage of their know-how. Simulations and experiments validate the concept-performance. [ABSTRACT FROM AUTHOR]

In this paper, we address the problem of trajectory tracking control of underactuated surface vessels in a quantitative method with only position and attitude available. Combined with high-gain observer, parameter compression algorithm and performance function, an adaptive control scheme with prescribed performance is proposed. The high-gain observer is constructed to estimate the velocities, and the parameter compression algorithm is adopted to address persistent perturbations and model uncertainties in a more concise way. By prescribed performance function, the controller can be designed with prescribed performance. The results about system stability is given and proved by using the Lyapunov direct method. The signals concerning with all the errors converge to a bounded set. Compared with the existing methods, the developed scheme can reduce the number of tuning parameters, and guarantee the tracking errors bounded within the prescribed performance constraints in the transformed coordinate, which means the steady errors, convergence rates and maximum overshoots can be guaranteed by the performance function. Comparison and numerical simulations are given to demonstrate the effectiveness of the proposed scheme. • An adaptive control scheme is proposed with only position and yaw angle available. • Parameter compression algorithm is adopted to reduce the number of tuning parameters. • The proposed scheme can guarantee the tracking errors with prescribed performance. [ABSTRACT FROM AUTHOR]