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In this work, autonomous navigation algorithms are implemented using specialized robotics tools known as Robot Operating System (ROS). The selected algorithms have been implemented in the high-level language Python, and validation tests are carried out in a real differential robot built at the laboratory CERLab. The main contribution is the implementation of a mixture of behaviors between the Pure Pursuit trajectory tracking algorithm and the Braitenberg obstacle avoidance algorithm, applied in simulations and to a differential robot. From the simulations and validation tests conducted, reliable performance is observed both for trajectory following and obstacle avoidance, according to the measurements executed with a motion capture system, showing satisfactory trajectory following in square and circular trajectories, as well as in the fixed obstacle avoidance in the test arena. [ABSTRACT FROM AUTHOR]

The article presents the implementation of autonomous navigation algorithms in a virtual simulation environment developed by the F1TENTH project. The selected algorithms include pure pursuit and a modification of the Braitenberg algorithm, which are combined to achieve desired behaviors. An improvement is proposed in the Braitenberg algorithm for high-speed obstacle avoidance. The algorithms were tested independently and in combination, achieving successful collision-free paths and desired trajectory tracking. The study was conducted by the CERLab at the University of Costa Rica and funded by the Vice-Rectorate of Research of the same university. The article titled "Global and Local Path Planning Study in a ROS-Based Research Platform for Autonomous Vehicles" presents a study on global and local route planning in a ROS-based research platform for autonomous vehicles. [Extracted from the article]

A novel robust adaptive neural network (NN) control scheme with prescribed performance is developed for the 3-D trajectory tracking of underactuated autonomous underwater vehicles (AUVs) with uncertain dynamics and unknown disturbances using new prescribed performance functions, an additional term, the radial basis function (RBF) NN, and the command-filtered backstepping approach. Different from the traditional prescribed performance functions, the new prescribed performance functions are innovatively proposed such that the time desired for the trajectory tracking errors of AUVs to reach and stay within the prescribed error tolerance band can be preset exactly and flexibly. The additional term with the Nussbaum function is designed to deal with the underactuation problem of AUVs. By means of RBF NN, the uncertain item lumped by the uncertain dynamics of AUVs and unknown disturbances is eventually transformed into a linearly parametric form with only a single unknown parameter. The developed control scheme ensures that all signals in the AUV 3-D trajectory tracking closed-loop control system are bounded. Simulation results with comparisons show the validity and the superiority of our developed control scheme. [ABSTRACT FROM AUTHOR]

Reinforcement learning (RL) is a promising technique for designing a model-free controller by interacting with the environment. Several researchers have applied RL to autonomous underwater vehicles (AUVs) for motion control, such as trajectory tracking. However, the existing RL-based controller usually assumes that the unknown AUV dynamics keep invariant during the operation period, limiting its further application in the complex underwater environment. In this article, a novel meta-RL-based control scheme is proposed for trajectory tracking control of AUV in the presence of unknown and time-varying dynamics. To this end, we divide the tracking task for AUV with time-varying dynamics into multiple specific tasks with fixed time-varying dynamics, to which we apply meta-RL for training to distill the general control policy. The obtained control policy can transfer to the testing phase with high adaptability. Inspired by the line-of-sight (LOS) tracking rule, we formulate each specific task as a Markov decision process (MDP) with a well-designed state and reward function. Furthermore, a novel policy network with an attention module is proposed to extract the hidden information of AUV dynamics. The simulation environment with time-varying dynamics is established, and the simulation results reveal the effectiveness of our proposed method. [ABSTRACT FROM AUTHOR]

This work addresses the issue of trajectory tracking accuracy degradation for vehicle dynamics parameters and road conditions changing, an adaptive trajectory tracking controller is designed for the intelligent vehicle. Based on the yaw dynamics model, a model predictive controller (MPC) is developed for the trajectory tracking process. Meanwhile, the impacts of different control parameters on trajectory tracking at various speeds are compared. And the change rule between optimal control parameter values and reference speeds is determined by cubic polynomial fitting to ensure minimal error during the trajectory following process. Then, an estimator for vehicle tire cornering stiffness and road adhesion coefficient is designed and tested based on the accelerated back-propagation neural network (ABPNN) model, with its estimation values being compared to those obtained through recursive least square method. Last, a co-simulation platform combining MATLAB/Simulink and CarSim software is established to conduct the trajectory tracking experiment under variable working conditions. Experimental results show that the proposed adaptive controller not only exhibits high accuracy in trajectory tracking but also demonstrates excellent stability and adaptability under medium to low speed conditions, particularly when encountering changing road conditions. [ABSTRACT FROM AUTHOR]

This article studies the optimal control policy learning for underactuated vertical take-off and landing (VTOL) aerial vehicles subject to the unknown mass and inertia matrix. A novel off-policy integral reinforcement learning (IRL) scheme is presented for simultaneously unknown parameter identification and optimal trajectory tracking. In the outer loop of the VTOL vehicles, a novel off-policy IRL scheme is proposed, where the fixed control policy for data generation is chosen to be different from the iterated control policy and the feedforward term with an unknown mass can be learned along with the optimal control policy. In the inner loop, a hybrid off-policy IRL algorithm is developed to tackle the optimal attitude control policy learning and inertia matrix identification under the hybrid control scheme introduced by the employed inner–outer loop control strategy. A simulation study is finally provided to demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

We present a model predictive control (MPC) framework to solve the constrained nonlinear output regulation problem. The main feature of the proposed framework is that the application does not require the solution to classical regulator (Francis–Byrnes–Isidori) equations or any other offline design procedure. In particular, the proposed formulation simply minimizes the predicted output error, possibly with some input regularization. Instead of using terminal cost/sets or a positive-definite stage cost as is standard in MPC theory, we build on the theoretical results by Grimm et al. using a detectability notion. The proposed formulation is applicable if the constrained nonlinear regulation problem is (strictly) feasible; the plant is incrementally stabilizable and incrementally input–output to state stable (i-IOSS, detectable). We show that for minimum phase systems, such a design ensures exponential stability of the regulator manifold. We also provide a design procedure in case of unstable zero dynamics using an incremental input regularization and a nonresonance condition. The theoretical results are illustrated with an example involving offset-free tracking. [ABSTRACT FROM AUTHOR]

In this study, we investigate the active fault tolerant control(AFTC) of unmanned surface vehicles (USV) under actuator faults and environmental disturbances using deep reinforcement learning(DRL). We realize the perception of the environment and continuous control through end-to-end learning based on neural networks(NN). The contents of AFTC include 1) actuator fault estimation(FE) based on long short-term memory(LSTM) and 2) a fault tolerant control(FTC) framework based on a deep deterministic policy gradient to enable the USV to learn autonomous decision making through training. We introduce mixed noise based on Gaussian noise and Ornstein–Uhlenbeck noise into the training, which increases the exploration space and improves the generalization ability of the USV. An experiential replay method based on quantum computing is designed to distinguish the importance of samples through preparation and depreciation operations, enabling a guided learning process. Finally, we verify the universality and effectiveness of the AFTC framework by tracking straight and sinusoidal trajectories. [ABSTRACT FROM AUTHOR]

In this paper, a Takenaka-Malmquist (TM) basis function based equivalent data-model is established by an adaptive rational decomposition for the finite-time interval trajectory tracking control and real-time control of a class of nonlinear systems in the frequency domain. This data model can adaptively learn and match the control process of nonlinear systems. As a result, the proposed trajectory tracking as well as real-time control method can reflect the feature of adaptive learning in order-by-order decomposition, and the feasibility of the proposed method is guaranteed by the convergence of adaptive decomposition by TM basis function under the maximum selection principle (MSP) in Hardy space $H^{2}(\mathbb {D})$. Compared with the traditional model-free control method, this data learning model which matches the control process has obvious advantages in the system model expression and control accuracy. Simulation results at the end of this paper show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this article, we address the disturbance/ uncertainty estimation of maritime autonomous surface ships (MASSs) with unknown internal dynamics, unknown external disturbances, and unknown input gains. In contrast to existing disturbance observers where some prior knowledge on kinetic model parameters such as the control input gains is available in advance, reduced- and full-order data-driven adaptive disturbance observers (DADOs) are proposed for estimating unknown input gains, as well as total disturbance composed of unknown internal dynamics and external disturbances. An advantage of the proposed DADOs is that the total disturbance and input gains can be simultaneously estimated with guaranteed convergence via data-driven adaption. We apply the proposed full-order DADO for the trajectory tracking control of an MASS without kinetic modeling and present a model-free trajectory tracking control law for the ship based on the DADO and a backstepping technique. We report the simulation results to substantiate the efficacy of the proposed DADO approach to model-free trajectory tracking control of an autonomous surface ship without knowing its dynamics. [ABSTRACT FROM AUTHOR]

This article addresses the problem of distributed path following of multiple under-actuated autonomous surface vehicles (ASVs) with completely unknown kinetic models. An integrated distributed guidance and learning control architecture is proposed for achieving a time-varying formation. Specifically, a robust distributed guidance law at the kinematic level is developed based on a consensus approach, a path-following mechanism, and an extended state observer. At the kinetic level, a model-free kinetic control law based on data-driven neural predictors via integral concurrent learning is designed such that the kinetic model can be learned by using recorded data. The advantage of the proposed method is two-folds. First, the proposed formation controllers are able to achieve various time-varying formations without using the velocities of neighboring vehicles. Second, the proposed control law is model-free without any parameter information on kinetic models. Simulation results substantiate the effectiveness of the proposed robust distributed guidance and model-free control laws for multiple under-actuated ASVs with fully unknown kinetic models. [ABSTRACT FROM AUTHOR]

This article proposes an adaptive integral sliding mode control (ISMC) strategy for quadrotor control that ensures faster and finite-time convergence along with chattering attenuation. Quadrotor dynamics are assumed to be unknown because of the high degree of parametric uncertainties, including external disturbances. The equivalent control law obtained by ISMC consists of quadrotor dynamics and, thus, cannot be applied to the quadrotor. A new fully connected recurrent neural network (FCRNN) controller has been proposed to mimic the equivalent control instead of estimating the Quadrotor dynamics separately. The proposed FCRNN architecture consists of output feedback to the input layer and the hidden layer, which enhances the approximation capability of FCRNN. All hidden layer neurons receive self-feedback and feedback from other hidden layer neurons, which further strengthens FCRNN’s potential to capture complex dynamic characteristics. As learning should happen in finite time, the finite-time stability of the overall system has been guaranteed using the Lyapunov stability theory, and the update laws for FCRNN weights in real time are derived using the same. To show the effectiveness of the proposed approach, a comprehensive analysis has been done against existing SMC strategy and against well-known function approximation techniques, e.g., the radial basis function network (RBFN) and RNN. [ABSTRACT FROM AUTHOR]

In this work, an adaptive learning model predictive control (ALMPC) scheme is proposed for the trajectory tracking of perturbed autonomous ground vehicles (AGVs) subject to input constraints. In order to estimate the unknown system parameter, we propose a set-membership-based parameter estimator based on the recursive least-squares (RLS) technique with the ensured nonincreasing estimation error. Then, the estimated system parameter is employed in MPC to improve the prediction accuracy. In the proposed ALMPC scheme, a robustness constraint is introduced into the MPC optimization to handle parametric and additive uncertainties. For the designed robustness constraint, its shape is decided off-line based on the invariant set, whereas its shrinkage rate is updated online according to the estimated upper bound of the estimation error, leading to further reduced conservatism and slightly increased computational complexity compared with the robust MPC methods. Furthermore, it is theoretically shown that the proposed ALMPC algorithm is recursively feasible under some derived conditions, and the closed-loop system is input-to-state stable (ISS). Finally, a numerical example and comparison study are conducted to illustrate the efficacy of the proposed method. [ABSTRACT FROM AUTHOR]

This article presents a control scheme for the robot manipulator’s trajectory tracking task considering output error constraints and control input saturation. We provide an alternative way to remove the feasibility condition that most BLF-based controllers should meet and design a control scheme on the premise that constraint violation possibly happens due to the control input saturation. A bounded barrier Lyapunov function is proposed and adopted to handle the output error constraints. Besides, to suppress the input saturation effect, an auxiliary system is designed and emerged into the control scheme. Moreover, a simplified RBFNN structure is adopted to approximate the lumped uncertainties. Simulation and experimental results demonstrate the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

This paper proposes a trajectory tracking control strategy for Skid-Steered wheeled UGVs based on a simplified controller design and robust analysis. The position control loops are decoupled by using a virtual orientation angle so that the analysis and design problems can be handled independently as disturbed first-order control systems. The transformed model is useful because it simplifies the robust stability analysis and the definition of the conditions that ensure velocity constraint satisfaction. Furthermore, the proposed analytical control law does not require the solution of any online optimization problem. The usefulness and the design simplicity of the proposed control strategy are illustrated in an experimental case study performed with a Clearpath Husky UGV. • A novel approach for trajectory control of Skid-Steer UGVs has been proposed. • Linear robust design rules are provided for the new analytical control law. • Robust stability and constrains satisfaction can be ensured during navigation. [ABSTRACT FROM AUTHOR]

The aim of this article is to investigate the trajectory tracking problem of systems with uncertain models and state restrictions using differential neural networks (DNNs). The adaptive control design considers the design of a nonparametric identifier based on a class of continuous artificial neural networks (ANNs). The design of adaptive controllers used the estimated weights on the identifier structure yielding a compensating structure and a linear correction element on the tracking error. The stability of both the identification and tracking errors, considering the DNN, uses a barrier Lyapunov function (BLF) that grow to infinity whenever its arguments approach some finite limits for the state satisfying some predefined ellipsoid bounds. The analysis guarantees the semi-globally uniformly ultimately bounded (SGUUB) solution for the tracking error, which implies the achievement of an invariant set. The suggested controller produces closed-loop bounded signals. This article also presents the comparison between the tracking states forced by the adaptive controller estimated with the DNN based on BLF and quadratic Lyapunov functions as well. The effectiveness of the proposal is demonstrated with a numerical example and an implementation in a real plant (mass-spring system). This comparison confirmed the superiority of the suggested controller based on the BLF using the estimates of the upper bounds for the system states. [ABSTRACT FROM AUTHOR]

The definition of asymptotic stability for a trajectory of a hybrid system with state-triggered jumps is not straightforward. Nearby solutions jump at close but noncoincident times, making the standard notion of closeness, based on vector difference, unsuitable to compare trajectories point-wise in time. With tracking control as ultimate goal, we propose a notion of stability and a constructive stability proof based on sensitivity analysis applicable to single-jump-flow trajectories. A key role in the analysis is played by a time-triggered linear system, associated with the discontinuous trajectory of interest, whose uniform asymptotic stability suffices to guarantee the asymptotic stability of the original discontinuous trajectory. As an illustrative example, the stability analysis is applied to guarantee closed-loop stable tracking for a trajectory with velocity jumps of a 2 DoF mechanical system with unilateral constraint. [ABSTRACT FROM AUTHOR]

• An SMC scheme for trajectory tracking of mechanical systems subject to quantization and event trigger is established for the first time. The quantized states are employed to determine whether or not the control execution should be triggered and transmitted to the actuator so that communication resources can be saved. • We show that quantization affects the designed triggering threshold and give the upper bounds of the quasi-sliding mode that is induced by both quantization and event trigger. • The proposed algorithm can guarantee uniform ultimate boundedness of the tracking error and ensure the existence of a minimal time between consecutive executions of the control task. This paper addresses the problem of the quantization-based event-triggered sliding mode tracking control for mechanical systems. Only the quantized values of measurement outputs and control inputs are transmitted to the controller and actuator sides, respectively. Furthermore, the event-triggering mechanism is located in the controller side and specific events are monitored by using quantized states to determine whether or not the control command should be transmitted. The quantization effect on the selected triggering threshold is analyzed. By using the sliding mode control (SMC) method, the sufficient condition for the stability of the system is given. The quasi-sliding mode band that is caused by quantization and event trigger is introduced for the first time. The proposed method guarantees the uniform ultimate boundedness of the tracking error and ensures the non-accumulation of inter-execution times. Simulation results illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper studies the problem of trajectory tracking control for a class of vehicles (underwater vehicles, some horizontally moving vehicles, indoor airships). The control development is based on some velocity transformation arising from the inertia matrix decomposition, Lyapunov's direct method and a non-adaptive nonlinear tracking controller in terms of the Generalized Velocity Components (GVC). In the nonlinear controller the control gains are strictly related to the vehicle dynamics (especially dynamical couplings). The general algorithm is presented for a 6 DOF vehicle. In the simulation two trajectories were tested. Moreover, one robustness test was done (corresponding to robustness issue considered in this work). The simulation results obtained for a full airship model show that the proposed control scheme guarantees satisfactory performance. [ABSTRACT FROM AUTHOR]

A new approach for trajectory tracking on uncertain complex networks is proposed. To achieve this goal, a neural controller is applied to a small fraction of nodes (pinned ones). Such controller is composed of an on-line identifier based on a recurrent high-order neural network, and an inverse optimal controller to track the desired trajectory; a complete stability analysis is also included. In order to verify the applicability and good performance of the proposed control scheme, a representative example is simulated, which consists of a complex network with each node described by a chaotic Lorenz oscillator. [ABSTRACT FROM AUTHOR]

This paper addresses the trajectory tracking control problem for underactuated unmanned aerial vehicles (UAVs), with specific focus on vehicles with thrust-vectoring capabilities. According to the different actuation mechanisms, the most common UAV platforms can achieve only a partial decoupling of attitude and position tasks. Since position tracking is of utmost importance for applications involving aerial vehicles, we propose a control scheme in which position tracking is the primary objective. To this end, this paper exploits the concept of attitude planner, a dynamical system through which the desired attitude reference is processed to guarantee the satisfaction of the primary objective: The attitude tracking task is considered as a secondary objective, which can be realized as long as the desired trajectory satisfies specific trackability conditions. [ABSTRACT FROM AUTHOR]

This paper investigates trajectory tracking problem for a class of underactuated autonomous underwater vehicles (AUVs) with unknown dynamics and constrained inputs. Different from existing policy gradient methods which employ single actor critic but cannot realize satisfactory tracking control accuracy and stable learning, our proposed algorithm can achieve high-level tracking control accuracy of AUVs and stable learning by applying a hybrid actors-critics architecture, where multiple actors and critics are trained to learn a deterministic policy and action-value function, respectively. Specifically, for the critics, the expected absolute Bellman error-based updating rule is used to choose the worst critic to be updated in each time step. Subsequently, to calculate the loss function with more accurate target value for the chosen critic, Pseudo Q-learning, which uses subgreedy policy to replace the greedy policy in Q-learning, is developed for continuous action spaces, and Multi Pseudo Q-learning (MPQ) is proposed to reduce the overestimation of action-value function and to stabilize the learning. As for the actors, deterministic policy gradient is applied to update the weights, and the final learned policy is defined as the average of all actors to avoid large but bad updates. Moreover, the stability analysis of the learning is given qualitatively. The effectiveness and generality of the proposed MPQ-based deterministic policy gradient (MPQ-DPG) algorithm are verified by the application on AUV with two different reference trajectories. In addition, the results demonstrate high-level tracking control accuracy and stable learning of MPQ-DPG. Besides, the results also validate that increasing the number of the actors and critics will further improve the performance. [ABSTRACT FROM AUTHOR]

This paper presents iterative sequential action control (iSAC), a receding horizon approach for control of nonlinear systems. The iSAC method has a closed-form open-loop solution, which is iteratively updated between time steps by introducing constant control values applied for short duration. Application of a contractive constraint on the cost is shown to lead to closed-loop asymptotic stability under mild assumptions. The effect of asymptotically decaying disturbances on system trajectories is also examined. To demonstrate the applicability of iSAC, we employ a variety of systems and conditions, including a 13-dimensional quaternion-based quadrotor and NASA's Transition Region and Coronal Explorer (TRACE) spacecraft. Each system is tested in different scenarios, ranging from feasible and infeasible trajectory tracking to setpoint stabilization, with or without the presence of external disturbances. Finally, limitations of this paper are discussed. [ABSTRACT FROM AUTHOR]

In this paper, we consider a multi-input driftless bilinear system evolving on the $n$ -dimensional sphere $S^{n}$. We first provide examples drawn from rigid body mechanics that provide the motivation for the control of bilinear systems on $S^{n}$. For the general framework, we establish the global controllability on $S^{n}$ and propose two linear control laws on $S^{n}$ that achieve asymptotic stabilization of an equilibrium point with an almost global domain-of-attraction. Further, the asymptotically stable closed-loop system trajectories are shown to be arcs on the geodesics of $S^{n}$ for a particular choice of the equilibrium point. Next, we propose two linear time-varying control laws to achieve trajectory tracking on $S^{n}$ and show the asymptotic stability of the tracking error. A distributed control is designed for the consensus of multiagent bilinear systems on $S^{n}$ with an undirected tree as the communication graph. The consensus manifold is shown to have an almost global domain of attraction. [ABSTRACT FROM AUTHOR]

In this technical note, a novel approach to trajectory tracking control of a complex network is presented, based on the idea and technique of inverse optimal pinning control. A control law that stabilizes the tracking error dynamics is developed. This controller allows different coupling strengths, and simultaneously minimizing an associated cost functional. Chaotic systems are considered to illustrate the applicability of the new methodology and the proposed controller effectiveness via simulations. [ABSTRACT FROM AUTHOR]

An attitude motion tracking control algorithm is studied via the indirect Legendre pseudospectral method for the asym metric underactuated rigid spacecraft equipped with only two pairs of jet thrusters. F irstly, the Legendre pseudospectral m ethod ( LPM ) is employed to plan a reference trajectory of the m in-tim e attitude m aneuver for the system. Secondly, defining the deviation between the actual and reference trajectories as the variable, and then the attitude motion tracking problem can be transform ed into a two-point boundary value problem (T P B V P ) according to the necessary condition of the Pontryagin m inim al value theory. At la st, the Legendre-G auss-Lobatto ( LGL) points are utilized to transfer the TPBVP into a system of linear equations to be solved. It avoids the integral operating for the traditional Riccati differential equations. The effectiveness of the attitude motion tracking control algorithm based on indirect LPM for an initial disturbance in the underactuated spacecraft is verified through the num erical sim ulation. [ABSTRACT FROM AUTHOR]

F or th e p ro blem o f b a llistic reen try targ et trajectory tra c k in g , th e sq u are-ro o t cu b a tu re q u ad ratu re sam p ling p a rtic le filter algorithm b ased on hy brid pro po sal d istrib u tio n ( H P D -S R C Q S P F ) is pro po sed . T h e p ro po sed algorithm c o n sists of two b asic pro po sal d istrib u tio n s. T h e priori d istrib u tio n is u sed a s a b asic pro po sal d istrib u tio n , a n d th e sq u are root cu b a tu re q u ad ratu re K alm an filter is u sed as th e o th er b asic p ro po sal d istrib u tio n . T h e hybrid pro po sal d istrib u tio n is clo se to th e po sterio r d istrib u tio n , so it h as th e c h a rac teristic s of high efficiency a n d high p recisio n . S im u lation re su lts show th at for th e b a llistic reen try target trajectory tra ck in g m o d el, com pared w ith th e stan d a rd p a rticle filter ( S P F ) algorithm and th e sq u are-ro o t cu b a tu re q u ad ratu re p a rticle filte r (S R C Q P F ) alg o rith m , th e H P D -S R C Q S P F algorithm not only h as a low co m pu tational c o m p le x ity , b u t also h as very good tra ck in g accu racy . E sp ecially in th e case of a b a llistic targ et m an eu v e r, th e pro po sed algorithm can o b tain h ig h er estim atio n accu racy at low er com pu tational cost. [ABSTRACT FROM AUTHOR]

We study opinion dynamics systems controlled by a leader. Following findings from experimental social psychology, the influence weights considered here have the peculiarity to be both state and time dependent. This allows for instance to model loss of patience or interest of individuals. The opinion of the leader is assumed to be controlled using a saturated control. The objective of the leader is to make all agents agree on a desired consensus value. First, all agents are gathered around the leader, which then steers all agents toward the target consensus value, where consensus is achieved. Trajectory tracking is also investigated. The time-dependent interactions have a significant impact on the control law. To achieve the control, the time-dependent term in the interactions is assumed to be persistent in time. Our results are illustrated by numerical simulation throughout the paper. [ABSTRACT FROM PUBLISHER]

The various uncertainties of Mars environment have great impact on the process of vehicles entering the atmosphere. To improve the robustness of control system against the model errors and to reduce the computational burden, an optimal feedback based tracking control law is developed. The control scheme presented in this paper determines the amplitude and the reversals of bank angle respectively in the longitudinal and lateral flight plane. At each control cycle, the amplitude of the bank angle is obtained by an optimal feedback controller to minimize tracking errors. The control gains are tuned according to the closed-loop error dynamics by using optimization methods. The bank reversals are executed if the crossrange exceeds a predetermined corridor which is designed by setting a boundary function. The accuracy and robustness of the proposed closed-loop optimal feedback based control law in tracking the reference trajectory is verified via 500 deviation simulations, in which modeling errors and external disturbances are considered. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the multiparty output consensus of linear heterogeneous agents, in which the outputs of the agents within a party synchronize to each other, but different parties have different synchronized trajectories. To solve this problem, graphs with complex edge weights are used. We provide a sufficient condition for leader-follower multiparty output consensus of linear heterogeneous agents. We also give a similar sufficient condition for multiparty state consensus of linear homogeneous agents as homogeneous agents could be considered as a special case of heterogeneous agents. As one direct application of our theory, we provide a solution to formation control of linear heterogeneous agents, where the position of each follower agent in the formation is described by two key pieces of information: A fixed orientation of the follower's location with respect to the leader's location (within a given coordinate system), and a time-variant formation constraint (such as the relative distance) between the leader and the follower, which is captured by some linear command generator. We present sufficient conditions for both simultaneous formation and reference trajectory tracking via the internal model principle. [ABSTRACT FROM PUBLISHER]

The robust trajectory tracking of fully actuated Lagrange systems is studied. Exogenous perturbations as well as parameter uncertainties are taken into account. A family of set-valued passivity-based controllers is proposed, including first-order sliding-mode schemes. The existence of solutions and the stability of the closed-loop system are established in continuous time. An implicit discretization approach is proposed and the well posedness and the stability of the closed-loop system are studied. Numerical simulations illustrate the effectiveness of the proposed discrete-time controller. [ABSTRACT FROM AUTHOR]

This paper focuses on the adaptive trajectory tracking control for a remotely operated vehicle (ROV) with an unknown dynamic model and the unmeasured states. Unlike most previous trajectory tracking control approaches, in this paper, the velocity states and the angular velocity states in the body-fixed frame are unmeasured, and the thrust model is inaccurate. Obviously, it is more in line with the actual ROV systems. Since the dynamic model is unknown, a new local recurrent neural network (local RNN) structure with fast learning speed is proposed for online identification. To estimate the unmeasured states, an adaptive terminal sliding-mode state observer based on the local RNN is proposed, so that the finite-time convergence of the trajectory tracking error can be guaranteed. Considering the problem of inaccurate thrust model, an adaptive scale factor is introduced into thrust model, and the thruster control signal is considered as the input of the trajectory tracking system directly. Based on the local RNN output, the adaptive scale factor, and the state estimation values, an adaptive trajectory tracking control law is constructed. The stability of the trajectory tracking control system is analyzed by the Lyapunov theorem. The effectiveness of the proposed control scheme is illustrated by simulations. [ABSTRACT FROM AUTHOR]

The model of reentry vehicle is strongly coupling, nonlinear, fast time-varying and uncertain. And its flight environment is complex and highly variable. To eliminate these bad effects in the design of control and guidance, a global sliding mode based tracking guidance law with fixed-time convergence is investigated in this paper. Firstly, the three- degree-of-freedom reentry guidance model is proposed. And we take the longitude as the dependent variable of the interpolation instead of the time in the standard reference trajectory. So the trajectory of the three variables is transformed into the tracking of the height and latitude. Then, a global sliding mode based tracking guidance law with fixed-time convergence is designed for the trajectory tracking matters. Finally, some simulations are shown to illustrate the effectiveness of the trajectory-tracking guidance algorithm. [ABSTRACT FROM AUTHOR]

Research on stabilization of coupled hyperbolic PDEs has been dominated by the focus on pairs of counter-convecting (“heterodirectional”) transport PDEs with distributed local coupling and with controls at one or both boundaries. A recent extension allows stabilization using only one control for a system containing an arbitrary number of coupled transport PDEs that convect at different speeds against the direction of the PDE whose boundary is actuated. In this paper we present a solution to the fully general case, in which the number of PDEs in either direction is arbitrary, and where actuation is applied on only one boundary (to all the PDEs that convect downstream from that boundary). To solve this general problem, we solve, as a special case, the problem of control of coupled “homodirectional” hyperbolic linear PDEs, where multiple transport PDEs convect in the same direction with arbitrary local coupling. Our approach is based on PDE backstepping and yields solutions to stabilization, by both full-state and observer-based output feedback, and trajectory tracking problems. [ABSTRACT FROM PUBLISHER]

Limited on-board energy resources of autonomous underwater vehicles (AUVs) demand design of an appropriate controller to achieve optimal energy consumption while tracking a commanded path accurately for some envisaged applications. The unstructured oceanic environment calls for a robust control law which is capable of handling parametric uncertainties and environmental disturbances. Though switching surface control has been proven to be an effective strategy for underwater operations, it provides no scope for energy optimization. For an energy critical mission, it is desirable to minimize the net control effort through advanced mathematical modelling, even at the cost of compromising accuracy within a reasonable bound. With this aspect in mind, the present work addresses these two issues (i.e. energy and accuracy) together through design of a novel controller based on sliding mode control in association with Euler–Lagrange based classical optimal control. Mathematical modelling and simulation results are presented to demonstrate the effectiveness of the proposed controller with real life parameters of an experimentally validated AUV, designed and developed for 150 m depth of operation at sea. [ABSTRACT FROM AUTHOR]

The free-floating manipulator system with non-zero initial momentum was studied. The configuration parameters of the manipulator system and the Lagrange equations were used to build the dynamic model. In order to eliminate the coupling effects between the base and the manipulators in angular velocity, the zero reaction motion equation was derived by using the manipulator system redundancy. A polynomial interpolation method was used to approach the trajectory of end-effector and the trajectory was planned due to the initial and final position of end-effector which could turn workspace of end-effector into joints space. A suitable proportion- differentiation (PD) control algorithm was designed to drive manipulators movement. Simulation results demonstrate that the end-effector can smoothly track the target and the movement of the manipulator doesn't make any disturbance to the base's attitude at the same time and the spacecraft also can work normally because of the stability of base. [ABSTRACT FROM AUTHOR]

This technical note is about trajectory tracking for discrete event systems. The main contribution is to compute incrementally control sequences with minimal or near-minimal length based on a partial exploration of the Petri net (PN) reachability graph and inspired by the model predictive control approach. The method is suitable for bounded and unbounded PNs, for PNs with weighted arcs and with some uncontrollable transitions. Finally forbidden markings or regions of markings are easily avoided with the proposed approach that can be used for supervisory control issues. [ABSTRACT FROM AUTHOR]

This paper develops a new control approach for trajectory tracking of mobile robots. For the purpose of tracking trajectory, the error dynamics of a mobile robot are divided into a first-order subsystem and a second-order subsystem by using a cascaded control design. Firstly, a global finite-time control law of the angular velocity is designed for the first-order system in order to stabilize the angle error of mobile robots. Subsequently, a finite-time sliding mode control law of forward velocity is synthesized, which guarantees the global stability of the second-order subsystem. Furthermore, the global uniform stability of the whole closed-loop system is analyzed by employing cascaded control theory, and some sufficient conditions are derived. Finally, the proposed control algorithm is applied to mobile robots, where simulation results demonstrate good convergence and performance. [ABSTRACT FROM AUTHOR]

A new reactionless space robot design concept was put forward to overcome the shortcomings of the current drive types. Control moment gyros (CMGs) were mounted on each part mechanical arm and system platform as movement actuators. Mechanical arms were connected with spherical hinges. Platform attitude and angular displacement were measured by the Rodrigues parameters (RPs). Based on system dynamics model built with Kane method, a gradually stable trajectory tracking control law was designed to track the platform attitude and mechanical arm angular displacement, and a CMGs steering law was designed to output exact expected control torque. Moreover, the designed control laws can be further extended to track and control the workspace trajectory by studying the trajectory planning algorithms between the mechanical arms workspace and joints space. The simulation results from the three joints systems have verified the feasibility of design and effectiveness of the proposed trajectory algorithms. [ABSTRACT FROM AUTHOR]

The existing Kalman filters for quaternion-valued signals do not operate fully in the quaternion domain, and are combined with the real Kalman filter to enable the tracking in 3-D spaces. Using the recently introduced HR-calculus, we develop the fully quaternion-valued Kalman filter (QKF) and quaternion-extended Kalman filter (QEKF), allowing for the tracking of 3-D and 4-D signals directly in the quaternion domain. To consider the second-order noncircularity of signals, we employ the recently developed augmented quaternion statistics to derive the widely linear QKF (WL-QKF) and widely linear QEKF (WL-QEKF). To reduce computational requirements of the widely linear algorithms, their efficient implementation are proposed and it is shown that the quaternion widely linear model can be simplified when processing 3-D data, further reducing the computational requirements. Simulations using both synthetic and real-world circular and noncircular signals illustrate the advantages offered by widely linear over strictly linear quaternion Kalman filters. [ABSTRACT FROM AUTHOR]

This paper presents a discrete-time inverse optimal neural controller, which is constituted by combination of two techniques: 1) inverse optimal control to avoid solving the Hamilton–Jacobi–Bellman equation associated with nonlinear system optimal control and 2) on-line neural identification, using a recurrent neural network trained with an extended Kalman filter, in order to build a model of the assumed unknown nonlinear system. The inverse optimal controller is based on passivity theory. The applicability of the proposed approach is illustrated via simulations for an unstable nonlinear system and a planar robot. [ABSTRACT FROM AUTHOR]

Abstract: This paper reports the creation of the HILS setup to simulate the performance of the AUS–UAV system and the Ground Station. The paper will give an overview of the integration of the Ground Station created using a guide, the Avionics Unit created using an embedded system, integrated with various sensors, and the simulator created using simulink/flight simulator combination. The simulation will show the Ground Station and the embedded system working cooperatively to guide the UAV through several mission waypoints in three dimensions (3D). The paper focuses on the algorithm used to create paths in 3D, and also the organization of waypoints to achieve a specific goal. The paper also details the testing of the HILS. [ABSTRACT FROM AUTHOR]

The natural trajectory tracking problem is studied for generic quantum states represented by density operators. A control design based on the Hilbert-Schmidt distance as a Lyapunov function is considered. The control dynamics is redefined on an extended space where the LaSalle invariance principle can be correctly applied even for non-stationary target states. LaSalle's invariance principle is used to derive a general characterization of the invariant set, which is shown to always contain the critical points of the Lyapunov function. Critical point analysis of the latter is used to show that, for generic states, it is a Morse function with n! isolated critical points, including one global minimum, one global maximum and n!-2 saddles. It is also shown, however, that the actual dynamics of the system is not a gradient flow, and therefore a full eigenvalue analysis of the linearized dynamics about the critical points of the dynamical system is necessary to ascertain stability of the critical points. This analysis shows that a generic target state is locally asymptotically stable if the linearized system is controllable and the invariant set is regular, and in fact convergence to the target state (trajectory) in this case is almost global in that the stable manifolds of all other critical points form a subset of measure zero of the state space. On the other hand, if either of these sufficient conditions is not satisfied, the target state ceases to be asymptotically stable, a center manifold emerges around the target state, and the control design ceases to be effective. [ABSTRACT FROM PUBLISHER]

This note presents simple controllers for achieving asymptotic trajectory (set-point and time-varying) tracking under the conditions of restricted input in vertical takeoff and landing aircraft. Unlike previous studies that apply nonlinear combination of saturated functions for stabilizing the closed-loop system, the proposed controllers are based on smooth functions that can easily be implemented in real time applications. [ABSTRACT FROM AUTHOR]

A control approach is proposed for a class of under-actuated vehicles in order to stabilize reference trajectories either in thrust direction, velocity, or position. The basic modeling assumption is that the vehicle is propulsed via a thrust force along a single body-fixed direction and that it has full torque actuation for attitude control (i.e., a typical actuation structure for aircrafts, Vertical Take-Off and Landing (VTOL) vehicles, submarines, etc.). Additional assumptions on the external forces applied to the vehicle are also introduced for the sake of control design and stability analyses. They are best satisfied for vehicles which are subjected to an external force field (e.g., gravity) and whose shape induces lift forces with limited amplitude, unlike airplanes but as in the case of many VTOL drones. The interactions of the vehicle with the surrounding fluid are often difficult to model precisely whereas they may significantly influence and perturb its motion. By using a standard Lyapunov-based approach, novel nonlinear feedback control laws are proposed to compensate for modeling errors and perform robustly against such perturbations. Simulation results illustrating these properties on a realistic model of a VTOL drone subjected to wind gusts are reported. [ABSTRACT FROM AUTHOR]

Abstract-This paper is concerned with off-line reference shaping of infinite horizon periodic trajectories for uncertain closed-loop systems with state/input constraints. The method allows us to improve tracking performance subject to constraints in the presence of uncertainties. First, the reference shaping for nominal systems is proposed, which yields a suboptimal solution through decomposition of the infinite horizon tracking problem into two finite ones. Second, the method is extended to fulfill the constraints robustly. [ABSTRACT FROM AUTHOR]

Abstract: A vehicle system driven by two independent DC motors is presented here, one of which is used for the right wheel and the other is used for the left wheel. An adaptive compensator using Takagi–Sugeno fuzzy systems is proposed to control the vehicle system. The compensator includes an adaptive model identifier and adaptive controller. An online method is used to adjust the parameters of the identifier model to match the behavior model of the vehicle system. Then, the parameters of the identifier model are employed in a standard parallel-distributed compensator to provide asymptotically stable equilibrium for the closed-loop vehicle drive system, in which the velocity and direction angle of the vehicle are controlled. Results demonstrate that the proposed controller structure is robust to load changes and follows different trajectories very well. [Copyright &y& Elsevier]