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Object tracking is one of the major tasks for mobile robots in many real-world applications. Also, artificial intelligence and automatic control techniques play an important role in enhancing the performance of mobile robot navigation. In contrast to previous simulation studies, this paper presents a new intelligent mobile robot for accomplishing multi-tasks by tracking red-green-blue (RGB) colored objects in a real experimental field. Moreover, a practical smart controller is developed based on adaptive fuzzy logic and custom proportional-integral-derivative (PID) schemes to achieve accurate tracking results, considering robot command delay and tolerance errors. The design of developed controllers implies some motion rules to mimic the knowledge of experienced operators. Twelve scenarios of three colored object combinations have been successfully tested and evaluated by using the developed controlled image-based robot tracker. Classical PID control failed to handle some tracking scenarios in this study. The proposed adaptive fuzzy PID control achieved the best accurate results with the minimum average final error of 13.8 cm to reach the colored targets, while our designed custom PID control is efficient in saving both average time and traveling distance of 6.6 s and 14.3 cm, respectively. These promising results demonstrate the feasibility of applying our developed image-based robotic system in a colored object-tracking environment to reduce human workloads. [ABSTRACT FROM AUTHOR]

Prescribed performance control allows preselected transient and steady‐state bounds of formation control performance. However, owing to the predetermined decaying property of performance functions, it is unavailable for the formation tracking problem with dynamic obstacle avoidance. Thus, it is essential to design a flexible performance function to accommodate the increasing formation error during dynamic obstacle avoidance. This article pays attention to the development of an adaptive flexible performance (AFP) function usable for the avoidance of dynamic obstacles with unknown velocities in the distributed formation framework. We develop an AFP‐based distributed formation tracker design for range‐constrained switched multi‐input multi‐output nonlinear multiagent systems with asynchronous switching. The AFP functions for ensuring graph connectivity and dynamic obstacle avoidance are derived by designing time‐varying variables adjusted by an adaptive relaxation signal. The neural‐network‐based adaptive distributed formation tracker using the derived performance functions is constructed with the adaptive relaxation signal to ensure the stability of the closed‐loop formation system, regardless of asynchronous switching and dynamic obstacles. The effectiveness of the proposed method is shown by simulation results. [ABSTRACT FROM AUTHOR]

This paper deals with modelling and robust control of linear multivariable uncertain systems based on the new linear multivariable ARX (Auto-Regressive with eXogenous input)-Laguerre model and the loop-shaping design procedure with the relative gain array theory. In fact, to guarantee significant parameter number reduction of the multivariable ARX-Laguerre model compared with the classic multivariable ARX model, we propose the optimization of the Laguerre poles of the model using the genetic algorithm where the Fourier coefficients are identified using the recursive least square method. Later, the optimized multivariable ARX-Laguerre model is exploited to propose a robust control algorithm for linear multivariable uncertain systems. Indeed, we propose to combine the loop-shaping design procedure approach and the relative gain array theory to develop a simplified final robust controller guaranteeing reference tracking and robust stability against parametric uncertainties. This proposition aims for the simplification of the control's scheme by adjusting the weighting matrix and the final robust controller calculated from the loop-shaping design procedure for a simpler and more experimentally applicable control scheme. The identification of Fourier coefficients and Laguerre poles as well as the robust multivariable control algorithm are experimentally validated on a laboratory coupled two-tank system showing good results in terms of modelling and attending desired control performances. [ABSTRACT FROM AUTHOR]

This paper concludes a robust optimal tracking control law for a class of nonlinear systems. A characteristic of this paper is that the designed controller can guarantee both robustness and optimality under nonlinearity and mismatched disturbances. Optimal controllers for nonlinear systems are difficult to obtain, hence a reinforcement learning method is adopted with two neural networks (NNs) approximating the cost function and optimal controller, respectively. We designed weight update laws for critic NN and actor NN based on gradient descent and stability, respectively. In addition, matched and mismatched disturbances are estimated by fixed-time disturbance observers and an artful transformation based on backstepping method is employed to convert the system into a filtered error nonlinear system. Through a rigorous analysis using the Lyapunov method, we demonstrate states and estimation errors remain uniformly ultimately bounded. Finally, the effectiveness of the proposed method is verified through two illustrative examples. [ABSTRACT FROM AUTHOR]

Emulsion pump station is widely used to provide power for full-mechanized coal mining face equipment. With the development of the mining technique of longwall face and the extensive use of large mining height and high working resistance of the hydraulic support, the fluid supply pressure is more unstable and the pressure impact is increasingly intensified. The intelligent control of the emulsion pump station is the key to the development of the stable fluid supply technology at the working face. This paper develops a new on-line adaptive learning technique to improve the response speed of pumping station based on the optimal control algorithm of adaptive dynamic programming (ADP). The state space model of the working face fluid supply system in the process of no-load column lifting of hydraulic support is first established using the flow relationship, and the effectiveness of the proposed control algorithm is then verified by simulation validation. Moreover, combined with a certain fluid supply system of a fully mechanized caving face in Shandong Energy Xinwen Group XinJulong Co., Ltd, a technical scheme of adaptive stable fluid supply is verified in engineering practice. The results indicate that the fluid supply system using the proposed control algorithm has a favorable tracking effect on the ideal trajectory, and the tracking error can converge to near zero. The output pressure of the fluid supply system using the stable fluid supply technology is more stable, and the practical effect is good, which meets the demand of stable fluid supply in the mining face. [ABSTRACT FROM AUTHOR]

Although optimal regulation problem has been well studied, resolving optimal tracking control via adaptive dynamic programming (ADP) has not been completely resolved, particularly for nonlinear uncertain systems. In this paper, an online adaptive learning method is developed to realize the optimal tracking control design for nonlinear motor driven systems (NMDSs), which adopts the concept of ADP, unknown system dynamic estimator (USDE), and prescribed performance function (PPF). To this end, the USDE in a simple form is first proposed to address the NMDSs with bounded disturbances. Then, based on the estimated unknown dynamics, we define an optimal cost function and derive the optimal tracking control. The derived optimal tracking control is divided into two parts, that is, steady‐state control and optimal feedback control. The steady‐state control can be obtained with the tracking commands directly. The optimal feedback control can be obtained via the concept of ADP based on the PPF; this contributes to improving the convergence of critic neural network (CNN) weights and tracking accuracy of NMDSs. Simulations are provided to display the feasibility of the designed control method. [ABSTRACT FROM AUTHOR]

Summary: This article formulates and solves a problem of model following adaptive control (MFAC) of nodes in the complex dynamic network (CDN) with the dynamic matrix‐relation links. From the perspective of composite systems, the CDN with the dynamic matrix‐relation links is considered as an interconnected system composed of intercoupling node subsystem (NS) and link subsystem (LS). Based on this idea, employing the Riccati matrix differential equation as the dynamic equation of LS, and the vector differential equation with the second derivative term as the dynamic equation of NS. Then, to remove the restriction that the state of LS is unavailable, an adaptive control scheme is proposed for NS with the help of constructed dynamic auxiliary tracking target for LS, such that the problem of MFAC of nodes is solved. Besides, it is proved mathematically that the state of NS is asymptotically tracking the state of the given reference model when the state of LS is asymptotically tracking the constructed dynamic auxiliary tracking target. Finally, the numerical simulation is given to verify the effectiveness of the theoretical results in this article. [ABSTRACT FROM AUTHOR]

Frequent state monitoring and controller updates can enhance the precision of tracking control, while simultaneously overburdening the communication network transmission. In this paper, For the purpose of saving communication costs, we propose event-triggered control algorithms for the optimal tracking control problem. First, we reconstruct the discrete-time nonlinear system into a converted system. Then, the adaptive dynamic programming algorithm is employed to find the optimal controller off-line, and the event-triggered scheme is used to reduce the communication costs online. Novel triggering conditions with fewer assumptions are designed to implement the event-triggered scheme. Different from existing works, the event-triggered scheme can be introduced not only into the converted system but also into the actual system, which is more practical because the actual controller is what one can only access in practice. In addition, with the developed algorithms, the tracking error can be proved to be stable at the origin, in other words, the actual system can be guaranteed to track the desired trajectory. Algorithms developed in this paper are implemented by three neural networks, the model network, the action network and the critic network. Finally, examples are presented to verify the effectiveness and rationality of the algorithms. [ABSTRACT FROM AUTHOR]

Usually, it is difficult to build accurate dynamic models for real robots, which makes safety‐critical control a challenge. In this regard, this article proposes a double‐level framework to design safety‐critical controller for robotic systems with uncertain dynamics. The high level planner plans a safe trajectory for low level tracker based on the control barrier function (CBF). First, the high level planning is done independently of the dynamic model by quadratic programs subject to CBF constraint. Afterward, a novel method is proposed to learn the uncertainty of drift term and input gain in nonlinear affine‐control system by a data‐driven Gaussian process (GP) approach, in which the learning result of uncertainty in input gain is associated with CBF. Then, a Gaussian processes‐based control barrier function (GP‐CBF) is designed to guarantee the tracking safety with a lower bound on the probability for the low level tracker. Finally, the effectiveness of the proposed framework is verified by the numerical simulation of UR3 robot. [ABSTRACT FROM AUTHOR]

The nodes and links connecting them are the two main geometric elements of a complex network. If the nodes and links change over time, all the nodes and all the links are regarded the dynamic systems, respectively, and thus, a complex dynamical network (CDN) can be regarded to be composed of the two interconnected subsystems: the nodes subsystem (NS) and the links subsystem (LS). The outer synchronization happens in two CDNs, which means that the state error between the two NSs converges to zero. However, the dynamics of LS is ignored in the existing literature about the outer synchronization. In addition, the existing literature rarely consider the unknown interaction between two CDNs into the outer synchronization. In view of this, this paper investigates the outer synchronization of two CDNs with the unknown interaction. By using the dynamics of LSs and synthesizing the adaptive control scheme, the outer synchronization is asymptotically achieved. The results in this paper show that the outer synchronization happens when the two LSs can track, respectively, the synthesized auxiliary dynamic tracking targets. Finally, the numerical simulation is given to show the effectiveness of the theoretical results in this paper. [ABSTRACT FROM AUTHOR]

In this paper, a pinning control scheme for adaptive trajectory tracking of networked nonlinear systems is proposed. The desired trajectory tracking objective is achieved via a novel adaptive control strategy, in which the existing concept of pinning control is extended to an adaptive pinning control. The adaptive control is based on a recurrent high‐order neural network identification strategy for unknown pinned nodes dynamics. The combined pinning‐neural features in tracking control constitute the novelty of this approach. The proposed control law operates properly with a different reference along with unknown dynamics and coupling variations. In contrast to the linearization approach, less restrictive assumptions for node dynamics are imposed. Moreover, matrix conditions that include network topology and control properties for both pinned and non‐pinned nodes are obtained. The applicability of this control scheme is illustrated via simulations. [ABSTRACT FROM AUTHOR]

This article presents an adaptive control system for active local reduction of broadband noise at moving locations, using the camera tracking for path model decomposition to reduce the filter lengths. To enable a dynamic placement of local quiet zones with respect to amoving target, previous works proposed the combination of the remotemicrophone techniquewith a camera-based target tracking. The estimated 3-D coordinates are used at run time for a dynamic update of the transfer paths models, which correspond to virtualmicrophones at predetermined locations. Nevertheless, whereas current applications like headrests feature only short distances to the target, the considered ANC system aims at larger ranges for the moving quiet zone, resulting in long impulse responses and high delays in the transfer paths. To reduce the associated model lengths, this work evaluates a decomposed identification stage that takes advantage of the camera-tracking system by estimating the acoustic path delays optically. In experiments, thismethod is shown to be superior to signal processing-based approaches like cross-correlation and thus enables an accurate separation of the delays. Eventually, the validation of the broadband ANC performance at a moving microphone shows that the quiet zone could be tracked over a range of 0.8 m, while using only few transfer path models and saving over 100 coefficients due to the proposed decomposition. [ABSTRACT FROM AUTHOR]

In this paper, formation control problem for multiple uncertain nonlinear second-order agents in the presence of heterogeneous communication delays is addressed. A continuous repulsive vector is incorporated into agents' velocity to ensure collision avoidance, whose time derivative is approximated via a finite-time robust integral of the sign of the error (RISE)-like observer. Using adaptive neural control scheme, unknown model dynamics, external disturbances, RISE estimation error, and the time-delays among agents are robustly addressed. The sufficient conditions on the stability of the overall system and collision avoidance are derived using Lyapunov–Krasovskii functionals and algebraic graph theory, and it is proven that the formation tracking error converges to a small neighborhood of zero. Numerical simulation results are also given to illustrate the effectiveness of our proposed methods. [ABSTRACT FROM AUTHOR]

In this paper, the cooperative adaptive fault-tolerant tracking control problem of high-order nonlinear multi-agent systems with signal transmission faults is studied. A neural network-based adaptive fault-tolerant control scheme is proposed, which guarantees that all followers asymptotically synchronize to a leader with tracking errors converging to a small neighborhood of the origin in spite of signal transmission faults. Based on algebraic graph theory and Lyapunov theory, the analysis of stability and parameter convergence of the proposed algorithm are conducted. Finally, an example is provided to validate the theoretical results. [ABSTRACT FROM AUTHOR]

• An ultrasonic source with unknown dynamics was tracked, given known assumptions. • The estimator is robust to errors from inaccurate measurements. • Increased source speed minimally effects accuracy of source position estimation. Sound source localization (SSL) is the ability to successfully estimate the bearing and distance of a sound in space relative to the sensing position and pose. SSL as a topic of interest for engineers often revolves around the ability of robots to track other robots, human voices, or other acoustic objects. Common approaches to this goal frequently use large arrays, computationally intensive and complex machine learning methods, or require known dynamic models of a system which may not always be available. In this work we seek to experimentally verify a solution to SSL using a minimal amount of inexpensive equipment on a two microphone, or stereo, sensing platform. A previously developed Bayesian estimator allows for localization of an emitter using easily available a priori information and timing data received from the sensor platform. Our results show that our approach is accurate for the tested paths and that the estimator can correct itself when dynamic assumptions are broken for short times due to hardware and software limitations. [ABSTRACT FROM AUTHOR]

This article designs a novel adaptive trajectory tracking controller for nonholonomic wheeled mobile robot under kinematic and dynamic uncertainties. A new velocity controller, in which kinematic parameter is estimated, produces velocity command of the robot. The designed adaptive sliding mode dynamic controller incorporates an estimator term to compensate for the external disturbances and dynamic uncertainties and a feedback term to improve the closed-loop stability and account for the estimation error of external disturbances. The system stability is analyzed using Lyapunov theory. Computer simulations affirm the robustness of the designed control scheme. [ABSTRACT FROM AUTHOR]

How to achieve a robust performance remains an intractable problem with the various object tracking algorithms, due to some unfavorable factors, e.g., occlusion, appearance change, etc. A robust object-tracking approach is proposed based on a composite similarity measure. Based on the first several frames in a video sequence, an initial dictionary is constructed by searching for the best candidates via the kd-tree method. For a target candidate, a likelihood value is computed by weighting the sparse representation coefficients of local patches obtained via the adaptive structural local sparse appearance (ASLSA) model. In order to alleviate the effect of occlusion, a smoothing operation is designed in computing the likelihood value. Meanwhile, after obtaining the codebook by applying the k-means clustering algorithm on the randomly sampled positive and negative templates, a confidence value is computed, via the sparse collaborative model (SCM), for each of the candidates. Furthermore, a composite similarity measure is devised, which combines the likelihood value and the confidence value for each candidate. Finally, the best candidate is searched for, according to the composite similarity measure. Due to the complementarity of SCM and ASLSA, the proposed algorithm is a more robust approach to object tracking. Experimental results on several challenging sequences demonstrate the effectiveness and feasibility of the proposed method. ©2018 SPIE and IS&T[DOI: 10.1117/1.JEI.27.4.043053] [ABSTRACT FROM AUTHOR]

Multiple autonomous mobile robot system has attracted increasing attention from scholars for its spatial and functional distributivity, high fault tolerance, strong robustness and many other advantages. Aiming at numerical instability, huge calculation amount, poor precision and other problems existing in the synergetic dynamic object tracking of multiple mobile robot in unknown complex environment, this paper proposes the covariance intersection multirobot object tracking algorithm based on self-adaption SR-CKF. The algorithm is distributed and it can improve the accuracy of the evaluation on relevant objects without independence assumption for data information, and thus avoids the evaluation of the cross correlation among objects’ status. In addition, targeting at bad observation information, the self-adaption SR-CKF is built on the basis of the information covariance matching principle, which has improved the robustness of the whole system. The simulation result has proved that this algorithm can effectively solve the problems in multirobot synergetic objects tracking in unknown environment. [ABSTRACT FROM AUTHOR]

This paper investigates how to integrate the complementary information from RGB and thermal (RGB-T) sources for object tracking. We propose a novel Convolutional Neural Network (ConvNet) architecture, including a two-stream ConvNet and a FusionNet, to achieve adaptive fusion of different source data for robust RGB-T tracking. Both RGB and thermal streams extract generic semantic information of the target object. In particular, the thermal stream is pre-trained on the ImageNet dataset to encode rich semantic information, and then fine-tuned using thermal images to capture the specific properties of thermal information. For adaptive fusion of different modalities while avoiding redundant noises, the FusionNet is employed to select most discriminative feature maps from the outputs of the two-stream ConvNet, and updated online to adapt to appearance variations of the target object. Finally, the object locations are efficiently predicted by applying the multi-channel correlation filter on the fused feature maps. Extensive experiments on the recently public benchmark GTOT verify the effectiveness of the proposed approach against other state-of-the-art RGB-T trackers. [ABSTRACT FROM AUTHOR]

This automated object tracking system is very essential in terms of surveillance system and many more applications. In this paper, the design of fuzzy proportional-integral-derivative controller is proposed to control a robot eye to achieve a real-time video object tracking. The controller uses vision-based object tracking as feedback and generates velocity commands for the robot eye to keep the object of interest (OBI) in video at the centre of the image frame. The particle filter (PF) is used to track OBI in video by estimating its position in image frame. A new optimized bootstrap PF (OBPF) is introduced solving problem of PF on video object tracking for rigid motion. A new adaptive OBPF (AOBPF) is introduced for tracking object that has variable speed. The experimental and comparison results of the designed controllers using different methods of optimization are presented to demonstrate and to verify the proposed video object tracking system performance. [ABSTRACT FROM AUTHOR]

We provide a new method to simulate the process of tracking the noncooperative object that moves beyond visual range with a photon-counting laser ranging system. Based on fundamentals of photon-counting laser ranging techniques and parameters of the experimental prototype, we generate echo events according to their probability. Then, we accumulate the echo data in a certain period of time and accurately extract the object's trajectory with mean-shift and random sample consensus algorithms. Depending on the trajectory during the accumulation period, we predict the relative movement of the object in succeeding cycles by using self-tuning α-β filtering and carefully pick out photon echo signals and apply the polynomial fitting to them to compute the trajectory of the object. The simulation shows that the error between the theoretical trajectory and the extracted trajectory is decreasing all the time, which suggests that we can track the object precisely as the time goes by. The simulation in this paper provides a new way for applications like satellite orientation, identification, troubleshooting, etc. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive dynamic surface control scheme is proposed for a class of multi-input multi-output (MIMO) nonlinear time-varying systems. By fusing a bound estimation approach, a smooth function and a time-varying matrix factorisation, the obstacle caused by unknown time-varying parameters is circumvented. The proposed scheme is free of the problem of explosion of complexity and needs only one updated parameter at each design step. Moreover, all tracking errors can converge to predefined arbitrarily small residual sets with a prescribed convergence rate and maximum overshoot. Such features result in a simple adaptive controller which can be easily implemented in applications with less computational burden and satisfactory tracking performance. Simulation results are presented to illustrate the effectiveness of the proposed scheme. [ABSTRACT FROM PUBLISHER]

A feedback/feedforward controller architecture is developed that characterises the achievable reference tracking of real time inputs for both minimum phase and non-minimum phase systems with time delays, when there are no modelling errors or external disturbances. This characterisation is obtained by factoring the plant into its minimum phase, non-minimum phase, and time delay components, which are used to design two feedforward controllers that inject signals into two points of the feedback loop. Design constraints are provided that determine both the types of signals that may be achieved, and the feedforward controllers that will generate that output. Of course, in practice, both modelling errors and external disturbances will be present. In this case, we develop robust analysis tools that both guide the feedback controller design process, and provide rigorous robust tracking performance that guarantees for the overall resulting closed-loop system. Robust methods for designing the feedforward controllers are presented, and numerical examples are provided. The performance of this architecture depends strongly on the choice of design parameters, and the accuracy of the plant model used. Hence, the use of adaptation methods is also considered, and it is shown that they can readily be employed to improve the performance of this control methodology. [ABSTRACT FROM PUBLISHER]

Energy efficiency is critical for Wireless Sensor Networks (WSNs) since sensor nodes usually have very limited energy supply from battery. Sleep scheduling and nodes cooperation are two of the most efficient methods to achieve energy conservation in WSNs. In this paper, we propose an adaptive energy optimization approach for target tracking applications, called Energy-Efficient Node Coordination (EENC), which is based on the grid structure. EENC provides an unambiguous calculation and analysis for optimal the nodes cooperation theoretically. In EENC, the sleep schedule of sensor nodes is locally synchronized and globally unsynchronized. Locally in each grid, the sleep schedule of all nodes is synchronized by the grid head, while globally the sleep schedule of each grid is independent and is determined by the proposed scheme. For dynamic sleep scheduling in tracking state we propose a multi-level coordination algorithm to find an optimal nodes cooperation of the network to maximize the energy conservation while preserving the tracking performance. Experimental results show that EENC can achieve energy saving of at least 38.2% compared to state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

This paper proposes an adaptive neural network based control scheme for coordinated multiple mobile manipulators in the presence of model uncertainties and external disturbances. A key feature of this scheme is that the prior information of the possible upper bound of the uncertainty is not required. A feedforward neural network (FFNN) is employed to learn this upper bound. The proposed controller not only achieves uniformly ultimate boundedness (UUB) for trajectory tracking error of the object but also keeps the internal force error under a bound. Simulation studies are carried out for coordinated two wheels driven mobile manipulators. [ABSTRACT FROM AUTHOR]

Particle filter algorithms are widely used for object tracking in video sequences, but the standard particle filter algorithm cannot solve the validity of particles ideally. To solve the problems of particle degeneration and sample impoverishment in a particle filter tracking algorithm, an improved object tracking algorithm is proposed, which combines a multi-feature fusion method and a genetic evolution mechanism. The algorithm dynamically computes the feature's fusion weight by the discriminability of each vision feature and then constructs the important density function based on selecting a feature's fusion method adaptively. Moreover, a self-adaptive genetic evolutionary mechanism is introduced into the particle resampling process and makes the particle become an agent with the ability of dynamic self-adaption. With self-adaptive crossover and mutation operators, the evolution system produces a large number of new particles, which can better approximate the true state of the tracking object. The experimental results show that the proposed object tracking algorithm surpasses the conventional particle filter on both robustness and accuracy, even though the tracking object is very challenging regarding illumination variation, structural deformation, the interference of similar targets and occlusion. [ABSTRACT FROM AUTHOR]

In recent years, plenty of object tracking methods have been put forward for better tracking accuracies. However, few of them can be applied to the real-time applications due to high computational cost. In this paper, aiming at achieving better real-time tracking performance, we propose an adaptive robust frame-work for object tracking based on the CamShift approach, which is notable for its simplicity and high efficiency. An adaptive local search method, which is composed of two key steps, i.e. iterative extension and iterative split search, is put forward to search for the optimal object candidate to avoid that the CamShift tracker may get confused by the distracters from the surrounding background and hence erroneously incorporate them into the object region. A Kalman filter is also incorporated into our framework for prediction of the object's movement, so as to reduce the search effort and possible tracking failure caused by fast object motion. The experimental results demonstrate that the proposed tracking framework is robust and computationally effective. [ABSTRACT FROM AUTHOR]

• In order to carry out the ground target tracking task, a neuroadaptive integral robust controller is proposed. • By incorporating the RISE control into adaptive NN, the asymptotic stability of system can be expected. • Using the MLP technology, only two learning parameters are required for neural approximation. This paper aims to achieve the visual quadrotor tracking for ground moving target in the presence of various uncertainties. In order to provide a stable quadrotor motion, the perspective image moments extracted from the defined virtual image plane are selected as the visual features to deduce a decoupled visual quadrotor model. Then a neuroadaptive integral robust controller is designed for the outer and inner loops of visual quadrotor within the image based visual servo (IBVS) framework, which enables error stabilization and improves the anti-disturbance capability. Then, the unknown lumped uncertainties consisting of parametric uncertainties and external disturbances are estimated via constructing a minimal learning parameter-neural network (MLP-NN) and compensated in the feedforward loop, subsequently the residual estimation errors are further suppressed by the structured robust integral of the sign of the error (RISE) feedback control law, hence a high-precise servo tracking performance can be expected. The innovations of the algorithm lie in, (1) with the NN approximation compensated in feedforward loop, the residual estimation errors in feedback loop are decreased, hence the conservatism that using high gains when facing large uncertainties is relaxed, (2) by means of the peculiarity of RISE control, asymptotic stability can be guaranteed with continuous and bounded control inputs, (3) using MLP technology, the number of NN learning parameters are decreased to only two for the closed-loop system, and the online computational load is greatly reduced. Finally, the validity and superiority of the proposed method are validated through affluent simulations and comparisons. [ABSTRACT FROM AUTHOR]

This paper investigates the post-capture control problem associated with trajectory tracking and inertia property identification for a six degree-of-freedom (6DOF) combined spacecraft system subject to input saturation. Post-capture of non-cooperative target will cause a large shift in the dynamics of combined spacecraft mainly resulting from the change of inertia properties. An adaptive tracking control law for combined spacecraft is designed based on the terminal sliding mode and dynamic surface control techniques, and the inertia properties can be identified simultaneously. To estimate mass and inertia matrix, the expression of parameter estimation error is obtained by introducing a group of auxiliary filtered variables. A saturation compensator is employed to deal with the input saturation. Within the Lyapunov framework, the proposed controller is proved to guarantee the finite-time convergence of both trajectory tracking and inertia property identification driven by continuous control forces. Numerical simulations are finally performed to demonstrate the effectiveness of the designed control law. [ABSTRACT FROM AUTHOR]