Particle filters and occlusion handling for rigid 2D–3D pose tracking

In this paper, we address the problem of 2D–3D pose estimation. Specifically, we propose an approach to jointly track a rigid object in a 2D image sequence and to estimate its pose (position and orientation) in 3D space. We revisit a joint 2D segmentation/3D pose estimation technique, and then extend the framework by incorporating a particle filter to robustly track the object in a challenging environment, and by developing an occlusion detection and handling scheme to continuously track the object in the presence of occlusions. In particular, we focus on partial occlusions that prevent the tracker from extracting an exact region properties of the object, which plays a pivotal role for region-based tracking methods in maintaining the track. To this end, a dynamical choice of how to invoke the objective functional is performed online based on the degree of dependencies between predictions and measurements of the system in accordance with the degree of occlusion and the variation of the object’s pose. This scheme provides the robustness to deal with occlusions of an obstacle with different statistical properties from that of the object of interest. Experimental results demonstrate the practical applicability and robustness of the proposed method in several challenging scenarios.     

Particle swarm optimization–Markov Chain Monte Carlo for accurate visual tracking with adaptive template update

A novel tracking method is proposed, which infers a target state and appearance template simultaneously. With this simultaneous inference, the method accurately estimates the target state and robustly updates the target template. The joint inference is performed by using the proposed particle swarm optimization–Markov chain Monte Carlo (PSO–MCMC) sampling method. PSO–MCMC is a combination of the particle swarm optimization (PSO) and Markov chain Monte Carlo sampling (MCMC), in which the PSO evolutionary algorithm and MCMC aim to find the target state and appearance template, respectively. The PSO can handle multi-modality in the target state and is therefore superior to a standard particle filter. Thus, PSO–MCMC achieves better performance in terms of accuracy when compared to the recently proposed particle MCMC. Experimental results demonstrate that the proposed tracker adaptively updates the target template and outperforms state-of-the-art tracking methods on a benchmark dataset.     

Robust adaptive uniform exact tracking control for uncertain Euler–Lagrange system

This paper offers a solution to the robust adaptive uniform exact tracking control for uncertain nonlinear Euler–Lagrange (EL) system. An adaptive finite-time tracking control algorithm is designed by proposing a novel nonsingular integral terminal sliding-mode surface. Moreover, a new adaptive parameter tuning law is also developed by making good use of the system tracking errors and the adaptive parameter estimation errors. Thus, both the trajectory tracking and the parameter estimation can be achieved in a guaranteed time adjusted arbitrarily based on practical demands, simultaneously. Additionally, the control result for the EL system proposed in this paper can be extended to high-order nonlinear systems easily. Finally, a test-bed 2-DOF robot arm is set-up to demonstrate the performance of the new control algorithm.     

A Durbin–Watson serial correlation test for ARX processes via excited adaptive tracking

We propose a new statistical test for the residual autocorrelation in ARX adaptive tracking. The introduction of a persistent excitation in the adaptive tracking control allows us to build a bilateral statistical test based on the well-known Durbin–Watson statistic. We establish the almost sure convergence and the asymptotic normality for the Durbin–Watson statistic leading to a powerful serial correlation test. Numerical experiments illustrate the good performances of our statistical test procedure.     

Non-linear velocity observer for vehicles with tyre–road friction estimation

A non-linear observer is proposed for the estimation of the longitudinal and lateral velocities of automotive vehicles due to highly non-linear friction. To take the unknown time-varying road conditions into account, a real-time tyre–road friction estimation algorithm is provided for adaptation to changes of different road adhesion characteristics. Besides, the coupling effects of the longitudinal and lateral forces are fully exploited to design the non-linear observer based on the longitudinal and lateral acceleration measurements. The observer is computationally efficient and is based on a standard sensor configuration commonly available in modern cars. The uniform global asymptotical stability of the adaptive observer is guaranteed under a certain persistency-of-excitation condition. Moreover, a stronger local stability result can also be obtained, i.e. uniform local exponential stability. The performance of the observer is compared with that of existing approaches under different manoeuvres and road conditions.     

Continuous–discrete-time adaptive observers for nonlinear systems with sampled output measurements

This paper considers the continuous–discrete-time adaptive observer (CDAO) design for a class of nonlinear systems with unknown constant parameters and sampled output measurements. The proposed observer is actually an impulsive system, since the observer state flows according to a set of differential equations and with instantaneous state jumps corresponding to measured samples and their estimates, and an inter-sample output predictor is used to predict the output during sampling intervals. By assuming appropriate persistent excitation conditions and following a technical lemma, an upper bound of the sampling intervals is derived, with which the convergence of the observer state and unknown parameters can be ensured. Finally, the proposed observer is used in examples of chaotic oscillators and single-link flexible-joint robot manipulator to show the effectiveness.     

Inversion-based optimal output tracking–transition switching with preview for nonminimum-phase linear systems

In this article, the problem of nonperiodic tracking–transition switching with preview is considered. Such a control problem exists in applications including nanoscale material property mapping, robot manipulation, and probe-based nanofabrication, where the output needs to track the desired trajectory during the tracking sections, and rapidly transit to another point during the transition sections with no post-transition oscillations. Due to the coupling between the control of the tracking sections and that of the transition ones, and the potential mismatch of the boundary system state at the tracking–transition switching instants, these control objectives become challenging for nonminimum-phase systems. In the proposed approach, the optimal desired output trajectory for the transition sections is designed through a direct minimization of the output energy, and the needed control input that maintains the smoothness of both the output and the system state across all tracking–transition switching is obtained through a preview-based stable-inversion approach. The needed preview time is quantified by the characteristics of the system dynamics, and can be minimized via the recently developed optimal preview-based inversion technique. The proposed approach is illustrated through a nanomanipulation example in simulation.     

Robust tracking control for operator-based uncertain micro-hand actuator with Prandtl–Ishlinskii hysteresis

Since the hysteresis property inherently exists in the rubber material, it is necessary to deal with the control issues for the micro-hand by considering the hysteresis property. Therefore, in this paper, the robust tracking control for the micro-hand systems is discussed from the aspect of the Prandtl–Ishlinskii hysteresis property which is more applicable for the real applications. Firstly, a new model is obtained by combining the dynamic model of the micro-hand with Prandtl–Ishlinskii hysteresis property. Secondly, a new stability condition based on bounded input and bounded output stability is proposed for the Prandtl–Ishlinskii hysteresis modeled micro-hand system from two different cases. Thirdly, by designing the robust controllers based on the internal model control method, the tracking performance can be improved by eliminating the effect from the disturbance. Finally, simulation is used to further demonstrate the effectiveness of the proposed design scheme.     

Leader–follower tracking control with guaranteed consensus performance for interconnected systems with linear dynamic uncertain coupling

This paper considers the leader–follower tracking control problem for linear interconnected systems with undirected topology and linear dynamic coupling. Interactions between the systems are treated as linear dynamic uncertainty and are described in terms of integral quadratic constraints (IQCs). A consensus-type tracking control protocol is proposed for each system based on its state relative to its neighbours. In addition, a selected set of subsystems is used to control their relative states with respect to the leader. Two methods are proposed for the design of this control protocol. One method uses a coordinate transformation to recast the protocol design problem as a decentralised robust control problem for an auxiliary interconnected large-scale system. Another method is direct; it does not employ coordinate transformation, rather it also allows for more general linear uncertain interactions. Using these methods, sufficient conditions are obtained which guarantee that the system tracks the leader. These conditions guarantee a suboptimal bound on the system consensus and tracking performance. The proposed methods are compared using a simulation example, and their effectiveness is discussed. Also, algorithms are proposed for computing suboptimal controllers.     

Probabilistic odd–even: an adaptive wormhole routing algorithm for 2D mesh network-on-chip

Wormhole routing is a popular routing technique used in network-on-chip. It is efficient but susceptible to deadlock, while deadlock will significantly degrade the network performance of NoC. Most existing adaptive wormhole routings avoid deadlock by reducing the degree of adaptiveness and thus sacrificing network performance. In this paper, we address both deadlock and network performance issues jointly, and propose a probabilistic odd–even (POE) routing algorithm that achieves the minimum packet delivery delay. The proposed POE dynamically adjusts the probabilities of constrained turns that may lead to deadlocks according to the current network conditions, and uses an efficient deadlock detection and recovery scheme when a deadlock happens. By adopting constrained turns adaptively to the network status, it not only reduces the frequency of deadlock and allows the network to be swiftly recovered when it occurs, but also greatly improves the degree of adaptiveness to obtain high network performance. Experimental results show that our method achieves a significant performance improvement both in terms of network throughput and average packet latency compared with the existing methods such as XY, odd–even, abacus turn model and fully adaptive routing algorithm while it only has moderate energy consumption.     

A Youla–Kucera parametrized adaptive feedforward compensator for active vibration control with mechanical coupling

Most of the adaptive feedforward vibration (or noise) compensation systems feature an internal ”positive feedback” coupling between the compensator system and the correlated disturbance measurement which serves as reference. This may lead to the instability of the system. Instead of the standard IIR structure for the adaptive feedforward compensator, the paper proposes a Youla–Kucera parametrization of the adaptive compensator. The central compensator assures the stability of the system and its performances are enhanced in real time by the direct adaptation of the Youla–Kucera parameters. Theoretical and experimental comparison with recent results obtained using an IIR adaptive feedforward compensator are provided.     

An adaptive XIGA with locally refined NURBS for modeling cracked composite FG Mindlin–Reissner plates

Engineering with Computers - This paper is devoted to numerical investigations on mechanical behavior of cracked composite functionally graded (FG) plates. We thus develop an efficient adaptive...     

Finite-horizon differential games for missile–target interception system using adaptive dynamic programming with input constraints

In this paper, the problem of intercepting a manoeuvring target within a fixed final time is posed in a non-linear constrained zero-sum differential game framework. The Nash equilibrium solution is found by solving the finite-horizon constrained differential game problem via adaptive dynamic programming technique. Besides, a suitable non-quadratic functional is utilised to encode the control constraints into a differential game problem. The single critic network with constant weights and time-varying activation functions is constructed to approximate the solution of associated time-varying Hamilton–Jacobi–Isaacs equation online. To properly satisfy the terminal constraint, an additional error term is incorporated in a novel weight-updating law such that the terminal constraint error is also minimised over time. By utilising Lyapunov's direct method, the closed-loop differential game system and the estimation weight error of the critic network are proved to be uniformly ultimately bounded. Finally, the effectiveness of the proposed method is demonstrated by using a simple non-linear system and a non-linear missile–target interception system, assuming first-order dynamics for the interceptor and target.     

An ESO-based integrated trajectory tracking control for tractor–trailer vehicles with various constraints and physical limitations

This paper develops an effective integrated control strategy for the trajectory tracking control of a tractor–trailer vehicle which suffers from inaccessible system states and uncertain disturbance for practical implementation. In addition, diverse problems, such as nonholonomic constraints, underactuated dynamics, physical limitations, etc, can be resolved favourably all together. Aiming to the vehicle trajectory tracking, a constrained model predictive control (MPC) is introduced as a trajectory tracking module, by which the underactuated dynamics, various constraints and physical limitations, can be tackled at the same time. For the desired velocity tracking, a robust global terminal sliding mode control (GTSMC) is employed to guarantee the finite-time convergence of the velocity tracking process, which will improve the transient performance to a great extent. Particularly, in the absence of velocity information, an extended state observer (ESO) is developed to estimate the vehicle velocity in addition to simultaneously obtaining the uncertain disturbance information, which offers prerequisite for the previous control approaches. The simulation results confirm that the presented control strategy can synthesise varied control techniques effectively and deal with diverse problems for the trajectory tracking of tractor–trailer vehicles successfully.     

Optimal Paths for Variants of the 2D and 3D Reeds–Shepp Car with Applications in Image Analysis

We present a PDE-based approach for finding optimal paths for the Reeds–Shepp car. In our model we minimize a (data-driven) functional involving both curvature and length penalization, with several generalizations. Our approach encompasses the two- and three-dimensional variants of this model, state-dependent costs, and moreover, the possibility of removing the reverse gear of the vehicle. We prove both global and local controllability results of the models. Via eikonal equations on the manifold \(\mathbb {R}^d \times {\mathbb {S}}^{d-1}\) we compute distance maps w.r.t. highly anisotropic Finsler metrics, which approximate the singular (quasi)-distances underlying the model. This is achieved using a fast-marching (FM) method, building on Mirebeau (Numer Math 126(3):515–557, 2013; SIAM J Numer Anal 52(4):1573–1599, 2014). The FM method is based on specific discretization stencils which are adapted to the preferred directions of the Finsler metric and obey a generalized acuteness property. The shortest paths can be found with a gradient descent method on the distance map, which we formalize in a theorem. We justify the use of our approximating metrics by proving convergence results. Our curve optimization model in \(\mathbb {R}^{d} \times \mathbb {S}^{d-1}\) with data-driven cost allows to extract complex tubular structures from medical images, e.g., crossings, and incomplete data due to occlusions or low contrast. Our work extends the results of Sanguinetti et al. (Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications LNCS 9423, 2015) on numerical sub-Riemannian eikonal equations and the Reeds–Shepp car to 3D, with comparisons to exact solutions by Duits et al. (J Dyn Control Syst 22(4):771–805, 2016). Numerical experiments show the high potential of our method in two applications: vessel tracking in retinal images for the case \(d=2\) and brain connectivity measures from diffusion-weighted MRI data for the case \(d=3\), extending the work of Bekkers et al. (SIAM J Imaging Sci 8(4):2740–2770, 2015). We demonstrate how the new model without reverse gear better handles bifurcations.