In recent years, the combination of event cameras and computer vision has shown increasingly excellent performance. Due to high sensitivity, event cameras are capable of addressing the issue of motion blur in conventional cameras, and are well-suited for analyzing fast-moving objects, making them highly suitable for depth estimation in UAV applications This paper focuses on methods for depth estimation using events generated by event cameras. Due to the asynchronicity of events, it is difficult to directly transmit events to the depth estimation network. So the method to preprocess events is important. Unlike existing processing methods, this paper creatively proposes the idea of adaptive stacks, which can change the size of weighted stacks in real time according to the events generation rate. In this way, we can solve the problems caused by traditional processing methods, and better utilize the effective information of events. Then, a depth estimation network corresponding to the adaptive stacks is designed to form a complete end-to-end events depth estimation model: Adaptive Stacks Depth Estimation Network (ASNet). Compared with other models, ASNet has demonstrated excellent depth estimation accuracy and has great application prospects.

This paper investigates the cooperative planning and control problem for multiple connected autonomous vehicles (CAVs) in different scenarios. In the existing literature, most of the methods suffer from significant problems in computational efficiency. Furthermore, as the optimization problem is nonlinear and nonconvex, it typically poses great difficulty in determining the optimal solution. To address this issue, this work proposes a novel and completely parallel computation framework by leveraging the alternating direction method of multipliers (ADMM). The nonlinear and nonconvex optimization problem in the autonomous driving problem can be divided into two manageable sub-problems; and the resulting sub-problems can be solved by using effective optimization methods in a parallel framework. Here, the differential dynamic programming (DDP) algorithm is capable of addressing the nonlinearity of the system dynamics rather effectively; and the nonconvex coupling constraints with small dimensions can be resolved by invoking the notion of semi-definite relaxation (SDR), which can also be solved in a very short time. Due to the parallel computation and efficient relaxation of nonconvex constraints, our proposed approach effectively realizes real-time implementation; and thus extra assurance of driving safety is provided. In addition, two transportation scenarios for multiple CAVs are used to illustrate the effectiveness and efficiency of the proposed method.

This paper works on multiple-pursuer single-evader (MPSE) problems with a fast evader, which means multiple pursuers try to capture one evader while the evader tries to escape from the encirclement. The biggest concern is that the maximum velocity of the evader is larger than all the pursuers. Some improved strategies for the evader and pursuers based on traditional algorithms are firstly provided. Then gene expression programming (GEP) is used to generate new strategies which are better than the traditional ones. This paper shows configurations of function set, terminal set, fitness, evaluation function, and other parameters used in the GEP method, which can be implemented in other cases or similar problems.

Continued great efforts have been dedicated toward high-quality trajectory generation based on optimization methods; however, most of them do not suitably and effectively consider the situation with moving obstacles; and more particularly, the future position of these moving obstacles in the presence of uncertainty within some possible prescribed prediction horizon. To cater to this rather major shortcoming, this work shows how a variational Bayesian Gaussian mixture model (vBGMM) framework can be employed to predict the future trajectory of moving obstacles; and then with this methodology, a trajectory generation framework is proposed which will efficiently and effectively address trajectory generation in the presence of moving obstacles, and incorporate the presence of uncertainty within a prediction horizon. In this work, the full predictive conditional probability density function (PDF) with mean and covariance is obtained and, thus, a future trajectory with uncertainty is formulated as a collision region represented by a confidence ellipsoid. To avoid the collision region, chance constraints are imposed to restrict the collision probability, and subsequently, a nonlinear model predictive control problem is constructed with these chance constraints. It is shown that the proposed approach is able to predict the future position of the moving obstacles effectively; and, thus, based on the environmental information of the probabilistic prediction, it is also shown that the timing of collision avoidance can be earlier than the method without prediction. The tracking error and distance to obstacles of the trajectory with prediction are smaller compared with the method without prediction.

In this paper, we consider the problem of formation control of multiple unmanned aerial vehicles (MUAVs). Based on a self-organizing approach, we propose a solution for both circle and arbitrary polygon formations. The proposed solution is a distributed one only using the neighbouring UAV information. Simulation is conducted to verify two types of the formations as well as UAV failure and re-formation process.

Even though path planning is a well-studied problem in 2D environment, finding an optimal or near-optimal path in a complex and unknown 3D environment has great prospect, but it is hard to find the optimal path quickly. In this paper, we propose a new algorithm called Hierarchical Lazy Theta* (HLT*), which can plan the near-optimal path efficiently for real-time operation based on the heuristic-based path-finding algorithm Lazy Theta* with a hierarchical path planning approach. Path refinement, smoothing, and polishing are used to refine the path to ensure the feasibility of computed path. Its computation time and path quality are dependent on parameters, such as map size, environment complexity, sensor detection range, refinement range, computation time limits, and any restriction on planning time. Simulation experiments are used to assess the performance, and the simulation results show that HLT* algorithm is capable of planning a high-quality path in a shorter time.

Ultrasonic motors (USMs) are widely used in many applications that precise and fast motions are required, such as precision machines and medical devices, etc. In this paper, a USM -driven ear surgical device is introduced. To address the control challenges of the USM, an intelligent controller consisting of a cerebellar model articulation controller (CMAC) and a sliding mode compensator (SMC) is designed. Therein, the CMAC serves as the main controller due to its fast learning ability while the SMC is used to eliminate the approximation error between the CMAC and the desired perfect controller. Several experiments are carried out to validate the effectiveness of the proposed control scheme, and the results show that the proposed control scheme is able to achieve good tracking performance and guaranteed robustness.

In recent years, Global Positioning System (GPS) spoofing is becoming more attentions in our society. How to detect GPS spoofing is a challenging work. In this paper, a GPS spoofing detection scheme is proposed based on a quadrotor unmanned aerial vehicle (UAV) model. The idea is that it compares the error between the UAV model estimator and the actual signal with a given threshold to justify whether the UAV is spoofed. Simulation is given to show that the proposed detection scheme is effective for the controlled UAV when its GPS signal is spoofed.

In this paper, an automatic office-based ear surgical device for the treatment of Otitis Media with Effusion (OME) which overcomes the disadvantages of the conventional surgical treatment is introduced. Since the device carries out the surgery automatically under the guidance of force sensing information, this information must be reliable so that the safety of the device can be ensured. Hence, a force detection method based on the disturbance observer is proposed in this paper. To overcome the disadvantages of the conventional disturbance observer, an advanced disturbance observer is designed and its stability is analyzed. Finally, the simulation study on the disturbance observer is carried out. The results show that the advanced disturbance observer can estimate the disturbance correctly and precisely.

Rehabilitation robots, by necessity, have direct physical interaction with humans. Physical interaction affects the controlled variables and may even cause system instability. Thus, human-robot interaction control design is critical in rehabilitation robotics research. This paper presents an interaction control strategy for a gait rehabilitation robot. The robot is driven by a novel compact series elastic actuator, which provides intrinsic compliance and backdrivablility for safe human-robot interaction. The control design is based on the actuator model with consideration of interaction dynamics. It consists mainly of human interaction compensation, friction compensation, and is enhanced with a disturbance observer. Such a control scheme enables the robot to achieve low output impedance when operating in human-in-charge mode and achieve accurate force tracking when operating in force control mode. Due to the direct physical interaction with humans, the controller design must also meet the stability requirement. A theoretical proof is provided to show the guaranteed stability of the closed-loop system under the proposed controller. The proposed design is verified with an ankle robot in walking experiments. The results can be readily extended to other rehabilitation and assistive robots driven with compliant actuators without much difficulty.

We are developing an intelligent compact and modular knee-ankle-foot robot gait rehabilitation at outpatient and home settings. The robot is designed with a novel compact compliant force controllable actuator. We adopt a modular design for the knee and ankle joint so that the robot can assist patients with different conditions of gait impairments. The light-weight anthropomorphic structure designed based on biomechanical studies is built with advanced composite materials to achieve portability. A prototype of the robot has been built for testing. In this paper, we present the mechanical design of the robot with focus on the actuator and mechanism design and analysis, with testing results to demonstrate the performance of the actuator.

In-vitro fertilization (IVF) and related technologies are arguably the most challenging of all cell culture applications. This paper introduces a new design of an integrated oocyte retrieval solution before the oocytes are further put into laboratory processing during IVF treatment. It facilitates the surgery operation and addresses the temperature inaccuracy of follicular fluid during the transfer to the patient's body. The mechanical design is implemented into a medical-standard conforming platform. An accurate temperature estimation and optimization scheme is also proposed. Comparison before and after the introduction of the new prototype under various operating conditions reveals a significant improvement in performance. This yields a potential application for the medical/healthcare industry.

Among motor faults, bearing, rotator, and stator failures are the most commonly reported. Because of the low-amplitude fault signatures in the current spectrum, they are also the most challenging to diagnose, even in line-driven motors. However, a fault simulator of induction motors has not been adequately investigated in the literature. The purpose of this paper is to build a hardware-in-the-loop (HIL) simulation system to model system failures of induction drives. The HIL system is based on a dynamical mathematical model, consisting of a dSPACE control system to process data and a real dc motor. The proposed system can produce bearing, rotator, stator, and sensor failures for testing of various fault diagnosis schemes. The experimental results show its functionality.

In this paper, we analyze Xu and Yuille’s robust principal component analysis (RPCA) learning algorithms by means of the distance measurement in space. Based on the analysis, a family of fuzzy RPCA learning algorithms is proposed, which is robust against outliers. These algorithms can explicitly be understood from the viewpoint of fuzzy set theory, though Xu and Yuille’s algorithms were proposed based on a statistical physics approach. In the proposed algorithms, an adaptive learning procedure overcomes the difficulty of selection of learning parameters in Xu and Yuille’s algorithms. Furthermore, the robustness of proposed algorithms is investigated by using the theory of influence functions. Simulations are carried out to illustrate the robustness of these algorithms.

This paper investigates the problem of fault detection and diagnosis in a class of nonlinear systems with modeling uncertainties. A nonlinear observer is first designed for monitoring fault. Radial basis function (RBF) neural network is used in this observer to approximate the unknown nonlinear dynamics. When a fault occurs, another RBF is triggered to capture the nonlinear characteristics of the fault function. The fault model obtained by the second neural network (NN) can be used for identifying the failure mode by comparing it with any known failure modes. Finally, a simulation example is presented to illustrate the effectiveness of the proposed scheme.

Nonlinearities including friction and force ripple, exist in the permanent magnet linear motors, which limit control precision. In this paper, the dynamics model of the linear motor under nonlinear effects are first given. Parameter estimation via limit cycle experiments using the dual-relay feedback apparatus are proposed. Guidelines for choosing appropriate relay gains to tune the limit cycle amplitudes and frequencies within the feedback system with friction and force ripple are also provided. All the results are verified by simulation.

In this paper, fault diagnosis and accommodation control are developed for robotic systems. First, a nonlinear observer in the proposed method is designed based on the available model. The fault detection is carried out by comparing the observer states with their signatures. Secondly, state observers are constructed based on possible fault function sets. Thirdly, the accommodation control design is developed using a normal controller plus a neural network compensator to capture the nonlinear characteristics of faults. Finally, if the fault isolation is completed successfully, the second fault accommodation controller is presented based on the fault information obtained by the isolation scheme.

In this work, a fault detection method is developed based on a neural network (NN) learning model. The robust observer is designed for monitoring fault, without NN learning, when the system of concern is operating in the normal healthy mode. By comparing appropriate states with their signatures, the fault diagnosis can be carried out and the NN learning is then triggered to identify the fault function.

In this paper, we consider the decentralized adaptive control design problem for uncertain mechanical systems, where uncertainty may arise due to isolated subsystem and/or interconnections among subsystems. Radial basis function neural networks are used to approximate the nonlinear functions to include both dynamic and interconnection uncertainties in each subsystem. The stability of the thus designed control system can be guaranteed by a rigid proof. Finally, a simulation example is given to illustrate the effectiveness of the proposed algorithm.

There is a growing trend in developing the wireless machine-to-machine (M2M) communication for factory automation. Currently, most wireless protocols for infrastructureless mobile ad-hoc networks focus on the mobility issue and are mainly suitable for narrowband radio devices. The mechanisms for route update and discovery that work well for wireless mobile ad-hoc networks prove to be redundant and instead add to processing overheads in a static wireless factory setting. This paper presents the development and implementation of a new wireless routing protocol, Bluewave, that caters specifically to wireless communication between machines in a factory setting. Bluewave utilizes features of Bluetooth technology when performing the route setup. The merits of the proposed protocol, in terms of shorter initialization time and route setup time, are duly discussed. A case study illustrating the application of Bluewave to an online condition-based maintenance scenario is presented. Experimental results obtained are furnished to show the viability of Bluewave in industrial automation

In this letter, we solve the problem of decentralized adaptive asymptotic tracking for a class of large scale systems with significant nonlinearities and uncertainties. Neural networks (NNs) are used as a control part to cancel the effect of the unknown nonlinearity. Semiglobal asymptotic stability results are obtained and the tracking error converges to zero.

This paper describes an approach based on neural networks (NNs) for geometrical error modeling and compensation for precision motion systems. A laser interferometer is used to obtain the systematic error measurements of the geometrical errors, based on which an error model may be constructed and, consequently, a model-based compensation may be incorporated in the motion-control system. NNs are used to approximate the components of geometrical errors, thus dispensing with the conventional lookup table. Apart from serving as a more adequate model due to its inherent nonlinear characteristics, the use of NNs also results in less memory requirements to implement the error compensation for a specified precision compared to the use of lookup table. The adequacy and clear benefits of the proposed approach are illustrated via applications to various configurations of precision-positioning stages, including a single-axis, a gantry, and a complete XY stage

This paper addresses the physical dynamic modeling and simulates the adaptive control of a precision gantry stage. The stage is posed as a three-degree-of-freedom system, based on a patented air-bearing stage provided by the Singapore Institute of Manufacturing Technology. Using this stage as the target model, a mathematical model is then built using the Lagrangian equation. An adaptive control method is formulated for the positioning of the stage, with minimal a priori information assumed of the model. A stability analysis is given for the proposed control scheme. Simulation results are also documented to illustrate the feasibility of the scheme. Future direction is to implement the adaptive controller to the actual gantry stage.

In this paper, we develop a predictive learning controller for ram velocity of injection molding based on neural networks. We first introduce a model of describing the injection molding, including the time horizon and the batch index. The feedback control plus biased function is proposed for controlling this plant. More specifically, a radial basis function (RBF) network is used to approximate the biased function based on the time horizon. The weights in the RBF are determined by a predictive control scheme based on the batch index. For this algorithm, relevant convergence is investigated. Simulation results reveal that the proposed control can achieve our claims.

Zhang et al. presented an excellent neural-network (NN) controller for a class of nonlinear control designs. The singularity issue is completely avoided. Based on a modified Lyapunov function, their lemma illustrates the existence of an ideal control which is important in establishing the NN approximator. In this paper, we provide a Lyapunov function to realize an alternative ideal control which is more direct and simpler. The major contributions of this paper are divided into two parts. First, it proposes a control scheme which results in a smaller dimensionality of NN than that of Zhang et al. In this way, the proposed NN controller is easier to implement and more reliable for practical purposes. Second, by removing certain restrictions from the design reported by Zhang et al., we further develop a new NN controller, which can be applied to a wider class of systems.

We develop a decentralized neural-network (NN) controller for a class of large-scale nonlinear systems with the high-order interconnections. The controller is a mixed NN comprised of a conventional NN and a special NN. The conventional NN is used to approximate the unknown nonlinearities in the subsystem, while a special NN is used to counter the high-order interconnections. We prove that this NN structure can achieve a stable controller for the large-scale systems.

We present a technology which allows manually controlled and automatically controlled vehicles to co-exist, thereby reducing much of the cost and infrastructure requirements in highway automation. An autonomous vehicle driving system (AVDS) is presented which only uses information from vehicle sensors, thereby eliminating the requirement that vehicles communicate with one another. This format allows gradual market penetration of AVDS, which makes the technology attractive from a systems implementation standpoint. To evaluate the performance of the mixed traffic, we simulate manual and automated vehicles together and show that our AVDS can lead to potentially large gains in capacity and smoother flow without the loss of safety.

We present a new compensation technique for a friction model, which captures problematic friction effects such as Stribeck effects, hysteresis, stick-slip limit cycling, pre-sliding displacement and rising static friction. The proposed control utilizes a PD control structure and an adaptive estimate of the friction force. Specifically, a radial basis function (RBF) is used to compensate the effects of the unknown nonlinearly occurring Stribeck parameter in the friction model. The main analytical result is a stability theorem for the proposed compensator which can achieve regional stability of the closed-loop system. Furthermore, we show that the transient performance of the resulting adaptive system is analytically quantified. To support the theoretical concepts, we present dynamic simulations for the proposed control scheme.