Following biology's lead, soft robotics has emerged as a perfect candidate for actuation within complex environments. While soft actuation has been developed intensively over the last few decades, soft sensing has so far slowed to catch up. A largely unresearched area is the change of the soft material properties through prestress to achieve a degree of mechanical sensitivity tunability within soft sensors. Here, a new 3D force sensor which employs novel hydraulic filament artificial muscles capable of in-situ sensitivity tunability is introduced. Using a neural network (NN) model, the new soft 3D sensor can precisely detect external forces based on the change of the hydraulic pressures with error of$\sim 1.0, \sim 1.3$, and$\sim 0.94$% in the$\text{x, y}$, and z-axis directions, respectively. The sensor is also able to sense large force ranges, comparable to other similar sensors available in the literature. The sensor is then integrated into a soft robotic surgical arm for monitoring the tool-tissue interaction during an ablation process.

Robotic vision is increasingly applied for surface inspection of built infrastructure. For this, it is essential to develop robust algorithms for semantic segmentation. This article presents a deep learning approach using a bidirectional self-rectifying network with Bayesian modeling (BSNBM) for improving detection accuracy, in dealing with the embedded uncertainty caused by false-positive labels and nonlinearity in sequentially convolutional blocks. For integration with residual encoders, a feature preserving branch is designed, wherein the output of previous dilated convolutional blocks is upsizedly or downsizedly passed on and concatenated with the following blocks recursively and bidirectionally. Further, to achieve robustness in feature representation with an acceptable level of credibility, convolutional kernels are randomized via a Bayesian model and adjusted per evidence update. As such, the network becomes less sensitive to uncertainty and redundant nonlinearity, which is inevitable in activation layers. Experimental results confirm the advantage of our BSNBM over current crack detection approaches.

This paper presents a novel method, called the Summit Navigator, to effectively extract local maxima of an image histogram for multi-object segmentation of images. After smoothing with a moving average filter, the obtained histogram is analyzed, based on the data density and distribution to find the best observing location. An observability index for each initial peak is proposed to evaluate if it can be considered as dominant by using the calculated observing location. Recursive algorithms are then developed for peak searching and merging to remove any false detection of peaks that are located on one side of each mode. Experimental results demonstrated the advantages of the proposed approach in terms of accuracy and consistency in different reputable datasets.

Design of a frequency-shaped second-order sliding mode (FS2SM) controller is demonstrated by means of exploiting second-order low-pass filter (LPF) to model the dynamic sliding surface to shape the frequency characteristics of the equivalent dynamics. The proposed technique is numerically verified in the simulation of a half-car model (HCM) with inbuilt active hydraulically interconnected suspension (HIS) system. The closed-loop performances confirm that inclusion of an appropriate filter in the control scheme allows not only to reduce the roll angle but also its spectrum can be shaped.

In this paper, we present a new discrete-time Fast Terminal Sliding Mode (FTSM) controller for mirror-based pointing systems. We first derive the decoupled model of those systems and then estimate the parameters using a nonlinear least-square identification method. Based on the derived model, we design a FTSM sliding manifold in the continuous domain. We then exploit the Euler discretization on the designed FTSM sliding surfaces to synthesize a discrete-time controller. Furthermore, we improve the transient dynamics of the sliding surface by adding a linear term. Finally, we prove the stability of the proposed controller based on the Sarpturk reaching condition. Extensive simulations, followed by comparisons with the Terminal Sliding Mode (TSM) and Model Predictive Control (MPC) have been carried out to evaluate the effectiveness of the proposed approach. A comparative study with data obtained from a real-time experiment was also conducted. The results indicate the advantage of the proposed method over the other techniques.

This paper presents a novel and feasible path planning technique for a group of unmanned aerial vehicles (DAVs) conducting surface inspection of infrastructure. The ultimate goal is to minimise the travel distance of DAVs while simultaneously avoid obstacles, and maintain altitude constraints as well as the shape of the UAV formation. A multiple-objective optimisation algorithm, called the Angle-encoded Particle Swarm Optimization (θ- PSO) algorithm, is proposed to accelerate the swarm convergence with angular velocity and position being used for the location of particles. The whole formation is modelled as a virtual rigid body and controlled to maintain a desired geometric shape among the paths created while the centroid of the group follows a pre-determined trajectory. Based on the testbed of 3DR Solo drones equipped with a proprietary Mission Planner, and the Internet-of- Things (loT) for multi-directional transmission and reception of data between the DAV s, extensive experiments have been conducted for triangular formation maintenance along a monorail bridge. The results obtained confirm the feasibility and effectiveness of the proposed approach.

This paper presents a computer vision-based method to automatically detect concrete cracks. We focus on images containing the concrete: background and crack, where the background is the major mode of the gray-scale histogram. Therefore, we address the detection problem of potential concrete cracks by dealing with histogram thresholding to extract regions of interests from the background. We first employ line emphasis and moving average filters to remove noise from concrete surface images obtained from an inspection robot. The developed algorithm is then applied for automatic detection of significant peaks from the gray-scale histogram of the smoothed image. The biggest peak and its corresponding valley(s) are consequently identified to calculate the threshold value for image binarization. The effectiveness of our proposed method was successfully evaluated on various test images, where cracks could be identified without the requirement of some heuristic reasoning.

The objective of this work is to develop a data processing system that can automatically generate waypoints for navigation of an unmanned aerial vehicle (UAV) to inspect surfaces of structures like buildings and bridges. The input includes data recorded by two 2D laser scanners, orthogonally mounted on the UAV, and an inertial measurement unit (IMU). To achieve the goal, algorithms are developed to process the data collected. They are separated into three major groups: (i) the data registration and filtering to generate a 3D model of the structure and control the density of point clouds for data completeness enhancement; (ii) the surface and obstacle detection to assist the UAV in monitoring tasks; and (iii) the waypoint generation to set the flight path. Experiments on different data sets show that the developed system is able to reconstruct a 3D point cloud of the structure, extract its surfaces and objects, and generate waypoints for the UAV to accomplish inspection tasks.

This paper addresses the design and implementation of an Adaptive Fading Extended Kaiman Filter (AF-EKF) for the sensorless Permanent Magnet Synchronous Motor (PMSM) on a Field Programmable Gate Array (FPGA) chip. The rotor position and speed of the motor are estimated by the implemented AF-EKF and their estimates are then used in vector control of the PMSM. In conventional Kaiman filtering, abrupt state changes may not be tracked adequately since sudden variations may seriously affect the auto-correlation Gaussian property of white noise in the filter residuals. For this, the AF-EKF has been developed to recover the estimation results in events of frequent and sharp state jumps. The AF-EKF is, therefore, a promising estimator for PMSM drives that are subject to frequently-varying loads speed commands. Here, for realization of the PMSM sensorless control using the system-on-programmable-chip technology, high-speed arithmetic functions and pipelining are employed in the FPGA implementation. The finite state machine method is also used to facilitate the execution timing and chip design. The co-simulation of Modelsim/Simulink shows effectiveness of the proposed chip-based AF-EKF PMSM speed estimation.

It is essential to maintain air quality standards and inform people when air pollutant concentrations exceed permissible limits. For example, ground-level ozone, a harmful gas formed by NOxand VOCs emitted from various sources, can be estimated through integration of observation data obtained from measurement sites and effective air-quality models. This paper addresses the problem of predicting air pollution emissions over urban and suburban areas using The Air Pollution Model with Chemical Transport Model (TAPM-CTM) coupled with the Extended Fractional Kaiman Filter (EFKF) based on a Matern covariance function. Here, the ozone concentration is predicted in the airshed of Sydney and surrounding areas, where the length scale parameter I is calculated using station coordinates. For improvement of the air quality prediction, the fractional order of the EFKF is tuned by using a Genetic Algorithm (GA). The proposed methodology is validated at monitoring stations and applied to obtain a spatial distribution of ozone over the region.

This paper is concerned with the guaranteed cost static output feedback (SOF) control for a networked control system (NCS) in imperfect conditions. First, by analyzing the sampling process in connection with network-induced delays and consecutive data packet dropouts and by introducing a new delay function, we formulate such a NCS as a continuous time delay system with the practical assumption that network delays and consecutive dropouts are bounded. Then, based on the delay-dependent approach, sufficient conditions for the existence of a guaranteed cost SOF controller in the NCS are obtained via a set of non-convex matrix inequalities. A cone complementary linearization (CCL) algorithm is used to solve these inequalities to determine a sub-optimal controller that minimizes the guaranteed cost of the NCS. Simulation results are shown to demonstrate the effectiveness of the proposed method.

An extension of the input-to-state stabilisation (ISS) framework for interconnected systems with parameterised quadratic constraints constitutes the main contributions of this paper. The asymptotically dissipative constraint (ADC), that prescribes an approaching characteristic towards a dissipative-bounded property of input and output profiles, is employed in the extended ISS condition in this work. A partially decentralised model predictive control (DeMPC) scheme engaging offline and online computations for constrained interconnected systems is subsequently developed with the ADC-based stability constraint. Herein, we consider the inter-subsystem communication network that has multiple connection topologies. Demands on the communication links and online data are limited with a perturbed state-feedback strategy, which consists of a state feedback and a perturbation. The state feedback gains are determined in accordance with the communication network topologies and for constraint satisfactions, while the perturbations are calculated by the online optimisations adopting only decoupled objective functions.

Teaching multivariable control usually involves a certain level of mathematical sophistication and hence requires some labaratorial exemplification of the material given in formal lectures. This paper reports on a hands-on approach to multi-variable control education via the implementation of a model predictive controller on a two-input, two output coupled drive apparatus. This scaled-down system represents many industrial processes while provides an excellent set-up for demonstrating the cross-coupled effects in multi-input multi-output systems. Here, a model predictive controller (MPC) is developed and implemented on the basis of a constrained optimization problem to show control performance via the belt tension and velocity outputs, demonstrate the decoupling capability, and also illustrate such issues as control input saturation, the selection of operating point, reference inputs, and system robustness to external disturbance and varying parameters. The implementation is based on Labview and MATLAB Model Predictive Control Toolbox.

This paper presents a new stabilizing method for the control of complex systems operating in semi-automatic modes. The complex system is modeled by several spatially-coupled subsystems interconnected in parallel, serial and cycle configurations. Each subsystem is regulated by a dedicated autonomous controller that also allows for a manual control mode. An interconnection stability condition which takes the couplings between subsystems into consideration is derived from the renowned dis-sipative systems theory. Built upon this stability condition, decentralized stabilizing agents for autonomous controllers are subsequently deployed independently and segregatedly from the control algorithms. Due to this independence, human errors from man-machine interactions, that may destabilize the control systems, can be avoidable; also different types of control algorithms and controllers of subsystems are interoperable with the same stabilizing mechanism. To accomplish such tasks simultaneously, the stabilizing agents render overriding outputs for the automatic controllers, and at the same time, provide instability warning signals and manipulation guidance to the operators to successfully regulate the subsystems in the manual control mode, yet maintain the plant-wide stability. Real-time data of control inputs and plant outputs is exerted under the auspices of controller dissipativity indices and trajectories to stabilize the systems with closed-loop control and man-in-the-loop coexistence. Our main results are illustrated in simulation for a three-unit system.

In air quality modelling, determination of the background ozone level is essential as it highly affects the accuracy of the photochemical air quality model. It is known that the background ozone level, especially in urban areas, has been changing over the years. Unfortunately, the reasons of that alteration were not clear and the background ozone itself was not easily derived in practice. In this paper, a new background ozone model will be developed by using the ozone ambient quality data and the meteorological data at the several stations in the Sydney basin. To accomplish the modelling process, an adaptively-tuned radial basis function neural network metamodel is proposed and utilised in the simulation. Different input parameters are considered to evaluate their influence on the constructed background ozone model. The proposed model, subject to some statistical criteria, demonstrates its capability of estimating the background ozone level with a reasonably good accuracy.

Steering maneuver is essential in robotic motion planning. Despite a lot of steering mechanisms successfully developed in past years, for miniature robots, real-time computation is still a limitation for robot path tracking. The design issues in cooperative control of battery-powered nonholonomic robots rest with the complicacy of the control strategies, the low power consumption and real-time processing capability. Conventionally, the improvement of computing speed mostly relies on the increment of the system clock and often results in some transient loss. Thus, an elaborate control algorithm developed for PC might not work on an embedded system. This paper presents a comprehensive steering algorithm which, via issuing predicaments for computation, will dramatically reduce the resource usage in hardware circuit design. The proposed algorithm is implemented on an embedded system for ubiquitous robotics using the field programmable gate array (FPGA) technology.

The contrast enhancement of gray-level digital images is considered in this paper. In particular, the mean image intensity is preserved while the contrast is enhanced. This provides better viewing consistence and effectiveness. The contrast enhancement is achieved by maximizing the information content carried in the image via a continuous intensity transform function. The preservation of image intensity is obtained by applying gamma-correction on the images. Since there is always a trade-off between the requirements for the enhancement of contrast and preservation of intensity, an improved multiobjective particle swarm optimization procedure is proposed to resolve this contradiction, making use of its flexible algorithmic structure. The effectiveness of the proposed approach is illustrated by a number of images including the benchmarks and an image sequence captured from a mobile robot in an indoor environment.

This paper presents the design of a new hybrid filter for quality improvement of power systems in unbalanced conditions. This filter includes a variable passive filter in parallel with an active filter. The passive part is designed with a varying inductance to reduce the rating of the active part. By using this proposed filter it is possible to compensate for the current harmonics and unbalanced loads with a lower rating active filter compared with conventional ones. Also a simple and accurate method is used to generate the harmonic reference current in the active part of this filter which makes it efficient in unbalanced load conditions. Furthermore, an intelligent hysteresis band current controller is utilized to generate inverter pulses owing to its quick current controllability and easy implementation. Simulation results using the MATLAB Simulink Power System Toolbox show the efficiency and effectiveness of the proposed hybrid filter in elimination of harmonics and balance of the utility current with a desired level of load balancing.

This paper proposes an integral controller design scheme for nonlinear systems based on optimal control and the passivity theorem in order to suppress the effect of external disturbances. The main strategy is to augment an optimal controller with a PI type controller. To guarantee the proposed controller has a desired stability margin, the passivity-based design method is introduced. Here, the inverse optimal control technique is employed to avoid the need of solving a Hamilton-Jacobi equation. An illustrative example is given to show the design procedure and the controller effectiveness.

On-line prediction of unmanned ground vehicle (UGV) behaviour during interactions with terrain is essential for autonomous and safe operations of skid-steering UGVs. This paper presents a fast and accurate algorithm for a new interaction model to predict performance of a UGV running on a particular terrain. By approximating nonlinear relations involved in the interaction between the vehicle and terrain, a closed form of the interaction model is obtained to enable on-line computation. The minimum absolute error criteria are applied to secure accuracy for the proposed method. The development is compared with the other models in terms of both computation speed and accuracy.

Evolutionary computation algorithms, such as the particle swarm optimization (PSO), have been widely applied in numerical optimizations and real-world product design, not only for their satisfactory performances but also in their relaxing the need for detailed mathematical modelling of complex systems. However, as iterative heuristic searching methods, they often suffer from difficulties in obtaining high quality solutions in an efficient manner. Since unnecessary resources used in computation iterations should be avoided, the determination of a proper termination condition for the algorithms is desirable. In this work, termination is cast as a decision-making process to end the algorithm. Specifically, the non-parametric sign- test is incorporated as a hypothetical test method such that a quantifiable termination in regard to specifiable decision-errors can be assured. Benchmark optimization problems are tackled using the PSO as an illustrative optimizer to demonstrate the effectiveness of the proposed termination condition.

When using an extended Kalman filter (EKF) in simultaneous localization and mapping (SLAM) for a mobile robot with bearing-only measurements, it is crucial to correctly assign correspondences between measurements and registered features in the map, otherwise the filter diverges or becomes inconsistent. Conventional methods based on the Mahalanobis distance metric may produce data association ambiguities. Its reliability may further be degraded in bearing-only SLAM due to the limited amount of information delivered from the sensor. The data association process is cast here as that of making a decision based on the sensor measurement as whether to update the EKF or not. For this, cost functions are applied taking into account the interferences from other features. The proposed approach enhances robustness of the data association and consequently assures the performance of bearing-only SLAM. Results from simulations and experiments are included to demonstrate the effectiveness of the method in a typical indoor scenario.

The effects of randomness of control coefficients in particle swarm optimization (PSO) are investigated through empirical studies. The PSO is viewed as a method to solve a coverage problem in the solution space when the global-best particle is reported as the solution. Randomness of the control coefficients, therefore, plays a crucial role in providing an efficient and effective algorithm. Comparisons of performances are made between the uniform and Gaussian distributed random coefficients in adjusting particle velocities. Alternative strategies are also tested, they include: i) pre-assigned randomness through the iterations, ii) selective hybrid random adjustment based on the fitness of the particles. Furthermore, the effect of velocity momentum factor is compared between a constant and random momentum. Numerical results show that performances of the proposed variations are comparable to the conventional implementation for simple test functions. However, enhanced performances using the selective and hybrid strategy are observed for complicate functions.

This paper presents an effective methodology for the control of a group of mobile robots moving in desired formations. Virtual head robot tracking and three-point l-l controllers incorporated with reactive control schemes are proposed here to overcome the cases when the control law is undefined and subject to potential inter-robot collision. In order for the whole group to enter a desired formation, a general procedure is suggested in the initialisation stage. Advantages of our approach include the establishment and maintenance of any formation from arbitrary initial conditions with collision avoidance for a large group of mobile robots. The approach is validated through simulation cases to illustrate the capability of handling singularities and avoiding collision in a group of N robots

In this paper, the possibility and necessity of multi-step trajectory planning in Extended Kalman Filter (EKF) based SLAM is investigated. The objective of the trajectory planning here is to minimize the estimation error of the robot and landmark locations subject to a given time horizon. We show that the problem can be regarded as an optimization problem for a gradually identified model. A numerical method is proposed for trajectory planning using a variant of the nonlinear Model Predictive Control (MPC). The proposed method is optimal in the sense that the control action is computed using all the information available at the time of decision making. Simulation results are included to compare the results from the one-step look-ahead trajectory planning and the proposed multi-step look-ahead technique.

Use of a Gaussian Sum filter (GSF) to efficiently solve the initialisation problem in bearing-only simultaneous localisation and mapping (SLAM) is the main contribution of this paper. When information about the range is not available, the initial probability density function (pdf) of a landmark in the environment can not be represented using a Gaussian. The GSF is an attractive candidate for estimation in this scenario as it can deal with arbitrary pdfs represented as sets of Gaussians. However, the implementation of the GSF requires maintaining a bank of extended Kalman filters. The resulting computational complexity needs to be reduced by employing a minimum number of filters. In this work, the performance of each extended Kalman filter (EKF) in the GSF is evaluated using the sequential probability ratio test (SPRT). As such the number of members in the Gaussian sum can be reduced rapidly and the efficiency of the GSF can be significantly increased, providing a solution to the important problem of bearing-only SLAM. The effectiveness of the proposed approach is demonstrated by simulation and experiment conducted using a Pioneer mobile robot.

Speech activated interface between human and autonomous/semi-autonomous systems requires accurate voice detection and recognition. In this area, pitch and end-point detection is of vital importance. This paper presents a new method for pitch detection based on the phase of the continuous wavelet transform. The advantage of the proposed technique is that it can serve not only as an accurate pitch detector, but also can offer an efficient solution to the end-point detection problem. Experimental results are provided for the detection of pitch periods and end points in a neural-network based voice enabled wheelchair system.

This paper presents the design of fuzzy rotating sliding mode controllers with an application to the swing control problem of electrohydraulic servo systems of a robotic excavator. By introducing the integral of differential pressure as an auxiliary state, the dynamic model of an electrohydraulic servo system can be cast as a second-order coupled nonlinear system. Using a successive loop closure procedure, the differential pressure is first considered as the control input for position tracking. The servovalve command voltage required to track the desired differential pressure is then computed. Fuzzy rotating sliding surfaces are applied to achieve fast tracking and robustness to uncertainties. The validity and advantage of the proposed technique are verified through simulation of the cab swing electrohydraulic servo system of a robotic excavator. Control implementation and experimental results are provided. The results obtained indicate the feasibility of the control technique in lower level control applications of the hydraulically-actuated robotic excavator.

In this paper, a fuzzy global controller based on sliding mode control is proposed for a reasonable amalgamation of local controllers to meet various control requirements for complex systems operating in highly uncertain environments. Also, a fuzzy logic approach is developed for the smooth allocation of several algorithms of a controller to gain the advantages, and at the same time to alleviate the disadvantages of each control algorithm. Illustrative examples are provided.

Compensating for friction is considered in the paper using the variable structure systems approach. First, variable structure-based observers are developed for friction estimation in mechanical systems with or without information of velocity. The estimates are then used for a model-based feedforward compensation for friction. For a non-model based approach, a robust sliding mode controller can also be used to cancel the influence of friction. Sigmoidal functions are used in lieu of signum functions to reduce chattering. Simulation results verify the validity of the proposed technique to compensate for friction of both static and dynamic models.

The application of a robust sliding mode control method to the load frequency control problem of a single area power system is considered. The control signal consists of an equivalent control, switching control and fuzzy control. The influence of not only load changes but also parameter variations and governor's backlash is considered with and without generation-rate constraints. Simulation results demonstrate that the system responses are strongly robust to load disturbances and parameter variations even in the presence of governor's backlash deadband and imposed generation physical constraints.

This paper presents a novel robust sliding mode controller for a class of nonlinear systems. The control signal consists of: an equivalent control to assign the desired dynamics to the closed-loop system, a switching control to guarantee a sliding mode, and a fuzzy control to enhance fast tracking and to attenuate chattering. Sufficient conditions for asymptotic tracking are achieved when matching conditions are satisfied. The control method is applied to the position control of an electrohydraulic servo system. To verify the potential and effectiveness of the proposed controller, simulation results are given to demonstrate its strong robustness against a large range of parameter variations, load disturbance and nonlinear spring stiffness.