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As a prosthesis made to compensate for the residual loss of the amputee's limb, the shoulder disarticulation upper limb prosthesis replaces the missing arm function of the shoulder amputee to a certain extent. However, the current upper limb prosthesis mainly interacts with the outside world through the prosthetic hand for grasping and gripping, and the interaction between other parts and the environment is often neglected, which is not in line with the use habits of the human arm. To address this problem, this paper proposes a reinforcement learning–based method for controlling the forearm interaction of a shoulder-disconnected upper limb prosthesis, and analyzes and solves the forces during the interaction, reducing the impact of uncertainty on interaction actions and accelerating training while ensuring the stability of handheld items. We evaluated the performance of the control method during the interaction between the upper limb prosthesis and the external environment through simulation experiments. After the training, the bionic arm was able to push the object into the target range for different objects and pushing distance requirements, which showed the good control effect of the method. Also, the control method can be applied to improve the interaction between the robotic arm and the environment. [ABSTRACT FROM AUTHOR]

In the semiconductor manufacturing process, extended state observer (ESO)-based run-to-run (RtR) control is an intriguing solution. Although an ESO-RtR control strategy can effectively compensate for the lumped disturbance, appropriate gains are required. In this article, a cutting-edge deep reinforcement learning (DRL) technique is integrated into ESO-RtR, and a composite control framework of DRL-ESO-RtR is developed. In particular, the well-trained DRL agent serves as an assisted controller, which produces appropriate gains of ESO. The optimized ESO then presents a preferable control recipe for the manufacturing process. Under the RtR framework, the gain adjustment problem of ESO is formulated as a Markov decision process. An efficient state space and reward function are wisely designed using the system's observable information. Correspondingly, the gain of the ESO is adaptively adjusted to cope with changing environmental disturbances. Finally, a twin-delayed deep deterministic policy gradient algorithm is employed to implement the suggested scheme. The feasibility and superiority of the developed method are validated in a deep reactive ion etching process. Comparative results demonstrate that the presented scheme outperforms the ordinary ESO-RtR controller in terms of disturbance rejection. [ABSTRACT FROM AUTHOR]

Using electroencephalography (EEG) signals to drive a vehicle could help disabled people expand their range of motion and improve their independence. A brain-controlled vehicle (BCV) is a vehicle that is commanded by analyzing EEG signals. However, the analysis and transmission effect of EEG signals is not ideal, the driving performance of the BCV solely relying on EEG signals is relatively poor. In this paper, to solve this problem, we propose a dynamic shared control method based on adaptive network-based fuzzy inference system (ANFIS). First, an ANFIS intelligent controller is designed to automatically make decisions according to the state of the vehicle. Then, safety coefficient and intention coefficient are proposed to evaluate the safety and driving intention of the brain-controlled driver. Finally, a fuzzy controller with safety and intention coefficients as inputs and brain-controlled driver weights as outputs is designed. The controller is the embodiment of a human–machine interaction, which allows the driver to maintain maximum control authority over the BCV under safe conditions by dynamically balancing the control authority of the brain-controlled driver and the ANFIS controller on the BCV. To verify the effectiveness of the proposed method, a joint simulation platform of Carsim and Matlab is established, and several groups of comparative simulation experiments are carried out, through which, it is demonstrated that the proposed method can effectively avoid road deviation while well maintaining the control authority of the brain-controlled driver. [ABSTRACT FROM AUTHOR]

The anti-lock brake system (ABS) is an active safety device used in ground vehicles to increase the brake force between the tire and the road during panic braking. Due to the high non-linearity of the tire and road interaction plus uncertainties derived from vehicle dynamics, a standard proportional-integral-derivative (PID) controller is not deemed enough for the system to produce optimum performance. An active force control (AFC) based scheme is proposed to enhance the robustness of the system and reject undesirable disturbances. A P-type iterative learning algorithm (ILA) is implemented in the AFC loop to estimate the vital parameter continuously for force feedback compensation. In this paper, the control scheme to be known as PID-ILAFC was validated experimentally through its implementation on a test rig. A hardware-in-the-loop (HIL) test via LabVIEW was formulated with novel intelligent control schemes to execute the algorithm in real-time, thereby practically verifying the response of the ABS in the wake of parametric changes and varied operating and loading conditions. The PID-ILAFC controller is specifically designed to provide a proper slip ratio close to the reference value, a reduced stopping distance, and stability in vehicle movement during panic braking. The results clearly exhibit more robustness and superior performance of the AFC-based ABS in achieving the reduced stopping distance and good slip ratio in comparison to the PID and passive counterparts for a dry road condition setting. [ABSTRACT FROM AUTHOR]

Natural gas control ball valves are fluid control devices that regulate the flow and direction of the medium in a natural gas transportation system, and nowadays the requirements for valve intelligence are increasing. To implement the intelligent control of the valve system, the precise acquisition of the operating parameters of the control ball valve was studied in this paper. A numerical simulation of the internal flow field of a V-shaped ball valve was conducted based on a finite volume method and an renormalization group (RNG) k–ε turbulence model. A valve performance test bench was established, and the pressures at the inlet and outlet of the valve at various relative openings were measured, in which the accuracy of the numerical model was verified. Through numerical simulations, the internal flow characteristics at various relative openings and spool cone angles were studied. By comparing the influence of three different relative openings on the valve internal flow field, it was found that the valve showed better flow characteristics under the relative opening of 80%, and the increase of the cone angle effectively improved the unstable flow in the valve. An accurate expression between the external parameters of the valve operating condition and the internal flow was established, and the expression is the theoretical basis to implement intelligent control. This study provides strong technical support for an efficient operation of natural gas pipelines. [ABSTRACT FROM AUTHOR]

The design of an efficient controller for a process of batch crystallization is still an important area of ongoing research. In this paper, a β -variable adaptive model-free control (β -AMFC) has been proposed to control a seeded batch adipic acid crystallization process to get a final unimodal crystal size distribution (CSD) within the required mean weight size. The β -AMFC is developed based on β online adaptive estimation algorithm to enhance the classic model-free control's (MFC) performance. It copes with unknown as well as partially known dynamics and the inherent complex behavior of this nonlinear process. The concept consists of using an updated local approximation model. Two structures of β -AMFC are considered. The first is a β -AMFC based on intelligent Proportional-Integral (β -iPI) control and the second is based on intelligent Proportional-Integral control combined with Sliding Mode Control (β -iPISMC). To obtain the controller parameters of the two β -AMFC structures, the particle swarm optimization (PSO) method was employed. The comparison of alternative approaches is performed by means of simulations including different scenarios. It is found that the β -iPISMC has a superior performance: it allows the output convergence 50 minutes earlier than the β -iPI and it is more robust against any sudden disturbances. [ABSTRACT FROM AUTHOR]

In addition to the difficulty of system modeling, every physical system faces actuator saturation, making the real control different from the controller output (desired control input). When this occurs, the controller output does not regulate the system properly and errors occur as a result of incorrect updating. This is known as the windup phenomenon. In the event that the controller is configured to override actuator saturation, it can lead to a failure in the controller's performance, such as large overshoots, high response time, and even system instability in the worst case. Therefore, in this paper, a new robust model-free controller method is proposed. It is a combination of four nonlinear control techniques, namely model-free controller, fractional-order sliding mode controller, fractional integral–proportional (PI) controller, and anti-windup compensator, which gives rise to the new model-free controller algorithm called hybrid fractional-order intelligent PI fractional sliding mode controller with an anti-windup compensator termed (MF-FOiPI-FOSMC-AW). However, to illustrate the effectiveness of the new proposed MF-FOiPI-FOSMC-AW method, simulation and experimental results compared to classical PI and iPI controllers (with and without an anti-windup compensator) on a hydraulic system are presented in the presence or absence of external disturbances for different types of references. Finally, simulation is conducted through an experimental comparison of the proposed approach with other strategies such as the classical anti-windup PI controller and the anti-windup intelligent PI controller, which is carried out for this purpose. [ABSTRACT FROM AUTHOR]

During the operation of an underwater tracked vehicle on a soft substrate, the instantaneous center of rotation (ICR) no longer coincides with the geometric center after the longitudinal offset is accounted for by the external environment. Due to this, designing an underwater trajectory tracking system based on a kinematic model is a challenging task. In order to achieve the high-precision trajectory tracking control of underwater tracked vehicles, this paper proposes a trajectory tracking algorithm based on the kinematic model of tracked vehicles. First, an inverse tangent algorithm is proposed to address the problem that the front viewpoint of the Stanley algorithm may not be on the target path and cause deviation. Second, by using the inverse tangent algorithm to introduce the target bow angle, as well as combining the mechanisms by which biological endocrine hormones are regulated with the advantages of single-neuron proportional–integral–derivative (PID), a composite endocrine intelligent control law is proposed for the design of the lateral control law of an underwater tracked vehicle; a longitudinal control law is designed based on the longitudinal offset of the ICR obtained by the estimation of the unscented Kalman filter as a means of compensating for longitudinal errors. Finally, a kinematic model is used in order to determine the desired drive wheel speed of the underwater tracked vehicle. The method is compared with the method without longitudinal deviation compensation, then with the methods presented in related literature, and finally, a certain degree of experimental validation is carried out. These results validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive intelligent controller is developed for the velocity-tracking problem of a nonholonomic wheeled mobile robot (WMR) in the presence of external disturbances and measurement noises. The whole control system is consisting of two subsystems, where each subsystem has its own control responsibility. In this way, first, a kinematic controller is implemented according to the kinematic model of the robot, and then a dynamic controller is designed based on the characteristic of the robot dynamics. Our focus is designing and developing an adaptive fractional-order fuzzy logic proportional--integral--derivative (FOFPID) controller for the trajectory-tracking task in a two-WMR. Unlike the prevalent works which only designed the scaling factors of FOFPID, a simultaneous optimization of fuzzy membership functions and controller coefficients are realized to improve the efficiency of the WMR dynamic controller. Accordingly, the controller parameters are optimally adjusted by employing a combination of the sin cos algorithm and harmony search, called SCA-HS. To validate the applicability of the suggested framework, experimental studies are also conducted on a real-time platform using a two-WMR prototype. The experimental results confirm the effectiveness of the proposed controller for the exact trajectory-tracking problem in the presence of disturbances and noises. [ABSTRACT FROM AUTHOR]

The generous application of robot-assisted minimally invasive surgery (RAMIS) promotes human-machine interaction (HMI). Identifying various behaviors of doctors can enhance the RAMIS procedure for the redundant robot. It bridges intelligent robot control and activity recognition strategies in the operating room, including hand gestures and human activities. In this paper, to enhance identification in a dynamic situation, we propose a multimodal data fusion framework to provide multiple information for accuracy enhancement. Firstly, a multi-sensors based hardware structure is designed to capture varied data from various devices, including depth camera and smartphone. Furthermore, in different surgical tasks, the robot control mechanism can shift automatically. The experimental results evaluate the efficiency of developing the multimodal framework for RAMIS by comparing it with a single sensor system. Implementing the KUKA LWR4+ in a surgical robot environment indicates that the surgical robot systems can work with medical staff in the future. [ABSTRACT FROM AUTHOR]

Due to the complexity of map modeling, the massive computation and high redundancy of the traditional A* algorithm will greatly reduce the efficiency of pathfinding, resulting in huge performance consumption. Meanwhile, limited by neighborhood search strategy in grid map, the traditional A* algorithm is actually unable to achieve the optimal path in the global sense. To solve these problems, this paper proposes an improved A* algorithm based on graph preprocessing. First, the free space on the map was decomposed into several polygon regions using the improved convex decomposition method based on Maklink. Then, each region was coded into feature nodes according to A* algorithm. Finally, an optimal region passage was found based on the principle of A* algorithm, in which the global optimal path solution was obtained. Compared with the traditional A* algorithm and other classical path planning algorithms, the proposed algorithm has significant advantages in planning speed, path cost, stability, and completeness. [ABSTRACT FROM AUTHOR]

Currently, the most efficient method of resolving the pollution problem of weed management is by using variable spraying technology. In this study, an improved genetic proportional-integral-derivative control algorithm (IGA-PID) was developed for this technology. It used a trimmed mean operator to optimize the selection operator for an improved searching rate and accuracy. An adaptive crossover operator and mutation operator were constructed for a rapid convergence speed. The weed density detection was performed through an image acquisition and processing subsystem which was capable of determining the spraying quantity. The variable spraying control sub-system completed variable spraying operation. The performance of the system was evaluated by simulations and field tests, and compared with conventional methods. The simulation results indicated that the parameters of the overshoot (1.25%), steady-state error (1.21%) and the adjustment time (0.157s) of IGA-PID were the lowest when compared with the standard algorithms. Furthermore, the field validation results showed that the system with the proposed algorithm achieved the optimal performance with spraying quantity error being 2.59% and the respond time being 3.84s. Overall, the variable spraying system based on an IGA-PID meets the real-time and accuracy requirements for field applications which could be helpful for weed management in precise agriculture. [ABSTRACT FROM AUTHOR]

In this study, we propose a fuzzy logic-based autonomous car control system and its deployment into the JavaScript Racer game. The main design goal of the self-driving car control system is to end up with an intelligent vehicle control system that is capable to keep the car finish race in the shortest time as possible while keeping the car on the road course regardless the road conditions. To accomplish such a design goal, the proposed intelligent driving system takes up and merges the frameworks of control theory, fuzzy logic and image processing. The proposed structure is composed of vision-based lane detection system, fuzzy logic-based position reference (FPR) generator, fuzzy logic-based velocity reference (FVR) generator, low level position and velocity controllers. The vision-based lane detection system solves the road curve estimation problem in real time and provides this estimation feedback for the FPR and FVR to generate appropriate position and velocity reference signals. The design of the intelligent FPR and FVR generators is accomplished by converting expert knowledge into fuzzy rules. The generated reference signals from the FPR and FVR are then tracked by low level position and velocity controllers. The proposed structure is capable to percept the environment (i.e. road tracking), make decisions (reference signal generation) and control car dynamics (i.e. speed and position) as expected from an autonomous system. The paper presents the structure and design of the intelligent driving system by providing all the design steps in detail. The efficiency of the fuzzy logic-based car control system is shown with results collected from the game environment. It is revealed that the developed intelligent system can successfully operate in various racetracks and different operating points. [ABSTRACT FROM AUTHOR]

This paper proposes an intelligent technique-based optimal controller for harmonics mitigation to maintain the power quality in renewable energy source (RES)-based distribution systems. The proposed intelligent technique is the joint execution of both the fractional-order proportional integral controller and moth-flame optimization with random decision forest. In the proposed approach, moth-flame optimization optimizes the dataset of fundamental and harmonic loop parameters such as terminal voltage and direct current voltage present in the hybrid shunt active power filter. The dataset is generated based on the linear and nonlinear load variation and parameter variation of the renewable energy sources, subject to the minimum error objective function. Based on the accomplished dataset, random decision forest accurately predicts the parameters and produces optimized control signals. The proposed technique guarantees the system with less complexity for the harmonics mitigation of the power quality event and hence the accuracy of the system is raised. Then, the proposed model is executed in the Matrix Laboratory/Simulink working platform and the execution is assessed with the existing techniques. The simulation analysis of the proposed approach is tested using the six test cases with various combinations of nonlinear loads. In all the test cases, the performance of various system parameters, such as source current with and without filter, source voltage, hybrid shunt active power filter current, load current and voltage, is analysed. Furthermore, the total harmonic distortion at different load ratings is also examined. [ABSTRACT FROM AUTHOR]

This article presents a novel hybrid fractional-order intelligent proportional-integral-derivative (PID) controller integrated with a fractional-order fuzzy PID controller for load frequency control of a realistic interconnected two-area power system, where each area is powered from a different source. The strategy presented here involves incorporating a damping controller based on a thyristor-controlled series capacitor in the system. In doing so, the parameters of the current controller are optimized via craziness-based particle swarm optimization. The effectiveness of the strategy presented here in load frequency control is validated by extensive simulation examples. The results are compared with a recently published technique based on a fractional-order PID controller. Finally, the sensitivity analysis of the plant is studied by varying the plant parameters and operating load conditions from their prescribed values. It is observed that the current controller is a robust controller and performs satisfactorily with variations in system parameters and load patterns. [ABSTRACT FROM AUTHOR]

In this paper, a combination of a multi-layer quantum neural network (QNN) with the particle swarm optimization (PSO) algorithm is used with the aim of controlling a continuous stirred-tank reactor (CSTR) system. The CSTR process is highly non-linear and its dynamics are significantly sensitive to system parameter values. Normally, conventional controllers with fixed coefficients are applied to control this kind of system. In highly non-linear systems, having fixed controller coefficients in different operational conditions may decrease the performance of controllers. In the proposed scheme, by using a multi-layer QNN, an adaptive structure is designed for a PI-D controller. In order to train the QNN, the PSO algorithm is employed. With the aim of improving accuracy and convergence speed of the training process, some modifications have been applied to the movement of each particle towards the optimal point. Furthermore, in order to evaluate the performance of the system, the proposed scheme has been applied in various operational situations in the presence of disturbances and set-point change. The efficiency of the proposed control scheme is compared with PID and a perceptron neural network-based controller, and the simulation results endorse that the proposed scheme shows significantly better performance in different operating conditions. [ABSTRACT FROM AUTHOR]

This paper proposes a novel intelligent optimal control strategy for crawler vehicles with semi-active suspension. The proposed control strategy aims at improving vehicle ride comfort by addressing contradictory suspension properties requirements of ride comfort and handling stability simultaneously. After establishing seven degrees of freedom dynamic model of the crawler vehicle, a comprehensive evaluation index is developed to trade off among various vehicle performances, which include ride comfort, damper thermal reliability, elastic element fatigue and handling stability. Then, using modified staged continuous tabu search (MSCTS) algorithm, the optimal control efforts of semi-active suspension, damping ratios, are determined by minimizing the cost function defined by the comprehensive evaluation index. Demonstrated by simulations with triangle convex block and random ground roughness excitations, MSCTS control strategy can successfully improve ride comfort performance and achieve the optimal comprehensive performance as well. [ABSTRACT FROM AUTHOR]

Dynamic resource allocation in a cloud environment has become possible using virtualization technologies in cloud computing. One of the applications of these technologies is offering various applications by Software-as-a-Service (SaaS) infrastructures. Unfortunately, due to request rate increments in cloud rush hours, the related server cannot serve all the requests according to the service level agreement. Hence, the cloud provider’s quality of service will decrease. Thus a mechanism is required to control the admission rate of requests for cloud servers. In this study, an intelligent controller is designed and implemented on a field-programmable gate array (FPGA) in order to control the admission rate of requests for a SaaS server in the cloud. The controller is based on a brain emotional learning-based intelligent controller (BELBIC). First, an analytical model of a server is proposed and simulated, which shows the behavioural characteristics of a real server. Next, the BELBIC is designed to control the admission rate of the server. Finally, the system is implemented on FPGA hardware and simulated by a synthetic cloud workload in a hardware-in-the-loop manner. In order to compare the performance of the BELBIC, an adaptive neuro-fuzzy inference system (ANFIS) controller in addition to the popular PID controller is provided. The controllers’ efficiencies are compared in terms of server utilization, admission rate, drop rate of requests and the agility of the controllers. The results proved that the BELBIC offers faster rise time compared with the PID controller, which leads to better cloud utilization and a smaller number of dropped requests. [ABSTRACT FROM AUTHOR]

In this paper, a new intelligent control scheme based on multiple models and neural networks is proposed to adaptively control a class of Hammerstein nonlinear systems with arbitrary deadzone input. This approach consists of a linear robust adaptive controller, multiple neural networks-based nonlinear adaptive controllers and a switching mechanism. Since the control input is derived from a modified certainty equivalent principle, the manner in which the closed-loop stability is established forms the main contribution. To show the usefulness of the developed results, three simulation examples, including a direct current motor subject to a nonlinear friction, are studied. [ABSTRACT FROM AUTHOR]

The proposed structure is composed of a vision-based lane detection system, fuzzy logic-based position reference generator, fuzzy logic-based velocity reference generator, and low-level position and velocity controllers. Welcome to the special issue of selected papers from the 6 SP th sp International Conference on Control Engineering & Information Technology (CEIT 2018) held on 25-27 October 2018 in Istanbul, Turkey. Ugur Bugra Etlik et al. propose a fuzzy logic-based autonomous car control system and its deployment into the JavaScript Racer game. [Extracted from the article]

For dexterous interactive manipulations, the robot end-effector must follow a motion trajectory and exert an appropriate force against the contacted environment to provide a specified dynamic working compliance. Hence, the robotic impedance is an important function for future applications of intelligent robots. For this purpose, a generalized hybrid control strategy is developed to monitor the motion trajectory and the end-effector interaction force in the task space simultaneously. Here, the Altera development board-based embedded robotic control structure is constructed and the related intelligent position/force hybrid control strategy is developed. A new model-free intelligent fuzzy sliding mode controller is designed to monitor the force and position specifications for hybrid impedance control purposes. It has the advantages of one-dimensional fuzzy control features with online gain scheduling and sliding mode stability. A force sensor is installed in a Mitsubishi robot end-effector to measure the dynamic contact force during compliant operations. The experimental results show that the dynamic performance of the proposed hybrid impedance control system can reach the contouring error of less than 1 mm and a contact force error of less than 3 N, respectively. [ABSTRACT FROM PUBLISHER]

This work considers the control problem of overhead cranes with flexible cable and load hoisting or lowering during crane travel. The objective is to drive the overhead crane to its final desired position while suppressing the payload swing. The proposed control strategy is based on three fuzzy logic controllers where the control action is based on expert knowledge. These controllers are a trolley motion controller, payload hoist controller and payload anti-swing controller. Numerical simulations are provided to show the effectiveness of the proposed fuzzy control scheme. [ABSTRACT FROM AUTHOR]

A non-linear state feedback algorithm based on an OFS (orthonormal functional series) model combined with Volterra Functional Series is proposed to solve block-oriented non-linear systems. This algorithm has excellent approximation ability for the variance of system time-delay, and can guarantee the closed-loop robust stability and the zero steady-state error property. In addition, it has lower online optimization computational load when dealing with the hard input constraints in contrast to traditional NMPC (non-linear model predictive control) based on the OFS model. The analyses of the stability and robustness of this algorithm are put forward systemically, and the steady-state performance analysis is also given. Case studies involving simulations on a Wiener-type non-linear system and temperature experiments on a simulation chemical reactor are used to validate the efficiency and superiority of this proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, an efficient computer-aided plant species identification (CAPSI) approach is proposed, which is based on plant leaf images using a shape matching technique. Firstly, a Douglas–Peucker approximation algorithm is adopted to the original leaf shapes and a new shape representation is used to form the sequence of invariant attributes. Then a modified dynamic programming (MDP) algorithm for shape matching is proposed for the plant leaf recognition. Finally, the superiority of our proposed method over traditional approaches to plant species identification is demonstrated by experiment. The experimental result showed that our proposed algorithm for leaf shape matching is very suitable for the recognition of not only intact but also partial, distorted and overlapped plant leaves due to its robustness. [ABSTRACT FROM AUTHOR]

Wavelet analysis, as a promising technique, has been used to approach numerous problems in science and engineering. Recent years have witnessed its novel application in economic and finance. This paper is to investigate whether features (or indicators) extracted using the wavelet analysis technique could improve financial forecasting by means of Financial Genetic Programming (FGP), a genetic programming-based forecasting tool. More specifically, to predict whether the Dow Jones Industrial Average (DJIA) Index will rise by 2.2% or more within the next 21 trading days, we first extract some indicators based on wavelet coefficients of the DJIA time series using a discrete wavelet transform; we then feed FGP with those wavelet-based indicators to generate decision trees and make predictions. By comparison with the prediction performance of our previous study, it is suggested that wavelet analysis be capable of bringing in promising indicators, and improving the forecasting performance of FGP. [ABSTRACT FROM AUTHOR]

Although the International Electrotechnical Commission (IEC) codes method has been widely used to interpret dissolved gas-in-oil analysis (DGA) results in the world, it sometimes fails to diagnose, especially when more than one fault exists in a transformer. This paper presents a rough set-based fuzzy logic technique, which can diagnose multiple faults in a transformer. By applying rough set theory to the IEC codes, the reduct of the decision table can be obtained. By using a fuzzy method, the fuzzy membership functions of each fault diagnosis decision rules are displayed, and finally the fault type of the transformer is diagnosed. The application of this method to some transformers has yielded promising results. [ABSTRACT FROM AUTHOR]

It is well known that sliding mode control can give good transient performance and system robustness. However, due to the sensitivity of the sliding mode control before reaching the sliding surface, the presence of chattering may introduce problems to the tracking control of the system. In this paper, a scheme of fuzzy time-varying sliding mode controller with global robustness is proposed for a class of non-linear system with uncertainties. Since the proper fuzzy logic control is applied to the time-varying sliding surface, the state of the control system always lies on the sliding surface from the beginning of any initial state and the reaching phase is avoided. As a consequence, the system is robust with respect to external disturbances and parameter uncertainties. Furthermore, the chattering phenomenon, which is inherent to a conventional sliding mode control, is avoided by proper fuzzy tuning of the controller. The effectiveness of proposed controller is demonstrated through comparative simulations for a second-order non-linear uncertain system. [ABSTRACT FROM AUTHOR]

Automatic estimation of crowd density is very important for the safety management of the crowds. In particular, when the density of the crowds exceeds a critical level, the safety of people in the crowd may be compromised. This paper describes a novel method to estimate the crowd density based on the combination of multi-scale analysis and a support vector machine. The algorithm will first transform the crowd image into multi-scale formats using wavelet transform. The first-order and second-order statistical features at each scale of the transformed images are then extracted as density character vectors. Furthermore, a classifier based on a support vector machine is designed to classify the extracted density character vectors into different density levels. Compared with the conventional statistical techniques and wavelet energy techniques used in single-scale images, the test results of a set of 300 images show that the proposed algorithm can achieve much improved performance and more detailed information of the crowd density can be captured by the new feature extraction method. [ABSTRACT FROM AUTHOR]

The article discusses various reports published within the issue concerning the application of intelligent computing techniques and intelligent control.