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Maintaining the orderliness and efficiency of pedestrian flow through an architectural area is critical for the evacuation process. Especially, clogs and jams are easily triggered in width-changing areas. In this article, we consider pedestrian movement in heterogeneous corridors and design an optimal feedback control to regulate pedestrian flow. Flow characteristics are first studied based on microscopic social-force simulations. A Gaussian process describes the relationship between flow variables with the observation data. The macroscopic model for flow in heterogeneous corridors is developed. To avoid jams, discharges among these corridors are balanced with the narrowest corridor as the primary concern. At the equilibrium, a continuous-time nonlinear control system is formulated, and the adaptive dynamic programming learns the optimal feedback controller. Policy iteration (PI) and neural networks are combined together, and the convergence of neural-network-based PI is demonstrated by analyzing its equivalence to the Gauss–Newton method. Batch normalization is introduced to stabilize the learning process. Simulated experiments demonstrate that the control design can effectively regulate pedestrian flow for both macroscopic and microscopic models. Note to Practitioners—The development of video-processing techniques provides a powerful tool to detect human behavior in real time. In crowd events, the pedestrian movement must be regulated; otherwise, it is easy to fall into the faster-is-slower effect. It is especially important for evacuation routes with different widths. In this article, the optimal feedback control is studied to regulate pedestrian flow in heterogeneous corridors. It takes flow densities as state and produces commands that are composed of entrance influx and free-flow velocities. These commands can be executed with the support of speakers, displays, or the recently developed interactive robots. To avoid congestion, discharges of different corridors are balanced, and the system is optimally stabilized at equilibrium. Based on our work, engineers are able to design pedestrian flow control and achieve optimal evacuation in arbitrary heterogeneous corridors. [ABSTRACT FROM AUTHOR]

For systems that can only be locally stabilized, control laws and their effective regions are both important. In this paper, invariant policy iteration is proposed to solve the optimal control of discrete-time systems. At each iteration, a given policy is evaluated in its invariantly admissible region, and a new policy and a new region are updated for the next iteration. Theoretical analysis shows the method is regionally convergent to the optimal value and the optimal policy. Combined with sum-of-squares polynomials, the method is able to achieve the near-optimal control of a class of discrete-time systems. An invariant adaptive dynamic programming algorithm is developed to extend the method to scenarios where system dynamics is not available. Online data are utilized to learn the near-optimal policy and the invariantly admissible region. Simulated experiments verify the effectiveness of our method. [ABSTRACT FROM AUTHOR]

Cooperative adaptive cruise control (CACC) technique enhances the coordination of vehicles on road. Through CACC controllers, the platoon can exhibit promising traffic performance. In this paper, we study the control synthesis for two types of CACC systems, one of which uses acceleration feedforward while the other uses control feedforward. Under the predecessor-following topology, a general state-space model of vehicle platoon is established and the CACC characteristics including stabilization and string stability are realized by a set of constraints. To relax computation, complete quadratic Lyapunov-Krasovskii functionals are adopted and the CACC controllers are solved by linear matrix inequality tools. The proposed method provides a computationally efficient way to realize CACC design, and comparative experiments with existing work demonstrate the advantages. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new mid-level visual elements discovery method and apply it to the fine-grained classification. We present the duality between image patches and features extracted by the convolutional winner-take-all autoencoder (CONV-WTA-AE). The sparsity constraints used by CONV-WTA-AE make a group of objects sharing the same feature components. Hence, the image patches could be clustered by their sharing feature components and the feature components can be clustered by their co-occurrence in the image patches. We propose formulating the mid-level visual elements mining as a bipartite graph partitioning problem. The spectral partitioning algorithm is employed to co-cluster image patches and feature components. The CONV-WTA-AE is an unsupervised feature learning method. Hence, it avoids using expensive annotations. Our experiments demonstrate that the spectral partitioning method is very efficient but only the confident instances in a cluster are well discriminated. The similarity metric used by this algorithm is not accurate enough. Hence, we propose training a group of linear support vector machine (SVM) to refine the clustering results. These SVMs will be trained on the initial confident instances and provide a better discriminative similarity. Then we can re-assign instances to each clusters. To avoid overfitting, this process is iterated on many data subsets. We conduct a series of experiments on the MNIST dataset to verify our algorithm. The experimental results show that our method can discover meaningful image patch clusters. In the fine-grained classification task, visual elements are input into an ensemble of convolutional neural networks. The experiments on the CompCars dataset illustrate that our method can achieve the state-of-the-art performance. [ABSTRACT FROM AUTHOR]

The adaptive cruise control (ACC) problem can be transformed to an optimal tracking control problem for complex nonlinear systems. In this paper, a novel highly efficient model-free adaptive dynamic programming (ADP) approach with experience replay technology is proposed to design the ACC controller. Experience replay increases the data efficiency by recording the available driving data and repeatedly presenting them to the learning procedure of the acceleration controller in the ACC system. The learning framework that combines ADP with experience replay is described in detail. The distinguishing feature of the algorithm is that when estimating parameters of the critic network and the actor network with gradient rules, the gradients of historical data and current data are used to update parameters concurrently. It is proved with Lyapunov theory that the weight estimation errors of the actor network and the critic network are uniformly ultimately bounded under the novel weight update rules. The learning performance of the ACC controller implemented by this ADP algorithm is clearly demonstrated that experience replay can increase data efficiency significantly, and the approximate optimality and adaptability of the learned control policy are tested with typical driving scenarios. [ABSTRACT FROM AUTHOR]

We propose incremental least squares density difference (LSDD) change detection method, an incremental test to detect changes in stationarity based on the difference between the unknown prechange and the post-change probability density functions (pdfs). The method is computationally light and, hence, adequate to process continuous datastreams, as those emerging from the Internet of Things and the big data framework. The incremental change detection test operates on two nonoverlapping data windows to estimate the LSDD between the two pdfs. We construct a theoretical framework that shows how the distribution of LSDD values follows a linear combination of \chi ^2 distributions and provides thresholds to control false positive rates. The proposed test can operate online, with needed estimates and thresholds computed incrementally as fresh samples come. Comprehensive experiments validate the effectiveness of the test both in detecting abrupt and drift types of changes. [ABSTRACT FROM AUTHOR]

In this paper, the H\infty optimal control problem for a class of continuous-time nonlinear systems is investigated using event-triggered method. First, the H\infty optimal control problem is formulated as a two-player zero-sum (ZS) differential game. Then, an adaptive triggering condition is derived for the ZS game with an event-triggered control policy and a time-triggered disturbance policy. The event-triggered controller is updated only when the triggering condition is not satisfied. Therefore, the communication between the plant and the controller is reduced. Furthermore, a positive lower bound on the minimal intersample time is provided to avoid Zeno behavior. For implementation purpose, the event-triggered concurrent learning algorithm is proposed, where only one critic neural network (NN) is used to approximate the value function, the control policy and the disturbance policy. During the learning process, the traditional persistence of excitation condition is relaxed using the recorded data and instantaneous data together. Meanwhile, the stability of closed-loop system and the uniform ultimate boundedness (UUB) of the critic NN’s parameters are proved by using Lyapunov technique. Finally, simulation results verify the feasibility to the ZS game and the corresponding H\infty control problem. [ABSTRACT FROM AUTHOR]

In this paper, the fully cooperative game with partially constrained inputs in the continuous-time Markov decision process environment is investigated using a novel data-driven adaptive dynamic programming method. First, the model-based policy iteration algorithm with one iteration loop is proposed, where the knowledge of system dynamics is required. Then, it is proved that the iteration sequences of value functions and control policies can converge to the optimal ones. In order to relax the exact knowledge of the system dynamics, a model-free iterative equation is derived based on the model-based algorithm and the integral reinforcement learning. Furthermore, a data-driven adaptive dynamic programming is developed to solve the model-free equation using generated system data. From the theoretical analysis, we prove that this model-free iterative equation is equivalent to the model-based iterative equations, which means that the data-driven algorithm can approach the optimal value function and control policies. For the implementation purpose, three neural networks are constructed to approximate the solution of the model-free iteration equation using the off-policy learning scheme after the available system data is collected in the online measurement phase. Finally, two examples are provided to demonstrate the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

Event-triggered control has been an effective tool in dealing with problems with finite communication and computation resources. In this paper, we design an event-triggered control for nonlinear constrained-input continuous-time systems based on the optimal policy. Constraints on controls are handled using a bounded function. To learn the optimal solution with partially unknown dynamics, an online adaptive dynamic programming algorithm is proposed. The identifier network, the critic network, and the actor network are employed to approximate the unknown drift dynamics, the optimal value, and the optimal policy, respectively. The identifier is tuned based on online data, which further trains the critic and actor at triggering instants. A concurrent learning technique repeatedly uses past data to train the critic. Stability of the closed-loop system, and convergence of neural networks to the optimal solutions are proved by Lyapunov analysis. In the end, the algorithm is applied to the overhead crane system to observe the performance. The event-triggered optimal controller with constraints stabilizes the system and consumes much less sampling times. [ABSTRACT FROM AUTHOR]

The optimal tracking of non‐linear systems without knowing system dynamics is an important and intractable problem. Based on the framework of reinforcement learning (RL) and adaptive dynamic programming, a model‐free adaptive optimal tracking algorithm is proposed in this study. After constructing an augmented system with the tracking errors and the reference states, the tracking problem is converted to a regulation problem with respect to the new system. Several RL techniques are synthesised to form a novel algorithm which learns the optimal solution online in real time without any information of the system dynamics. Continuous adaptation laws are defined by the current observations and the past experience. The convergence is guaranteed by Lyapunov analysis. Two simulations on a linear and a non‐linear systems demonstrate the performance of the proposed approach. [ABSTRACT FROM AUTHOR]

Reinforcement learning offers a promising way for self‐learning control of an unknown system, but it involves the issues of policy evaluation and exploration, especially in the domain of continuous state. In this study, these issues are addressed from the perspective of probability. It models the action value function as the latent variable of Gaussian process, while the reward as the observed variable. Then an online approach is proposed to update the action value function by Bayesian inference. Taking an advantage of the proposed framework, a prior knowledge can be incorporated into the action value function, and thus an efficient exploration strategy is presented. At last, the Bayesian‐state‐action‐reward‐state‐action algorithm is tested on some benchmark problems and empirical results show its effectiveness. [ABSTRACT FROM AUTHOR]

In this paper, a self-learning control scheme is proposed for the infinite horizon optimal control of affine nonlinear systems based on the action dependent heuristic dynamic programming algorithm. The policy iteration technique is introduced to derive the optimal control policy with feasibility and convergence analysis. It shows that the “greedy” control action for each state is uniquely existent, the learned control policy after each policy iteration is admissible, and the optimal control policy is able to be obtained. Two three-layer perceptron neural networks are employed to implement the scheme. The critic network is trained by a novel rule to conform to the Bellman equation, and the action network is trained to yield a better control policy. Both training processes alternate until the optimal control policy is achieved. Two simulation examples are provided to validate the effectiveness of the approach. [ABSTRACT FROM AUTHOR]

This paper proposes a probably approximately correct (PAC) algorithm that directly utilizes online data efficiently to solve the optimal control problem of continuous deterministic systems without system parameters for the first time. The dependence on some specific approximation structures is crucial to limit the wide application of online reinforcement learning (RL) algorithms. We utilize the online data directly with the kd-tree technique to remove this limitation. Moreover, we design the algorithm in the PAC principle. Complete theoretical proofs are presented, and three examples are simulated to verify its good performance. It draws the conclusion that the proposed RL algorithm specifies the maximum running time to reach a near-optimal control policy with only online data. [ABSTRACT FROM AUTHOR]

In this paper, a type of fuzzy system structure is applied to heuristic dynamic programming (HDP) algorithm to solve nonlinear discrete-time Hamilton–Jacobi–Bellman (DT-HJB) problems. The fuzzy system here is adopted as a 0-order T–S fuzzy system using triangle membership functions (MFs). The convergence of HDP and approximability of the multivariate 0-order T–S fuzzy system is analyzed in this paper. It is derived that the cost function and control policy of HDP can be iterated to the DT-HJB solution and optimal policy. The multivariate 0-order T–S (Tanaka–Sugeno) fuzzy system using triangle MFs is proven as a universal approximator, to guarantee the convergence of the Fuzzy-HDP mechanism. Some simulations are implemented to observe the performance of the proposed method both in mathematical solution and practical issue. It is concluded that Fuzzy-HDP outperforms traditional optimal control in more complex systems. [ABSTRACT FROM AUTHOR]

Abstract: The paper proposes a novel iterative control scheme based on neural networks for optimally controlling a large class of nonlinear discrete-time systems affected by an unknown time variant delay and system uncertainties. An iterative Dual Heuristic dynamic Programming (DHP) algorithm has been envisaged to design the controller which is proven to converge to the optimal one. The key elements required by the DHP, namely the performance index function, the optimal control policy and the nonlinear delay-affected discrete-time system are modeled with feedforward neural networks. Examples demonstrate the validity of the proposed optimal control approach and its effectiveness in dealing with nonlinear time delay situations. [Copyright &y& Elsevier]

Abstract: The paper proposes a supervised adaptive dynamic programming (SADP) algorithm for a full-range adaptive cruise control (ACC) system, which can be formulated as a dynamic programming problem with stochastic demands. The suggested ACC system has been designed to allow the host vehicle to drive both in highways and in Stop and Go (SG) urban scenarios. The ACC system can autonomously drive the host vehicle to a desired speed and/or a given distance from the target vehicle in both operational cases. Traditional adaptive dynamic programming (ADP) is a suitable tool to address the problem but training usually suffers from low convergence rates and hardly achieves an effective controller. A SADP algorithm which introduces the concept of inducing region is here introduced to overcome such training drawbacks. The SADP algorithm performs very well in all simulation scenarios and always better than more traditional controllers. The conclusion is that the proposed SADP algorithm is an effective control methodology able to effectively address the full-range ACC problem. [Copyright &y& Elsevier]

A novel supervised Actor-Critic (SAC) approach for adaptive cruise control (ACC) problem is proposed in this paper. The key elements required by the SAC algorithm namely Actor and Critic, are approximated by feed-forward neural networks respectively. The output of Actor and the state are input to Critic to approximate the performance index function. A Lyapunov stability analysis approach has been presented to prove the uniformly ultimate bounded property of the estimation errors of the neural networks. Moreover, we use the supervisory controller to pre-train Actor to achieve a basic control policy, which can improve the training convergence and success rate. We apply this method to learn an approximate optimal control policy for the ACC problem. Experimental results in several driving scenarios demonstrate that the SAC algorithm performs well, so it is feasible and effective for the ACC problem. [ABSTRACT FROM AUTHOR]

Load frequency control (LFC) is one of the most profitable ancillary services of power systems. Governor dead band (GDB) nonlinearity is able to deteriorate the LFC performance. In this paper, controller design via a neural sliding-mode method is investigated for the LFC problem of power systems with GDB. Power systems are made up of areas. In each area, a sliding-mode LFC controller is designed by introducing an additional sate, and a RBF neural network is utilized to compensate the GDB nonlinearity of the area. Weight update formula of the RBF network is derived from Lyapunov direct method. By this scheme, not only the update formula is obtained, but also the control system possesses the asymptotic stability. Simulation results illustrate the feasibility and robustness of the presented approach for the LFC problems of single-area and multi-area power systems. [ABSTRACT FROM AUTHOR]

Urban transportation system is a large complex nonlinear system. It consists of surface-way networks, freeway networks, and ramps with a mixed traffic flow of vehicles, bicycles, and pedestrians. Traffic congestions occur frequently, which affect daily life and pose all kinds of problems and challenges. Alleviation of traffic congestions not only improves travel safety and efficiencies but also reduces environmental pollution. Among all the solutions, traffic signal control (TSC) is commonly thought as the most important and effective method. TSC algorithms have evolved quickly, especially over the past several decades. As a result, several TSC systems have been widely implemented in the world, making TSC a major component of intelligent transportation system (ITS). In TSC and ITS, many new technologies can be adopted. Computational intelligence (CI), which mainly includes artificial neural networks, fuzzy systems, and evolutionary computation algorithms, brings flexibility, autonomy, and robustness to overcome nonlinearity and randomness of traffic systems. This paper surveys some commonly used CI paradigms, analyzes their applications in TSC systems for urban surface-way and freeway networks, and introduces current and potential issues of control and management of recurrent and nonrecurrent congestions in traffic networks, in order to provide valuable references for further research and development. [ABSTRACT FROM PUBLISHER]

Abstract: In this paper adaptive dynamic programming (ADP) is applied to learn to play Gomoku. The critic network is used to evaluate board situations. The basic idea is to penalize the last move taken by the loser and reward the last move selected by the winner at the end of a game. The results show that the presented program is able to improve its performance by playing against itself and has approached the candidate level of a commercial Gomoku program called 5-star Gomoku. We also examined the influence of two methods for generating games: self-teaching and learning through watching two experts playing against each other and presented the comparison results and reasons. [Copyright &y& Elsevier]

The use of room temperature ionic liquids as either solvents or catalysts has been the subject of considerable recent attention because of the prospects for “green” catalysis. This paper presents a review of the potential applications of these unique liquid materials in industrial catalysis. [Copyright &y& Elsevier]

An introduction is presented in which the editor discusses various reports within the issue on topics including intelligent control, information processing, and passivity of neural networks.

Background: Peri-operative chemo-radiotherapyplayed important rolein locally advanced gastric cancer. Whether preoperative strategy can improve the long-term prognosis compared with postoperative treatment is unclear. The study purpose to compare oncologic outcomes in locally advanced gastric cancer patients treated with preoperative chemo-radiotherapy (pre-CRT) and postoperative chemo-radiotherapy (post-CRT).Methods: From January 2009 to April 2019, 222 patients from 2 centers with stage T3/4 and/or N positive gastric cancer who received pre-CRT and post-CRT were included. After propensity score matching (PSM), comparisons of local regional control (LC), distant metastasis-free survival (DMFS), disease-free survival (DFS) and overall survival (OS) were performed using Kaplan-Meier analysis and log-rank test between pre- and post-CRT groups.Results: The median follow-up period was 30 months. 120 matched cases were generated for analysis. Three-year LC, DMFS, DFS and OS for pre- vs. post-CRT groups were 93.8% vs. 97.2% (p = 0.244), 78.7% vs. 65.7% (p = 0.017), 74.9% vs. 65.3% (p = 0.042) and 74.4% vs. 61.2% (p = 0.055), respectively. Pre-CRT were significantly associated with DFS in uni- and multi-variate analysis.Conclusion: Preoperative CRT showed advantages of oncologic outcome compared with postoperative CRT.Trial Registration: ClinicalTrial.gov NCT01291407 , NCT03427684 and NCT04062058 , date of registration: Feb 8, 2011. [ABSTRACT FROM AUTHOR]

Visual navigation is an essential task for indoor robots and usually uses the map as assistance to providing global information for the agent. Because the traditional maps match the environments, the map-based and map-building-based navigation methods are limited in the new environments for obtaining maps. Although the deep reinforcement learning navigation method, utilizing the non-map-based navigation technique, achieves satisfactory performance, it lacks the interpretability and the global view of the environment. Therefore, we propose a novel abstract map for the deep reinforcement learning navigation method with better global relative position information and more reasonable interpretability. The abstract map is modeled as a Markov network which is used for explicitly representing the regularity of objects arrangement, influenced by people activities in different environments. Besides, a knowledge graph is utilized to initialize the structure of the Markov network, as providing the prior structure for the model and reducing the difficulty of model learning. Then, a graph neural network is adopted for probability inference in the Markov network. Furthermore, the update of the abstract map, including the knowledge graph structure and the parameters of the graph neural network, are combined into an end-to-end learning process trained by a reinforcement learning method. Finally, experiments in the AI2THOR framework and the physical environment indicate that our algorithm greatly improves the success rate of navigation in case of new environments, thus confirming the good generalization. [ABSTRACT FROM AUTHOR]

Background. The prognostic relevance of gastric tumor location has been reported and debated. Our study was conducted to examine the differences in clinicopathological features, prognostic factors, and overall survival (OS) between patients with proximal gastric cancer (PGC) and distal gastric cancer (DGC). Patients and Methods. Patients with PGC or DGC were identified from the China National Cancer Center Gastric Cancer Database (NCCGCDB) during 1997–2017. Survival analysis was performed via Kaplan-Meier estimates and Cox proportional hazards models. Results. We reviewed 16,119 cases of gastric cancer patients, including 6,479 of PGC and 9,640 of DGC. PGC patients presented as older patients (61.5 versus 56.4 years, P<0.001) and more males (82.9% versus 68.2%, P<0.001). Compared with DGC, PGC was more likely to be in later pT stage (pT3 and pT4, 65.0% versus 52.8%, P<0.001) and lymph node metastasis (54.8% versus 50.9%, P<0.001). In univariate analysis, PGC patients had a worse survival outcome in stage I (Hazard ratio [HR] = 2.04, 95% CI: 1.42-2.94) but a better prognosis in stage IV (HR = 0.85, 95% CI: 0.73-0.98) when compared to DGC patients. However, multivariate analysis demonstrated that PGC was not an independent predictor for poor survival (HR = 1.07, 95% CI: 1.00-1.14). Results from multivariate analysis also revealed that pT4, lymph node metastasis, distant metastasis, no gastrectomy, and Borrmann IV were independent predictors associated with poor survival for both PGC and DGC patients. Additional prognostic factors for PGC patients included underweight (BMI < 18.5) (HR = 1.29, 95% CI: 1.06-1.58), linitis plastica (HR = 2.13, 95% CI: 1.25-3.65), and overweight (23 ≤ BMI <27.5) (HR = 0.80, 95% CI: 0.71-0.90). During the 20-year study period, the 5-year OS increased significantly for both PGC and DGC, with the increase rate of 91.7% and 67.7%, respectively. Conclusion. In China, PGC significantly differed from DGC in clinicopathological characteristics and prognostic factors. However, there was no significant relationship between survival outcome and gastric tumor location. [ABSTRACT FROM AUTHOR]

In this article, we propose BNAS-v2 to further improve the efficiency of broad neural architecture search (BNAS), which employs a broad convolutional neural network (BCNN) as the search space. In BNAS, the single-path sampling-updating strategy of an overparameterized BCNN leads to terrible unfair training issue, which restricts the efficiency improvement. To mitigate the unfair training issue, we employ a continuous relaxation strategy to optimize all paths of the overparameterized BCNN simultaneously. However, continuous relaxation leads to a performance collapse issue that leads to the unsatisfactory performance of the learned BCNN. For that, we propose the confident learning rate (CLR) and introduce the combination of partial channel connections and edge normalization. Experimental results show that 1) BNAS-v2 delivers state-of-the-art search efficiency on both CIFAR-10 (0.05 GPU days, which is $4\times $ faster than BNAS) and ImageNet (0.19 GPU days) with better or competitive performance; 2) the above two solutions are effectively alleviating the performance collapse issue; and 3) BNAS-v2 achieves powerful generalization ability on multiple transfer tasks, e.g., MNIST, FashionMNIST, NORB, and SVHN. The code is available at https://github.com/zixiangding/BNASv2 . [ABSTRACT FROM AUTHOR]

The ability to detect concept drift, i.e., a structural change in the acquired datastream, and react accordingly is a major achievement for intelligent sensing units. This ability allows the unit, for actively tuning the application, to maintain high performance, changing online the operational strategy, detecting and isolating possible occurring faults to name a few tasks. In the paper, we consider a just-in-time strategy for adaptation; the sensing unit reacts exactly when needed, i.e., when concept drift is detected. Change detection tests (CDTs), designed to inspect structural changes in industrial and environmental data, are coupled here with adaptive k-nearest neighbor and support vector machine classifiers, and suitably retrained when the change is detected. Computational complexity and memory requirements of the CDT and the classifier, due to precious limited resources in embedded sensing, are taken into account in the application design. We show that a hierarchical CDT coupled with an adaptive resource-aware classifier is a suitable tool for processing and classifying sequential streams of data. [ABSTRACT FROM AUTHOR]

In this paper, a novel neural-network-based iterative adaptive dynamic programming (ADP) algorithm is proposed. It aims at solving the optimal control problem of a class of nonlinear discrete-time systems with control constraints. By introducing a generalized nonquadratic functional, the iterative ADP algorithm through globalized dual heuristic programming technique is developed to design optimal controller with convergence analysis. Three neural networks are constructed as parametric structures to facilitate the implementation of the iterative algorithm. They are used for approximating at each iteration the cost function, the optimal control law, and the controlled nonlinear discrete-time system, respectively. A simulation example is also provided to verify the effectiveness of the control scheme in solving the constrained optimal control problem. [ABSTRACT FROM AUTHOR]

The cart-pole system is one of the most popular laboratory experiments for control education. Balance control of the system is usually utilised for control theory education. Inevitably, one has to set an initial pole-angle. But it is hard to accurately set the angle by hand. This paper first models the system. Then, a nonlinearity measure is introduced to analyse the control problems of the system. Afterwards, an energy-based controller is employed to automatically and accurately set the deviation for balance control, and a balance controller is subsequently utilised to stabilise the system. Physical experiments are carried out to verify the validity of this method for students. [ABSTRACT FROM AUTHOR]

Abstract: The biggest problem that networked control systems face is the random time-varying delay, which often causes system instability and even collapse. Aiming at this problem, a new modeling scheme for the networked control systems, motivated from a variable-period sampling approach, is presented in this paper. Here, the time delay to occur at current sampling step is taken as the sampling period between current sampling step and next sampling step. To predict online the time delay induced in the networked control systems, a BP feedforward neural network is adopted and the training algorithm of the BP neural network is given. To make the BP neural network adapt to the changing environment of the networked control systems and improve its prediction accuracy, the BP neural network is designed to further update according to its prediction error after each prediction. At each sampling step, good approximation to actual time delay becomes available and different sampling period is obtained. Control simulations using the variable sampling period and fixed sampling period are compared. Simulation results show that this new approach can alleviate the influence of time delay to the greatest extent and improve the performance of the networked control systems. [Copyright &y& Elsevier]

Abstract: This paper proposes an improved natural image segmentation approach that is more effective, more controllable and more stable under various backgrounds than the traditional mean shift segmentation. The proposed approach employs following four new aspects: the changeable color bandwidth, the direct density searching, the global optimization for mode merging, and the elimination of texture patches. In bandwidth selection, the optimal color bandwidth under Plug-in rule used by the traditional approach is not suitable for actual vision tasks, and a changeable color bandwidth makes it easy to control the segmentation result. The performance of the direct density searching is better than that of mean shift under the same spatial bandwidth. A global optimization criterion for mode merging stabilizes the segmentation result of different images. The elimination of texture patches mostly removes the small patches resulting from texture. In addition, after mode detection, an image is partitioned into some local patches, each of which corresponds to a local mode. These patches are got with color information, and they can be taken as the initial segmentation for further processing that is based on a global optimization criterion constructed by texture features. [Copyright &y& Elsevier]

The reaction of the iminobiphosphines RNPPh2–PPh2, where R = C6H4(p-CN), C6H4(m-CN), C6H4(o-C6H5), C6F5 or C6H4(o-CF3), with one molecular equivalent of M(cod)Cl2 (M = Pd or Pt) results in a rearrangement of the NPP unit to the more commonly encountered P–N–P unit, forming mono-chelating complexes of general formula M{RN(PPh2)2}Cl2. The related reaction of the same range of iminobiphosphines with Pt(cod)Cl2 (but not Pd(cod)Cl2) in 2:1 ratio affords complexes of general formula [Pt{RN(PPh2)2}2]2Cl. All 15 complexes are isolated in moderate to high yield and they have been fully characterised by spectroscopic methods. Six complexes, viz. [M{C6H4(p-CN)N(PPh2)2}Cl2], [M{C6H4(m-CN)N(PPh2)2}Cl2] and [M{C6H4(o-C6H5)N(PPh2)2}Cl2] (M = Pd and Pt), have been characterised in the solid state by single crystal X-ray diffraction analysis. [Copyright &y& Elsevier]

Abstract: Computed Torque Control (CTC) is an effective motion control strategy for robotic manipulator systems, which can ensure globally asymptotic stability. However, CTC scheme requires precise dynamical models of robotic manipulators. To handle this impossibility, in this paper, a new approach combing CTC and Fuzzy Control (FC) is developed for trajectory tracking problems of robotic manipulators with structured uncertainty and/or unstructured uncertainty. Fuzzy part with a set of tunable parameters is employed to approximate lumped uncertainty due to parameters variations, unmodeled dynamics and so on in robotic manipulators. Based on Lyapunov stability theorem, it is shown that the proposed controller can guarantee stability of closed-loop systems and satisfactory tracking performances. The proposed approach indicates that CTC method is also valid for controlling uncertain robotic manipulators as long as compensative controller is appropriately designed. Finally, computer simulation results on a two-link elbow planar robotic manipulator are presented to show tracking capability and effectiveness of the proposed scheme. [Copyright &y& Elsevier]

Abstract: This paper presents a new technique for depth and motion estimation from image sequence, aiming at the model-based pose estimation problem. The key of the proposed technique is a novel depth estimation approach. It can compute directly the depths of model points in consecutive camera coordinate systems according to geometric relationships between a camera and model point pairs instead of individual model point. Based on the proposed depth estimation method, two strategies are discussed to tackle three different cases of camera motion. Both strategies first compute depths, independent of motion parameters, from two images. The difference between them is two or three images are required to estimate efficiently the camera motion. If the camera only translates, two images are needed to compute directly the translation. The strategy requiring three images is mainly for the case that the camera translates with large rotation, which is difficult to be recovered accurately from two images. However, if the camera translates with small rotation, then the two strategies are applicable. The main contributions of this paper are the proposed point pairs based depth estimation method and three images based strategy to recover large rotational motion. The presented technique is simple and appealing. Extensive experiments are performed on synthetic data and real images to demonstrate its efficiency and robustness. [Copyright &y& Elsevier]

Abstract: An adaptive sliding mode fuzzy control approach is proposed for a two-dimensional overhead crane. System linearization transforms the two-dimensional system to two independent systems: X-direction transport system and Y-direction transport system. Both the two systems are with the same dynamic model and include two subsystems: positioning subsystem and anti-swing subsystem. Combining SMC’s robustness and FLC’s independence of system model, a sliding mode fuzzy control approach is proposed for both X-direction transport and Y-direction transport. According to the influences on system dynamic performance, both the slope of sliding mode surface and the coordination between the two subsystems are automatically tuned by real time fuzzy inference respectively. The effectiveness of the proposed control approach is demonstrated by experiments with a two-dimensional prototype overhead crane. [Copyright &y& Elsevier]