This paper presents a cooperative intelligent driving (CID) scheme to optimally control a vehicle’s speed in multi-lane traffic, smooth its flow, and facilitate others to improve their performance. Under the scheme, lane-wise traffic speeds along the road, in the form of a road-speed profile (RSP), are dynamically estimated using information from connected vehicles (CVs) that broadcast their states. The driving decision under the scheme is computed in a model predictive control (MPC) framework that optimizes the vehicle’s acceleration to equalize traffic speeds across the lanes in a cooperative approach besides attaining the objective of safe and smooth driving. The optimization problem in the scheme is solved using a real-time computation method. The scheme is assessed by implementing it on a small portion of vehicles in typical freeway traffic affected by lane blocks or merging flows using the AIMSUN traffic simulator. It is found that low penetration of CID can relieve bottlenecks, harmonize the flow over lanes, and significantly improve overall traffic performance.

To ensure powersupply–demand balance under the increasing penetration of wind power, the existing nonanticipative robust unit commitment models (NRUCs) co-optimize the commitment status and the dispatch policy of power sources. Exploiting the fact that the wind power uncertainty reduces over time, this article proposes an NRUC where determining the dispatch policy is delayed until the uncertainty decreases. The proposed NRUC features three decision-making problems sequentially solved under different degrees of uncertainty. The first decision-making problem is formulated as an intractable three-stage robust optimization problem. To solve this problem, a suboptimal approach is developed where a constraint is imposed on the dispatch policy so that the transmission capacity constraint is met regardless of the dispatch level. Results of simulations on a 24-bus and a 300-bus test system show that the proposed NRUC outperforms existing NRUCs regarding feasibility and optimality under currently severe but decreasing wind power uncertainty.

A novel cyber-physical multiagent framework is proposed to control traffic at an intersection. The vehicles, or physical agents, may pass the intersection smoothly utilizing the timings of traffic lights provided in advance. In the cyberspace, the durations of upcoming traffic lights are computed by a group of cyber agents using a stochastic gradient-based method known as broadcast control of multiagent systems. For this computation of the traffic light durations, a function that represents the cost for blocking a vehicle by the red light is included in the objective function, which is minimized by the cyber agents to collectively provide the least-restrictive right-of-way in a receding horizon control approach to all vehicles at the intersection. The proposed scheme is evaluated through microscopic traffic simulation at various penetration rates of the automated vehicles, and performances are compared with existing schemes.

This paper presents a novel adaptive traffic signal control scheme that addresses a mixed manual-automated traffic scenario in a typically isolated intersection. The traffic signals are optimized in a receding horizon control framework that aims at minimizing the total crossing time of all vehicles, considering their dynamical states. The control scheme ensures comfortable crossing of manually driven vehicles by retaining the basic features of the traditional signal management systems. The optimal signal changing times are broadcasted one cycle ahead, which enables the automated vehicles to tune their speed in order to cross the intersection with minimum stop-delay. More specifically, the framework optimizes the green time of each signal without considering the existing cycle-split concept explicitly. The proposed signal control scheme is evaluated in microscopic traffic simulation considering the different proportion of turning traffic at the intersection and various penetration rates of the automated vehicles. It is observed that the optimization process usually results in the shortest possible green period of each signal that can be realized without reducing the capacity of the intersection at any traffic volumes. Consequently, the resulting short signal cycle which is adaptive to the traffic around the intersection improves the average speed and reduces both the traffic density and the number of idling vehicles. As a consequence, the fuel consumption efficiency and the rate of CO2emission around the intersection are also reduced. These results are compared with both the traditional fixed time and the actuated signal control schemes. As the portion of the automated vehicles increases in the case of the proposed scheme, the overall traffic flow performance further improves.

This paper proposes a Gaussian process-based self-organizing incremental neural network (GPINN) to address the density estimation problem of online unsupervised learning. First, we adopt Gaussian process models with adaptive kernels that map the distribution of the neighbors of each node to its link relationship. Second, combining GPINN and kernel density estimation, we derive the bandwidth matrix updating rule for adapting to the generated network. We theoretically analyze the advantages of the proposed approach in determining threshold regions over using distance measures. The experimental results on both synthetic data sets and real-world data sets show that our method achieves remarkable improvement in density estimation accuracy for large noisy data.

In this paper, we present an overview of the applications of graph theory in power system modeling, dynamics, coherency, and control. First, we study synchronization of generator dynamics using both nonlinear and small-signal representations of classical structure-preserving models of power systems in light of their network structure and the weights associated with the nodes and edges of the network graph. We overview important necessary and sufficient conditions for both phase and frequency synchronization. We highlight the role of graph structure in coherency properties, and introduce the idea of generator and bus aggregation whereby dynamic equivalent models of large power grids can be developed while retaining the concept of a “bus” in the network graph of the equivalent model. We also discuss several new results on graph sparsification for designing distributed controllers for power flow oscillation damping.

This paper presents a coordination scheme of automated vehicles at an intersection without using any traffic lights. Using a two-way communication network, vehicles approaching the intersection from all sections are globally coordinated, by considering their states all together in a model predictive control framework, in order to achieve smooth traffic flows at the intersection. The optimal trajectories of the vehicles are computed based on avoidance of their cross-collision risks around the intersection under relevant constraints and preferences. The scheme efficiently utilizes the intersection area by preventing each pair of conflicting vehicles from approaching their cross-collision point at the same time, instead of reserving the whole intersection area for the conflicting vehicles one after another. The scheme also enables left- or right-turning movements of vehicles under constrained velocity without using any auxiliary lanes. The proposed vehicle-intersection coordination scheme is evaluated through numerical simulation in a typical test intersection consisting of both multilanes and single-lane approaches with turning movements of vehicles. Observations under different traffic flow conditions reveal that the proposed scheme significantly improves intersection performance compared with the traditional signalized intersection scheme.

Traffic management on road networks is an emerging research field in control engineering due to the strong demand to alleviate traffic congestion in urban areas. Interaction among vehicles frequently causes congestion as well as bottlenecks in road capacity. In dense traffic, waves of traffic density propagate backward as drivers try to keep safe distances through frequent acceleration and deceleration. This paper presents a vehicle driving system in a model predictive control framework that effectively improves traffic flow. The vehicle driving system regulates safe intervehicle distance under the bounded driving torque condition by predicting the preceding traffic. It also focuses on alleviating the effect of braking on the vehicles that follow, which helps jamming waves attenuate to in the traffic. The proposed vehicle driving system has been evaluated through numerical simulation in dense traffic.

An energy saving control of unstable periodic orbits embedded in a hybrid chaotic system is proposed. The conventional controlling chaos methods utilize small perturbations of states or parameters as control input, however, quick time responses cannot be expected since the corresponding basins of attractions for higher periodic solutions become tiny. While If one allows a large perturbation to improve the time response, rather the total controlling energy which is proposed to the distance between the target orbit and the current orbit may increases. In this paper, when we consider the chaotic hybrid system, we noticed that we could utilize the perturbation of the referenced value for controlling, i.e., only a threshold value (Poincaré mapping surface) is updated in control. No control input as a perturbation of the state or parameter value is applied to the system. In fact, the threshold value is used instantly when the feedback system determines the next updated threshold value. The variation of the threshold value can be obtained numerically by computing variational equations, and the control matrix is designed with the linear control theory. Since no affection to the state and parameters, it is emphasized that the total behavior of the controlled system is different from the conventional methods, especially it is unlike the impulsive control methods. We demonstrate this control method in a simple hybrid system and show that a large basin of attraction for the control is realized.

While a robot is moving, ego noise is generated due to the fans and motors of the robot. Furthermore, a robot is not only subject to the ego noise, but also to the ambient noise of the environment, both having different short-term signal characteristics. Because ego-motion noise generated by the motors is non-stationary, and the BackGround Noise (BGN) is stationary, one single noise estimation method is unable to track the changes in both noise spectra rapidly and accurately. Therefore, we propose to use the combination of two different noise estimation methods adequate for each one of co-existing noise types in a unified framework: 1) a stationary noise estimation method called Histogram-based Recursive Level Estimation (HRLE) and 2) a non-stationary noise estimation method called Template-based Estimation (TE). In this paper, we evaluate the performance of several single-channel based noise estimation techniques in terms of their prediction accuracy and quality of the speech signals enhanced by spectral subtraction methods. The experimental results show that our system, compared to the conventional single-stage noise estimation methods, achieves better performance in attaining signal quality and improving word correct rates.

Using pre-recorded templates to estimate and suppress the ego noise of a robot is advantageous because this method is able to cope with the non-stationarity of this particular type of noise. However, standard template-based estimation requires human intervention in the offline training sessions, storage of large amounts of data and does not adapt to the dynamical changes in the environmental conditions. In this paper we investigate the feasibility of an incremental template learning system to tackle these drawbacks. Incremental learning enables the system to acquire new templates on the fly and update the older ones appropriately. Whilst allowing the system to continually increase its knowledge and enhancing its estimation performance, this learning scheme also reduces the size of the database. We evaluate the performance of the proposed noise estimation method in terms of its estimation accuracy, quality of speech signals enhanced by spectral subtraction method, and size of database. The experimental results show that our system compared to conventional single-channel noise estimation methods achieves better performance in attaining signal quality and improving word correct rates.

A lot of oscillatory phenomena exist in the natural world. In recent years, many of them have been found to play a crucial role in living organisms such as the circadian rhythms, neural networks, to list a few. This fact has prompted enormous theoretical research works on modeling/analysis of oscillatory phenomena. Among them, large scale arrays consisting of simple subsystems have drawn an intensive attention due to academic interest and also the similarity to actual cell models. In our work, we concentrate on the linearly coupled networks that have interesting applications such as Josephson junction networks. In general, the nonlinearity of the dynamics is indispensable for the occurrence of such phenomena. In this paper, we formulate the nonlinear individual subsystems within the framework of piecewise affine (PWA) systems, for which several practical analysis tools have been proposed. In summary, the overall dynamics is given as linearly coupled (a large number of) PWA systems. In this paper, we derive a sufficient condition under which the dynamics is Y-oscillatory. The Y-oscillation, originally introduced by Yakubovich, is a general notion of oscillatory phenomena that covers both periodic and aperiodic orbits. However, it is known that the analysis of PWA systems become more difficult to analyze as the number of modes increases, similarly to other switching systems. The main result is achieved by proving the well-posedness and ultimate boundedness. An important feature of the result is that, under the assumption that every subsystem has a property in common, the criteria can be rewritten in terms of connection topology and its complexity is considerably reduced so that it is applicable to large scale networks. For illustrative purpose, we analyze Fitzhugh-Nagumo equation that is a model for neural oscillator with the excitation property in mathematical physiology.

Noise generated due to the motion of a robot is not desired, because it deteriorates the quality and intelligibility of the sounds recorded by robot-embedded microphones. It must be reduced or cancelled to achieve automatic speech recognition with a high performance. In this work, we divide ego-motion noise problem into three subdomains of arm, leg and head motion noise, depending on their complexity and intensity levels. We investigate methods that make use of single-channel and multi-channel processing in order to suppress ego noise separately. For this purpose, a framework consisting of a microphone-array-based geometric source separation, a consequent post filtering process and a parallel module for template subtraction is used. Furthermore, a control mechanism is proposed, which is based on signal-to-noise ratio and instantaneously detected motions, to switch to the most suitable method to deal with the current type of noise. We evaluate the proposed techniques on a humanoid robot using automatic speech recognition (ASR). The preliminary results of isolated word recognition show the effectiveness of our methods by increasing the word correct rates up to 50% compared to the single channel recognition in arm and leg motion noises and up to 25% in very strong head motion noises.

This paper presents a system that gives a mobile robot the ability to recognize target speaker's speech, even if the robot performs an action and there are multiple speakers talking in the room. Associated problems to this system are twofold: (1) While the robot is moving, the joints inevitably generate ego-motion noise due to its motors. (2) Recognizing target speech against other interfering speech signals is a difficult task. Since typical solutions to (1) and (2), motor noise suppression and sound source separation, both introduce distortion to the processed signals, the performance of automatic speech recognition (ASR) deteriorates. Instead of removing the ego-motion noise with conventional noise suppression methods, in this work, we investigate methods to eliminate the unreliable parts of the audio features that are contaminated by the ego-motion noise. For this purpose, we model masks that filter unreliable speech features based on the ratio of speech and motor noise energies. We analyze the performance of the proposed technique under various test conditions by comparing it to the performance of existing Missing Feature Theory-based ASR implementations. Finally, we propose an integration framework for two different masks that are designed to eliminate ego noise and to filter the leakage energy of interfering sound sources. We demonstrate that the proposed methods achieve a high ASR accuracy.

While a robot is moving, the joints inevitably generate noise due to its motors, i.e. ego-motion noise. This problem is very crucial, especially in humanoid robots, because it tends to have a lot of joints and the motors are located closer to the microphones than the sound sources. In this work, we investigate methods for the prediction and suppression of the ego-motion noise. In the first part, we analyze the performance of different noise subtraction strategies, assuming that the noise prediction problem has been solved. In the second part, we present some results for a noise prediction scheme based on the current robot joint status. Performance is evaluated for a number of criteria, including Automatic Speech Recognition (ASR). We demonstrate that our method improves recognition performance during ego-motion considerably.

Audio-visual speech recognition (AVSR) is a promising approach to improve noise robustness of speech recognition in the real world. A phoneme and a viseme are used as an auditory and visual unit for AVSR, respectively. However, in the real world, they are often misclassified due to additional input noises. To solve this problem, we propose two approaches. One is audio-visual integration based on missing feature theory to cope with missing or unreliable audio and visual features for recognition. The other is a biologically-inspired approach, that is, phoneme and viseme grouping based on coarse-to-fine recognition. Preliminary experiments show that audio-visual speech recognition based on these approaches improves the noise robustness of AVSR drastically.

In this paper, a new adaptive robust control scheme for manipulators is proposed that overcomes the drawbacks of conventional adaptive robust control methods. The proposed controller has a simple structure by exploiting the special structure of the manipulator dynamics, and achieves the specified tracking precision without any a priori information on uncertainty. Furthermore, the feedback gain of the proposed method is almost necessary and minimum for the specified precision. To verify the advantages of the method, experimental results are shown for the trajectory control of a 2-DOF direct-drive arm.>

Deals with robust control of robot manipulators in the case where joint torque sensors are available. First, the authors derive a dynamic equation of the manipulator with joint torque sensors that explicitly expresses the total nonlinear multivariable structure. This dynamic equation makes it possible to construct the control system of the manipulator with joint torque sensors using the same method as in the conventional case without the sensors. Second, based on this dynamic equation, they propose a robust trajectory control scheme which achieves the specified tracking accuracy in the presence of the modeling errors including the modeling errors of actuator systems. In the proposed method, the joint torque sensor information is fully exploited to compensate the uncertainty of link and load parameters. Furthermore, an illustrative simulation result is given to show the effectiveness of the proposed control method.>