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Abstract: Abstract The development of intelligent connected technology has brought opportunities and challenges to the design of energy management strategies for hybrid electric vehicles. First, to achieve car-following in a connected environment while reducing vehicle fuel consumption, a power split hybrid electric vehicle was used as the research object, and a mathematical model including engine, motor, generator, battery and vehicle longitudinal dynamics is established. Second, with the goal of vehicle energy saving, a layered optimization framework for hybrid electric vehicles in a networked environment is proposed. The speed planning problem is established in the upper-level controller, and the optimized speed of the vehicle is obtained and input to the lower-level controller. Furthermore, after the lower-level controller reaches the optimized speed, it distributes the torque among the energy sources of the hybrid electric vehicle based on the equivalent consumption minimum strategy. The simulation results show that the proposed layered control framework can achieve good car-following performance and obtain good fuel economy. PubDate: 2022-04-21
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Abstract: Abstract Advanced fuel economy strategies are expected to reduce the fuel consumption of vehicles. An internal combustion engine (ICE) driving vehicle equipped with free-wheeling turns off the fuel injection and decouples the engine from the drivetrain when the driving force is not required. This paper proposes a method to reduce the fuel consumption of a vehicle equipped with free-wheeling. First, an optimization problem is formulated to minimize the fuel consumption of a vehicle with free-wheeling when the traveling distance, the initial and final speed are specified and the vehicle needs to glide before arriving at the end point for fuel economy. The speed profile of the vehicle, engine operating point, and engine on/off timing are obtained as the results of the optimization. The analytical and numerical analyses results demonstrate the effectiveness and the fuel-saving mechanism of the obtained speed profile. The main finding of the analyses is that rather than starting a gliding stage immediately after an acceleration or a constant speed stage, adding a pre-acceleration stage before the gliding stage is more fuel-economic under some conditions independent of the complexity of the vehicle model. The obtained speed profile including a pre-acceleration stage is applied to a driving scenario including traffic congestions. The results demonstrate the effectiveness of the pre-acceleration stage in reducing fuel consumption for a vehicle equipped with free-wheeling. PubDate: 2022-04-14
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Abstract: Abstract In this paper, we propose a benchmark problem for the challengers aiming to energy efficiency control of hybrid electric vehicles (HEVs) on a road with slope. Moreover, it is assumed that the targeted HEVs are in the connected environment with the obtainment of real-time information of vehicle-to-everything (V2X), including geographic information, vehicle-to-infrastructure (V2I) information and vehicle-to-vehicle (V2V) information. The provided simulator consists of an industrial-level HEV model and a traffic scenario database obtained through a commercial traffic simulator, where the running route is generated based on real-world data with slope and intersection position. The benchmark problem to be solved is the HEVs powertrain control using traffic information to fulfill fuel economy improvement while satisfying the constraints of driving safety and travel time. To show the HEV powertrain characteristics, a case study is given with the speed planning and energy management strategy. PubDate: 2022-03-28
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Abstract: Abstract In this paper, we review some existing control methodologies for complex systems with particular emphasis on those that are near critical. Due to the shortage of the classical control theory in handling complex systems, the reviewed control methods are mainly associated with machine learning techniques, game-theoretical approaches, and sparse control strategies. Additionally, several interesting and promising directions for future research are also proposed. PubDate: 2022-03-10
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Abstract: Abstract This paper reviews recent developments in learning-based adaptive optimal output regulation that aims to solve the problem of adaptive and optimal asymptotic tracking with disturbance rejection. The proposed framework aims to bring together two separate topics—output regulation and adaptive dynamic programming—that have been under extensive investigation due to their broad applications in modern control engineering. Under this framework, one can solve optimal output regulation problems of linear, partially linear, nonlinear, and multi-agent systems in a data-driven manner. We will also review some practical applications based on this framework, such as semi-autonomous vehicles, connected and autonomous vehicles, and nonlinear oscillators. PubDate: 2022-02-14 DOI: 10.1007/s11768-022-00081-3
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Abstract: Abstract Considering that the inevitable disturbances and coupled constraints pose an ongoing challenge to distributed control algorithms, this paper proposes a distributed robust model predictive control (MPC) algorithm for a multi-agent system with additive external disturbances and obstacle and collision avoidance constraints. In particular, all the agents are allowed to solve optimization problems simultaneously at each time step to obtain their control inputs, and the obstacle and collision avoidance are accomplished in the context of full-dimensional controlled objects and obstacles. To achieve the collision avoidance between agents in the distributed framework, an assumed state trajectory is introduced for each agent which is transmitted to its neighbors to construct the polyhedral over-approximations of it. Then the polyhedral over-approximations of the agent and the obstacles are used to smoothly reformulate the original nonconvex obstacle and collision avoidance constraints. And a compatibility constraint is designed to restrict the deviation between the predicted and assumed trajectories. Moreover, recursive feasibility of each local MPC optimization problem with all these constraints derived and input-to-state stability of the closed-loop system can be ensured through a sufficient condition on controller parameters. Finally, simulations with four agents and two obstacles demonstrate the efficiency of the proposed algorithm. PubDate: 2022-02-09 DOI: 10.1007/s11768-022-00079-x
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Abstract: Abstract In this paper, the leader–follower consensus of feedforward nonlinear multi-agent systems is achieved by designing the distributed output feedback controllers with a time-varying gain. The agents dynamics are assumed to be in upper triangular structure and satisfy Lipschitz conditions with an unknown constant multiplied by a time-varying function. A time-varying gain, which increases monotonously and tends to infinity, is proposed to construct a compensator for each follower agent. Based on a directed communication topology, the distributed output feedback controller with a time-varying gain is designed for each follower agent by only using the output information of the follower and its neighbors. It is proved by the Lyapunov theorem that the leader–follower consensus of the multi-agent system is achieved by the proposed consensus protocol. The effectiveness of the proposed time-varying gain method is demonstrated by a circuit system. PubDate: 2022-02-07 DOI: 10.1007/s11768-022-00083-1
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Abstract: Abstract Fault diagnosis is essential for the normal and safe operation of dynamic systems. To improve the spatial resolution among multiple channels and the discriminability among categories of the original data collected from actual operating equipments and to further achieve high diagnostic accuracy, this paper proposes a method for fault diagnosis by cascaded space projection (CSP) and a convolutional neural network (CNN) model. First, one of every kind of sample is selected from the original data to calculate the PCA transformation matrices. Second, the original data are expanded to 10 dimensions by the W2C projection matrix provided by Google-image searching, which is the main part of CSP. Third, the ten-dimensional matrix is multiplied by the PCA transformation matrix, which corresponds to its fault type, to make the data more representative by reducing unnecessary dimensions. Finally, the processed data are converted into images to input into a CNN, the backbone structure for fault diagnosis. To verify the effectiveness and reliability of the proposed method, the Case Western Reserve University (CWRU) and Xi’an Jiaotong University (XJTU-SY) rolling bearing datasets are used to perform experiments. Comparison with other methods is carried out to show the superiority of the proposed method. The experimental results demonstrate that the method proposed in this paper can effectively achieve 100% accuracy. PubDate: 2022-02-04 DOI: 10.1007/s11768-022-00084-0
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Abstract: Abstract This paper is concerned with the problem of safety stabilization for switched systems where the solvability of the problem under study for individual subsystems is not assumed. A new state-dependent switching strategy with guaranteed dwell-time for switched systems is constructed, and a sufficient condition for absence of Zeno behavior is derived. Also, a novel switched control design method is proposed to simultaneously guarantee the safety of the switched closed-loop system and stabilize the system based on the union of a common barrier function and a single Lyapunov function, which effectively handles the conflict between safety and stability objectives. Finally, two examples are presented to demonstrate the effectiveness of the proposed design approach. PubDate: 2022-02-04 DOI: 10.1007/s11768-022-00080-4
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Abstract: Abstract In this paper, we propose a model predictive control (MPC) strategy for accelerated offset-free tracking piece-wise constant reference signals of nonlinear systems subject to state and control constraints. Some special contractive constraints on tracking errors and terminal constraints are embedded into the tracking nonlinear MPC formulation. Then, recursive feasibility and closed-loop convergence of the tracking MPC are guaranteed in the presence of piece-wise references and constraints by deriving some sufficient conditions. Moreover, the local optimality of the tracking MPC is achieved for unreachable output reference signals. By comparing to traditional tracking MPC, the simulation experiment of a thermal system is used to demonstrate the acceleration ability and the effectiveness of the tracking MPC scheme proposed here. PubDate: 2022-01-26 DOI: 10.1007/s11768-022-00078-y
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Abstract: Abstract With the development of wireless communication technology, cyber physical systems are applied in various fields such as industrial production and infrastructure, where lots of information exchange brings cyber security threats to the systems. From the perspective of system identification with binary-valued observations, we study the optimal attack problem when the system is subject to both denial of service attacks and data tampering attacks. The packet loss rate and the data tampering rate caused by the attack is given, and the estimation error is derived. Then the optimal attack strategy to maximize the identification error with the least energy is described as a min–max optimization problem with constraints. The explicit expression of the optimal attack strategy is obtained. Simulation examples are presented to verify the effectiveness of the main conclusions. PubDate: 2022-01-26 DOI: 10.1007/s11768-021-00075-7
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Abstract: Abstract In this paper, we investigate the defense problem against the joint attacks of denial-of-service attacks and data tampering attacks in the framework of system identification with binary-valued observations. By estimating the key parameters of the joint attack and compensating them in the identification algorithm, a compensation-oriented defense scheme is proposed. Then the identification algorithm of system parameter is designed and is further proved to be consistent. The asymptotic normality of the algorithm is obtained, and on this basis, we propose the optimal defense scheme. Furthermore, the implementation of the optimal defense scheme is discussed. Finally, a simulation example is presented to verify the effectiveness of the main results. PubDate: 2022-01-24 DOI: 10.1007/s11768-021-00074-8
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Abstract: Abstract Time-delayed state feedback is an easy realizable control method that generates control force by differencing the current and the delayed versions of the system states. In this paper, a new form of the time-delayed state feedback structure is introduced. Based on the proposed time-delayed state feedback method, a new robust tracking system is designed. This tracking system improves the conventional state feedback with integral action disturbance rejection characteristics in the presence of the disturbance signals imposed on the system dynamics or on the sensors that measure the system states. Also, the proposed tracking system tracks the ramp-shaped reference input signal, which is not achievable through conventional state feedback. Moreover, since the proposed method adds delays to the closed-loop system dynamics, the ordinary differential equation of the system changes to a delay differential equation with an infinite number of characteristic roots. Thus, conventional pole placement techniques cannot be used to design the time-delayed state feedback controller parameters. In this paper, the simulated annealing algorithm is used to determine the proposed control system parameters and move the unstable roots of the delay differential equation to the left half-plane. Finally, the efficiency of the proposed reference input tracker is demonstrated by presenting two numerical examples. PubDate: 2022-01-20 DOI: 10.1007/s11768-021-00073-9
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Abstract: Abstract In this paper, the adaptive robust simultaneous stabilization problem of uncertain multiple n-degree-of-freedom (n-DOF) robot systems is studied using the Hamiltonian function method, and the corresponding adaptive \(L_2\) controller is designed. First, we investigate the adaptive simultaneous stabilization problem of uncertain multiple n-DOF robot systems without external disturbance. Namely, the single uncertain n-DOF robot system is transformed into an equivalent Hamiltonian form using the unified partial derivative operator (UP-DO) and potential energy shaping method, and then a high dimensional Hamiltonian system for multiple uncertain robot systems is obtained by applying augmented dimension technology, and a single output feedback controller is designed to ensure the simultaneous stabilization for the higher dimensional Hamiltonian system. On this basis, we further study the adaptive robust simultaneous stabilization control problem for the uncertain multiple n-DOF robot systems with external disturbances, and design an adaptive robust simultaneous stabilization controller. Finally, the simulation results show that the adaptive robust simultaneous stabilization controller designed in this paper is very effective in stabilizing multi-robot systems at the same time. PubDate: 2022-01-18 DOI: 10.1007/s11768-021-00076-6
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Abstract: Abstract In this paper, we present an output regulation method for unknown cyber-physical systems (CPSs) under time-delay attacks in both the sensor-to-controller (S-C) channel and the controller-to-actuator (C-A) channel. The proposed approach is designed using control inputs and tracking errors which are accessible data. Reinforcement learning is leveraged to update the control gains in real time using policy or value iterations. A thorough stability analysis is conducted and it is found that the proposed controller can sustain the convergence and asymptotic stability even when two channels are attacked. Finally, comparison results with a simulated CPS verify the effectiveness of the proposed output regulation method. PubDate: 2022-01-17 DOI: 10.1007/s11768-021-00072-w
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Abstract: Abstract Given a collection of parameterized multi-robot controllers associated with individual behaviors designed for particular tasks, this paper considers the problem of how to sequence and instantiate the behaviors for the purpose of completing a more complex, overarching mission. In addition, uncertainties about the environment or even the mission specifications may require the robots to learn, in a cooperative manner, how best to sequence the behaviors. In this paper, we approach this problem by using reinforcement learning to approximate the solution to the computationally intractable sequencing problem, combined with an online gradient descent approach to selecting the individual behavior parameters, while the transitions among behaviors are triggered automatically when the behaviors have reached a desired performance level relative to a task performance cost. To illustrate the effectiveness of the proposed method, it is implemented on a team of differential-drive robots for solving two different missions, namely, convoy protection and object manipulation. PubDate: 2021-12-07 DOI: 10.1007/s11768-021-00069-5
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Abstract: Abstract High-dimensional data encountered in genomic and proteomic studies are often limited by the sample size but has a higher number of predictor variables. Therefore selecting the most relevant variables that are correlated with the outcome variable is a crucial step. This paper describes an approach for selecting a set of optimal variables to achieve a classification model with high predictive accuracy. The work described using a biological classifier published elsewhere but it can be generalized for any application. PubDate: 2021-11-25 DOI: 10.1007/s11768-021-00071-x
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