Search

Purpose: Novel aircraft propulsion configurations require a greater integration of the propulsive system with the airframe. As a consequence of the closer integration of the propulsive system, higher levels of flow distortion at the fan face are expected. This distortion will propagate through the fan and penalize the system performance. This will also modify the exhaust design requirements. This paper aims to propose a methodology for the aerodynamic optimization of the exhaust for novel embedded propulsive systems. To model the distortion transfer, a low order throughflow fan model is included. Design/methodology/approach: As the case study a 2D axisymmetric aft-mounted annular boundary layer ingestion (BLI) propulsor is used. An automated computational fluid dynamics approach is applied with a parametric definition of the design space. A throughflow body force model for the fan is implemented and validated for 2D axisymmetric and 3D flows. A multi-objective optimization based on evolutionary algorithms is used for the exhaust design. Findings: By the application of the optimization methodology, a maximum benefit of approximately 0.32% of the total aircraft required thrust was observed by the application of compact exhaust designs. Furthermore, for the embedded system, it is observed that the design of the compact exhaust and the nacelle afterbody have a considerable impact on the aerodynamic performance. Originality/value: This paper presents a novel approach for the exhaust design of embedded propulsive systems in novel aircraft configurations. To the best of the authors' knowledge, this is the first detailed optimization of the exhaust system on an annular aft-mounted BLI propulsor. [ABSTRACT FROM AUTHOR]

Cyclic motions are fundamental patterns in robotic applications, including industrial manipulation and robot locomotion. This article proposes an approach for the online modulation of cyclic motions in robotic applications. For this purpose, we present an integrated programmable central pattern generator (CPG) for the online generation of the reference joint trajectory of a robotic system out of a library of desired periodic motions. The reference trajectory is then followed by the lower level controller of the robot. The proposed CPG generates a smooth reference joint trajectory convergence to the desired one while preserving the position and velocity joint limits of the robot. The proposed CPG consists of one novel bounded output programmable oscillator. We design the programmable oscillator for encoding the desired multidimensional periodic trajectory as a stable limit cycle. We also use the state transformation method to ensure that the oscillator’s output and its first time-derivative preserve the joint position and velocity limits of the robot. With the help of Lyapunov-based arguments, we prove that the proposed CPG provides the global stability and convergence of the desired trajectory. The effectiveness of the proposed CPG for trajectory generation is shown in a passive rehabilitation scenario on the Kuka iiwa robot arm, and also in walking simulation on a seven-link bipedal robot. [ABSTRACT FROM AUTHOR]

With the fast development of the economy, marine activities are increasing. Due to the advantages of offshore cranes, they are widely used in marine production as effective transportation tools. As a matter of fact, the offshore crane works on the ship known as a typical noninnertial system, which is affected by the ship movement. To tackle this problem, we propose a time optimal trajectory planning method for the considered offshore crane. Specifically, to tackle the couplings between state variables, we show that the offshore crane system is differentially flat with the payload coordinates as the flat outputs. Based on this fact, the planning problems for the jib motion and the rope length are further converted into the planning problems for flat outputs. Then, in order to ensure the trackability of the planned trajectory and improve the safety, we consider a series of physical constraints including the jib luffing motion velocity and acceleration constraints, the rope length varying velocity and acceleration constraints, and the payload swing angle and angular velocity constraints, and then a time optimization problem is further formulated. By utilizing a bisection-based method, the optimal payload transportation time is obtained as well as the corresponding time optimal trajectories. As far as we know, it is the first time optimal trajectory planning method designed for offshore cranes to achieve the fast and accurate payload transportation as well as the payload swing elimination in earth-fixed frame. At last, the effectiveness of the proposed method is verified by simulation tests. [ABSTRACT FROM AUTHOR]

This article addresses the problem of learning the optimal control policy for a nonlinear stochastic dynamical. This problem is subject to the “curse of dimensionality” associated with the dynamic programming method. This article proposes a novel decoupled data-based control (D2C) algorithm that addresses this problem using a decoupled, “open-loop–closed-loop,” approach. First, an open-loop deterministic trajectory optimization problem is solved using a black-box simulation model of the dynamical system. Then, closed-loop control is developed around this open-loop trajectory by linearization of the dynamics about this nominal trajectory. By virtue of linearization, a linear quadratic regulator based algorithm can be used for this closed-loop control. We show that the performance of D2C algorithm is approximately optimal. Moreover, simulation performance suggests a significant reduction in training time compared to other state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Over the last decade, the multi-operator differential evolution (MODE) has become one of the most popular research areas in the differential evolution (DE) family. Among these MODEs, an improved multi-operator differential evolution (IMODE) has achieved success as the winner in CEC2020. Despite its good performance, the random information sharing strategy may not efficiently handle the balance between exploration and exploitation for the complex numerical optimization. To address this limitation, we propose a two-phase migration strategy (TMS) to improve the performance of IMODE, called IMODE-TMS. In the first phase, the top-ranked individuals in each sub-population are retained for exploitation, and all the bottom-ranked individuals are assigned to three sub-populations to maintain diversity. Furthermore, the second phase plays a crucial role in eliminating stagnation. When a sub-population is identified as stagnant by the stagnation indicator, the optimal individual will migrate to that sub-population following the uni-directional ring structure. This process is mainly used to increase the off-trap capability on some problems with complex fitness landscapes. IMODE-TMS is tested on the CEC2020, CEC2021 and CEC2022 benchmark functions, and seven well-known complex global trajectory optimization problems (GTOP). The experimental results show that IMODE-TMS significantly outperforms not only IMODE but also other state-of-the-art comparison algorithms. [ABSTRACT FROM AUTHOR]

The objective of this paper is to present a convex–concave optimization approach for solving the problem of a multistage launch vehicle ascent trajectory. The proposed method combines convex–concave decomposition and successive linearization techniques to generate a new sequence of convex subproblems to replace the original non-convex problem. Bernstein approximation is used to transform the chance constraints into convex ones. A h p -adaptive pseudospectral scheme is employed to discretize the optimal control problem into a nonlinear programming problem with less computation cost. The performance of the proposed strategy is compared against other typical techniques in a selection of test case scenarios. Numerical results demonstrate the viability of the method and show pros and cons of the proposed technique. • A noise-perturbed multi-stage launch vehicle trajectory optimization model is proposed. • The convex–concave decomposition method is applied to the multistage launch vehicle ascent problem. • The convex–concave decomposition and successive linearization methods allow for a reduction of the computational cost. • An extension to handle chance constraints based on Bernstein approximation approach is proposed. [ABSTRACT FROM AUTHOR]

This paper presents an arbitrary high-order structure-preserving numerical method for the simulation and optimization of the articulated robot dynamics. The proposed numerical scheme is derived by using spectral variational integrators (SVI) to discretize the Lagrangian of multibody robot systems which are calculated by an efficient parallel Lie group formulation. The linear stability conditions of SVI for the simulation of the robot dynamics are given and proofed. The resulting algorithms are examined in a simplified industrial Selective Compliance Assembly Robot Arm (SCARA) and a wheel-leg hybrid mechanism. They exhibit salient advantages over conventional numerical methods. The algorithms converge exponentially, preserve the symplectic form and momentum automatically and exhibit excellent energy stability, which yield more qualitative simulation and optimization results for articulated multibody robot systems. • Efficient construction of structure-preserving numerical algorithm for robots. • Derivation of linear stability conditions of spectral variational integrators. • Dynamics simulation and trajectory optimization of articulated robots via SVI. [ABSTRACT FROM AUTHOR]

We consider the framework of transfer-entropy-regularized Markov decision process (TERMDP) in which the weighted sum of the classical state-dependent cost and the transfer entropy from the state random process to the control input process is minimized. Although TERMDPs are generally formulated as nonconvex optimization problems, an analytical necessary optimality condition can be expressed as a finite set of nonlinear equations, based on which an iterative forward–backward computational procedure similar to the Arimoto–Blahut algorithm is developed. It is shown that every limit point of the sequence generated by the proposed algorithm is a stationary point of the TERMDP. Applications of TERMDPs are discussed in the context of networked control systems theory and nonequilibrium thermodynamics. The proposed algorithm is applied to an information-constrained maze navigation problem, whereby we study how the price of information qualitatively alters the optimal decision polices. [ABSTRACT FROM AUTHOR]

This article focuses on the design, test, and validation of a deep neural network (DNN)-based control scheme capable of predicting optimal motion commands for autonomous ground vehicles (AGVs) during the parking maneuver process. The proposed design utilizes a multilayer structure. In the first layer, a desensitized trajectory optimization method is iteratively performed to establish a set of time-optimal parking trajectories with the consideration of noise-perturbed initial configurations. Subsequently, by using the preplanned optimal parking trajectory data set, several DNNs are trained in order to learn the functional relationship between the system state-control actions in the second layer. To obtain further improvements regarding the DNN performances, a simple yet effective data aggregation approach is designed and applied. These trained DNNs are then utilized as the motion controllers to generate feedback actions in real time. Numerical results were executed to demonstrate the effectiveness and the real-time applicability of using the proposed control scheme to plan and steer the AGV parking maneuver. Experimental results were also provided to justify the algorithm performance in real-world implementations. [ABSTRACT FROM AUTHOR]

Many safety-critical systems, such as autonomous vehicles and service robots, must achieve high-level task specifications with performance guarantees. Much recent progress toward this goal has been made through an automatic controller synthesis from temporal logic specifications. Existing approaches, however, have been limited to relatively short and simple specifications. Furthermore, existing methods either consider some prior discretization of the state space, deal only with a convex fragment of temporal logic, or are not provably complete. We propose a scalable, provably complete algorithm that synthesizes continuous trajectories to satisfy nonconvex temporal logic over reals (RTL) specifications. We separate discrete task planning and continuous motion planning on-the-fly and harness highly efficient Boolean satisfiability and linear programming solvers to find dynamically feasible trajectories that satisfy nonconvex RTL specifications for high-dimensional systems. The proposed design algorithms are proven sound and complete, and simulation results demonstrate our approach's scalability. [ABSTRACT FROM AUTHOR]

Zero residual vibration (RV) in the position control of a flexible system is difficult to achieve because this system has low stiffness and underactuated feature. In this paper, we present a position control strategy to achieve zero RV in the position control of two degrees-of-freedom (DOF) flexible systems. First, a general dynamic model for two-DOF flexible systems is established. Next, we transform the position control with zero RV to motion planning and tracking control. Based on the position control objective with zero RV, three constraints for the desired system trajectory are given. To make the desired system trajectory satisfy these three constraints, we propose a motion trajectory optimization method. Specifically, we plan a forward system trajectory and a reverse system trajectory based on bidirectional trajectory planning method. Then, the rewinding strategy and the trajectory optimization based on genetic algorithm are used to connect these two system trajectories and to obtain a complete desired system trajectory. Finally, a sliding mode tracking controller is designed to make the system track this desired system trajectory. In this way, the system can reach its target-rest-position with zero RV. The effectiveness and superiority of the proposed control strategy are demonstrated in simulations. [ABSTRACT FROM AUTHOR]

This paper is concerned with a trajectory optimization problem for multiple unmanned aerial vehicles (multi-UAV) systems, where the optimization model is constructed based on quadrotor UAV dynamics. The problem is decomposed into a two-layer structure consists of a master problem and n subproblems, with n the amount of UAVs in the system. We propose a model-based distributed algorithm to minimize the energy consumption of the multi-UAV system, in which each UAV obtains its own trajectory by solving the corresponding subproblem, and the UAVs coordinate their trajectories according to master problem. It is shown that the proposed algorithm achieves exact convergence over directed networks, and an upper bound on the residual of cost function is provided. A numerical simulation is presented to demonstrate the validity of our algorithm. [ABSTRACT FROM AUTHOR]

A new approach for trajectory optimization of musculoskeletal dynamic models is introduced. The model combines rigid-body and muscle dynamics described with a Hill-type model driven by neural control inputs. The objective is to find input and state trajectories that are optimal with respect to a minimum-effort objective and meet constraints associated with musculoskeletal models. The measure of effort is given by the integral of pairwise average forces of the agonist-antagonist muscles. The concepts of flat parameterization of nonlinear systems and sum-of-squares optimization are combined to yield a method that eliminates the numerous set of dynamic constraints present in collocation methods. With terminal equilibrium, optimization reduces to a feasible linear program, and a recursive feasibility proof is given for more general polynomial optimization cases. The methods of the article can be used as a basis for fast, and efficient solvers for hierarchical, and receding-horizon control schemes. Two simulation examples are included to illustrate the proposed methods. [ABSTRACT FROM AUTHOR]

In this paper, a convex optimization algorithm is proposed to solve the online trajectory optimization problem of boost back of vertical take-off/vertical landing reusable launch vehicles. To achieve high-precision landing of launch vehicles, trajectory optimization of the boost-back flight phase considering the accuracy of entry is carried out, especially in emergencies. The trajectory optimization problem is formulated as an optimal control problem with minimum fuel consumption, and then it is transformed into a series of convex optimization subproblems, which can be solved by primal-dual interior-point method accurately and rapidly. During the transformation, flip-Radau pseudospectral discretization method, lossless convexification and successive convexification technology are applied. To drive the vehicle to predetermined entry points at the expected velocity, terminal constraints are expressed as orbital constraints of the endpoint in the boost-back flight phase. Considering the influence of Earth's rotation, the right ascension of the ascending node of the target orbit is updated according to the time and true anomaly at the end of the boost-back flight phase. Furthermore, the homotopy method is applied to the situation where there is no good initial guess when emergency happens. The algorithm presented in this paper performs well upon the simulation experiments of mission change and thrust decline. With good accuracy, high computational efficiency, and excellent robustness, the convex approach proposed has a great potential for onboard application in reusable launch vehicles and other space vehicles. [ABSTRACT FROM AUTHOR]

This article presents a computational framework to efficiently generate feasible and safe trajectories for multiple autonomous vehicle operations. We formulate the optimal motion planning problem as a continuous-time optimal control problem, and approximate its solutions in a discretized setting using Bernstein polynomials. The latter possess convenient properties that allow to efficiently compute and enforce constraints along the vehicles’ trajectories, such as maximum speed and angular rates, minimum distance between trajectories and between the vehicles and known obstacles, etc. Thus, the proposed method is particularly suitable for generating trajectories in real-time for safe operations in complex environments and multiple vehicle missions. We show, using a rigorous mathematical framework, that the solution to the discretized optimal motion planning problem converges to that of the continuous-time one. The advantages of the proposed method are investigated through numerical examples. [ABSTRACT FROM AUTHOR]

Sensor–cloud system, as one of the vital components of the Internet of Everything, has increasingly gained popularity since it bridges the physical and cyber worlds. In the smart city, sensing devices are deployed to monitor the environment and the data aggregate at the roadside Aggregation Devices (ADs). Then, Mobile Vehicles (MVs) submit the data to the data center in an opportunistic routing way when passing ADs. We propose a Content based Intelligent Trust Evaluation (CITE) scheme for trustworthy data collection over large-scale sensor-cloud systems. In CITE scheme, unmanned Mobile Electric Vehicles (MEVs) are employed to visit some sensing devices and collect their data along the designed routing path. Then, a delicate comparison algorithm is proposed to determine the credibility of devices' data operations, and trust evaluation is based on it. Similarly, the trust of MVs is obtained by comparing the data reported from ADs with the data submitted to the data center. Moreover, to reduce the cost of MEVs, we also design a path planning algorithm. The simulation results show that the CITE scheme can identify reliable sensing devices and MVs with a rate of 87.3% and 100% and increase the collection rate by 25.2% on average compared with the previous strategies. [ABSTRACT FROM AUTHOR]

This brief presents an integrated trajectory planning and attitude control framework for six-degree-of-freedom (6-DOF) hypersonic vehicle (HV) reentry flight. The proposed framework utilizes a bilevel structure incorporating desensitized trajectory optimization and deep neural network (DNN)-based control. In the upper level, a trajectory data set containing optimal system control and state trajectories is generated, while in the lower level control system, DNNs are constructed and trained using the pregenerated trajectory ensemble in order to represent the functional relationship between the optimized system states and controls. These well-trained networks are then used to produce optimal feedback actions online. A detailed simulation analysis was performed to validate the real-time applicability and the optimality of the designed bilevel framework. Moreover, a comparative analysis was also carried out between the proposed DNN-driven controller and other optimization-based techniques existing in related works. Our results verify the reliability of using the proposed bilevel design for the control of HV reentry flight in real time. [ABSTRACT FROM AUTHOR]

• Distributed heterogeneous multi-agent optimization is investigated. • The nonconvex velocity constraints are considered in this paper. • An improved distributed algorithm is introduced. • A time varying transformation is employed. • The convergence analysis of the algorithm is completed with the aid of some new Lyapunov function. This paper briefly deals with the distributed optimization problem over heterogeneous multi-agent networks with nonconvex velocity constraints. Based on the constraint operator, a distributed optimization algorithm is proposed for each agent, where the gradient gains are nonuniform and only local neighbors' information is used by each agent. Then, a time-varying coordination transformation is introduced to change the closed-loop system into a new system, which is composed of linear part and nonlinear part. It is proved that the system matrices of linear part are time-varying stochastic matrices and the system states are bounded. Furthermore, under some mild assumptions, it is shown that all agents' position states could converge to the optimal solution of the team objective function, while the velocities of all agents are constrained to the corresponding nonconvex sets. Finally, the feasibility of the proposed algorithm is verified by simulations. [ABSTRACT FROM AUTHOR]

This paper proposes a novel robust real-time trajectory optimization method to obtain the analytic solutions for hypersonic boost-glide vehicle (HBGV), and the corresponding robust adaptive controller is designed to lead the tracking error to a small neighborhood near zero in finite time. The cost functions reflect the constraints and the direction of trajectory optimization about several essential traits, such as dynamic pressure, load factor, heat flux and thrust. Through the conditions on continuity and constraints at the joint points, the parameters of piecewise analytic ascent trajectory could be automatically modulated. Additionally, the real-time robustness of the analytic trajectory is guaranteed by replacing initial values with current states. Then, the referenced program pitch angle with far more practical application value for tracking problem is exported. Next, a controller integrating nonsingular terminal sliding mode control (NTSMC) with adaptive control is employed to guarantee that the closed-loop tracking system is robust under time-varying uncertainties with unknown bounds. The finite-time convergence is theoretically proved by detailed analyses on Lyapunov functions. In the end, simulation results demonstrate the feasibility of the piecewise analytic optimized ascent trajectory and the validity of the tracking controller in comparison with NTSMC and proportional derivative (PD) control. [ABSTRACT FROM AUTHOR]

Existing state-of-the-art frequency adaptation mechanisms of central pattern generators (CPGs) for robot locomotion control typically rely on correlation-based learning. They do not account for the tracking error that may occur between the actual system motion and CPG output, leading to the loss of precision, unwanted movement, inefficient energy locomotion, and in the worst cases, motor collapse. To overcome this problem, we developed online error-based learning for frequency adaptation of CPGs. The learning mechanism used for error reduction is a novel modification of the dual learner (DL) called dual integral learner (DIL). Being able to reduce tracking and steady-state errors, it can also perform fast and stable learning, adapting the CPG frequency to match the performance of robotic systems. Control parameters of the DIL are more straightforward for complex systems (like walking robots), compared to traditional correlation-based learning, since they correspond to error reduction. Due to its embedded memory, the DIL can relearn quickly and recover spontaneously from the previously learned parameters. All these features are not covered by the existing frequency adaptation mechanisms. We integrated the DIL into a neural CPG-based motor control system for use on different legged robots with various morphologies for evaluation. The results show that: 1) the DIL does not require precise adjustment of its parameters to fit specific robots; and 2) the DIL can automatically and quickly adapt the CPG frequency to the robots such that the entire trajectory of the CPG can be precisely followed with very low tracking and steady-state errors. Consequently, the robots can perform the desired movements with more energy-efficient locomotion compared to the state-of-the-art correlation-based learning mechanism called frequency adaptation through fast dynamical coupling (AFDC). In the future, the proposed error-based learning mechanism for fast online adaptation in robot motor control can be used as a basis for trajectory optimization, universal controllers, and other studies concerning the change of intrinsic or extrinsic parameters. [ABSTRACT FROM AUTHOR]

• All our feature analyses and experiments are conducted on the ground-truth datasets. • The distributions of features on the positive instances and the negative instances are different. • The features related to the 1-hop neighbors contribute to user identification much more than do other relationship-based features. • The information redundancies in k-hop neighbors are also very useful for user identification. User identification has been attracting considerable attention from academia. Due to the uniqueness and difficulty of faking friendship networks, some friendship-based methods have been presented to improve the identification performance. However, the information redundancies in k -hop (k > 1) neighbors and their contributions to user identification have not been fully analyzed in the existing work. Addressing these two issues helps to understand the problem of friendship-based user identification and to propose more effective solutions. In this paper, we first obtain ground-truth friendship networks across three popular social sites; then, we analyze the similarities of k -hop neighbors to fully characterize the information redundancies in the friendship network. We apply these information redundancies in several classifiers to study their contributions to user identification. Furthermore, we apply the friendship-based information redundancies jointly with the display-name-based information redundancies to perform user identification. The experiments show that (1) the similarities related to the 1-hop neighbors contribute to user identification much more than do the other similarities; (2) the information redundancies in the k -hop (k > 1) neighbors are also very useful for user identification; and (3) jointly applying display-name-based information redundancies can provide better performance and improve the universality of the identification method. [ABSTRACT FROM AUTHOR]

This paper addresses a one-dimensional optimal persistent monitoring problem using second-order agents. The goal is to control the movements of agents to minimize a performance metric associated with the environment (targets) over a finite time horizon. In contrast to earlier results limited to first-order dynamics for agents, we control their accelerations rather than velocities, thus leading to a better approximation of agent behavior in practice and to smoother trajectories. Bounds on both velocities and accelerations are also taken into consideration. Despite these added complications to agent dynamics, we derive a necessary condition for optimality and show that the optimal agent trajectories can be fully characterized by two parameter vectors. A gradient-based algorithm is proposed to optimize these parameters and yield a minimal performance metric. In addition, a collision avoidance algorithm is proposed to solve potential collision and boundary-crossing problems, thus extending the gradient-based algorithm solutions. Finally, simulation examples are included to demonstrate the effectiveness of our results. [ABSTRACT FROM AUTHOR]

Based on the overview of past Mars exploration missions and the requirements for future Mars landing missions, the importance of the Mars entry trajectory optimization and guidance techniques is analyzed. The challenges for the Mars atmospheric entry trajectory optimization and guidance are analyzed and summarized，based on the analysis of the Mars landing environment and the aerodynamic characteristics of the entry vehicles. Key techniques corresponding to the trajectory optimization problems and guidance schemes are proposed. Present researches and the development trend of the key techniques are analyzed synthetically. Finally，the development direction of the key techniques for Mars entry guidance of future Mars precision landing missions are previewed. [ABSTRACT FROM AUTHOR]

• The Van der Pol oscillator is employed as a reduced-order model for the control optimization of cylinder wakes. • A subspace identification strategy is used to relate the inputs to the higher dimensional model and the reduced-order model. • Suitable tracking and regulator problems are designed and resolved to determine the optimal controls. • An open-loop optimal controller that generates the dynamic features of the closed-loop integral controller is developed. • The optimal controller was found to successfully control vortex shedding behind a cylindrical structure. • A plausible explanation for the flow control mechanism in terms of mechanical energy and work is provided. In the present study, we develop an energetically efficient suboptimal open-loop strategy to control the wake behind a circular cylinder in the laminar regime. The open-loop suboptimal controller is designed to resemble the feedback integral controller with reference to its dynamical behaviour. Energetic efficiency is measured using the power loss coefficient. The Van der Pol model for the evolution of lift force on the cylinder is chosen as the reduced-order model for the development of an open-loop suboptimal controller. The parameter estimation of the low- dimensional model is carried out using the results from the continuum based Navier - Stokes simulations. It is shown that a subspace identification method can be used to model the relationship between the inputs to the reduced-order model and the inputs to the higher-order computational fluid dynamic model. The development of the suboptimal control is realised by means of solving suitably formulated optimal tracking and regulator problems using the Pontryagin's minimum principle. The resultant controller is found to be energetically efficient and also successful in the control of vortex shedding. [ABSTRACT FROM AUTHOR]

We present a trajectory optimization approach to reinforcement learning in continuous state and action spaces, called probabilistic differential dynamic programming (PDDP). Our method represents systems dynamics using Gaussian processes (GPs), and performs local dynamic programming iteratively around a nominal trajectory in Gaussian belief spaces. Different from model-based policy search methods, PDDP does not require a policy parameterization and learns a time-varying control policy via successive forward-backward sweeps. A convergence analysis of the iterative scheme is given, showing that our algorithm converges to a stationary point globally under certain conditions. We show that prior model knowledge can be incorporated into the proposed framework to speed up learning, and a generalized optimization criterion based on the predicted cost distribution can be employed to enable risk-sensitive learning. We demonstrate the effectiveness and efficiency of the proposed algorithm using nontrivial tasks. Compared with a state-of-the-art GP-based policy search method, PDDP offers a superior combination of learning speed, data efficiency, and applicability. [ABSTRACT FROM AUTHOR]

We consider the trajectory fitting estimators (TFEs) of a parameter in the drift coefficient of a non-recurrent diffusion processes introduced by Keller et al. (1984). We show the strong consistence and Gaussian limit distribution of the TFEs when one continuously observes a sample path over a time interval and obtain rate of convergence greater-order than the standard case. These results extend Dietz (2001) and Dietz and Kutoyants (2003) results in linear and polynomial cases. Numerical simulations illustrate the behavior of the estimators. [ABSTRACT FROM AUTHOR]

We study a notion of estimation entropy for continuous-time nonlinear systems, formulated in terms of the number of system trajectories that approximate all other trajectories up to an exponentially decaying error. We also consider an alternative definition of estimation entropy, which uses approximating functions that are not necessarily trajectories of the system, and show that the two entropy notions are equivalent. We establish an upper bound on the estimation entropy in terms of the sum of the desired convergence rate and an upper bound on the matrix measure of the Jacobian, multiplied by the system dimension. A lower bound on the estimation entropy is developed as well. We then turn our attention to state estimation and model detection with quantized and sampled state measurements. We describe an iterative procedure that uses such measurements to generate state estimates that converge to the true state at the desired exponential rate. The average bit rate utilized by this procedure matches the derived upper bound on the estimation entropy, and no other algorithm of this type can perform the same estimation task with bit rates lower than the estimation entropy. Finally, we discuss an application of the estimation procedure in determining, from the quantized state measurements, which of two competing models of a dynamical system is the true model. We show that under a mild assumption of “exponential separation” of the candidate models, detection always happens in finite time. [ABSTRACT FROM AUTHOR]

Based on the morphing requirements of the glide vehicles, a morphing glide vehicle (MGV) with high environmental adaptability is proposed. The trajectory optimization model of an MGV with redundant control variables is established. The pseudospectral method is used to the trajectory optimization, and the maneuverability and gliding ability of the MGV are quantitatively analyzed. The simulation results show that in some typical combat modes, the MGV ’ s performance can be effectively enhanced. At the same time, the optimization results can guide the morphing strategy and control system design. This article provides a reference for the scheme design and mission planning of the morphing vehicles. [ABSTRACT FROM AUTHOR]

This paper deals with the optimal formation control problem of multi-agent systems under a prior unknown desired shapes. The objective of this paper is to accomplish a rolling optimal formation of multi-agent systems by incorporating trajectory tracking and energy saving control into a unified framework. First, according to the real-time desired position offset between each follower and the leader, the rolling optimization performance indexes are introduced. Then, a distributed rolling optimization algorithm is proposed to realize the desired formation of multi-agent systems under the minimum energy consumption of agents. Finally, several simulation examples are conducted to illustrate the effectiveness of the proposed design algorithm. [ABSTRACT FROM AUTHOR]

The problem of position tracking of a mini drone subject to wind perturbations is investigated. The solution is based on a detailed unmanned aerial vehicle (UAV) model, with aerodynamic coefficients and external disturbance components, which is introduced in order to better represent the impact of the wind field. Then, upper bounds of wind-induced disturbances are characterized, which allow a sliding mode control (SMC) technique to be applied with guaranteed convergence properties. The peculiarity of the considered case is that the disturbance upper bounds depend on the control amplitude itself ( i.e . the system is nonlinear in control), which leads to a new procedure for the control tuning presented in the paper. The last part of the paper is dedicated to the analysis and reduction of chattering effects, as well as investigation of rotor dynamics issues. Conventional SMC with constant gains, proposed first order SMC, and proposed quasi-continuous SMC are compared. Nonlinear UAV simulator, validated through in-door experiments, is used to demonstrate the effectiveness of the proposed controls. [ABSTRACT FROM AUTHOR]

An adaptive mesh refinement melhod for solving the trajectory optimization problem is presented, which consists of the grid point insertion algorithm and the grid point elimination algorithm. The grid point insertion algorithm analyzes the interpolation errors of control variables at each of the grid point，and adds some neighboring grid points if the interpolation errors are large. The grid point elimination algorithm calculates the left and the right slopes of control variables at each of the grid point，and deletes the grid point where both the left and the right slopes are equal to zero. The proposed method is verified by using three typical optimal control problems and compared with several other mesh refinement methods. The numerical results indicate that the proposed method generates significantly smaller mesh size and requires even fewer mesh iterations for a given accuracy when compared with other mesh refinement methods. As a result, the proposed method can solve non-smooth trajectory optimization problems rapidly and accurately. [ABSTRACT FROM AUTHOR]

Presents a scientific explanation for why the sidespin helps the tenpin bowler score well. Alley and bowling ball trajectory; Friction on a sliding ball; Variations in the ball's deflection; Ball's angle of approach to pins.

The article reports that the congestion of airspace around the world is increasing rapidly, and pressure is mounting on the aviation industry to seek more sustainable practices. In Europe alone, skies are expected to see a 50 percent increase in flights in the next 20 years, while recent estimates anticipate 40 times more commercial unmanned aerial vehicles (UAVs) than commercial aircraft will be flying beyond visual line-ofsight by 2030.

In this paper, we analyze the 1-periodic schedule of a class of time interval models under the form of a polyhedron which can describe Timed Event Graphs and P-time Event Graphs. Using the duality and Stiemke's theorem, the main contribution is the determination of conditions where the extremum cycle times are finite and characteristic of a class of models. [ABSTRACT FROM AUTHOR]

An improved m ultiresolution-based approach is proposed to refine the m esh points in all the nonsmooth regions along the trajectory and avoid the omissions or failures of the mesh refinem ent that have been encountered for the existing m ultiresolution techniques. The method is designed by adding the m esh points from two higher resolution levels in each refinem ent iteration and introducing a m esh point checking algorithm. A generalized dyadic m esh is proposed so that the modified m ultiresolution technique can start with any uniform initial mesh with an odd num ber of the m esh p o in ts, thus overcoming the dyadic restrictions of the original m ultiresolution techniques. The effectiveness of the method is dem onstrated on several examples and its performance is com pared against other m esh refinem ent m ethods. In addition, the num erical results show that a fully refined m esh has little im pact on the objective but it ensures that the path or term inal constraints are more accurately satisfied. [ABSTRACT FROM AUTHOR]

In this paper, a new adaptive optimal guidance law with impact angle and seeker's field-of-view (FOV) angle constraints is proposed. To this end, the generalized optimal guidance law is derived first. A changeable impact angle weighting (IAW) coefficient is introduced and used to modify the guidance law to make it adaptive for all guidance constraints. After integrating the closed-form solution of the guidance command with linearized engagement kinematics, the analytic predictive models of impact angle and FOV angle are built, and the available range of IAW corresponding to constraints is certain. Next, a calculation scheme is presented to acquire the real-time value of IAW during the entire guidance process. When applying the proposed guidance law, the IAW will keep small to avoid a trajectory climbing up to limit FOV angle at an initial time but will increase with the closing target to improve impact position and angle accuracy, thereby ensuring that the guidance law can juggle orders of guidance accuracy and constraints control. [ABSTRACT FROM AUTHOR]

This paper presents a strategy for the soft landing trajectory optimization on an asteroid. The interior gravity field model, a counterpart of the classical spherical harmonic model，is used to estimate the gravity acceleration near the target asteroid. This model has an advantage in computational efficiency and its convergence properties can be guaranteed within the constructed interior Brillouin sphere. The soft-landing optimization problem on an asteroid is converted into a successive second-order convex programming (SOCP) through constraints slack，linearization of dynamics and discretization which can be solved by the primal-dual interior-point methods. The numerical simulations shows that the lander arrives at the target site with zero velocity and all the constraints are satisfied. The fuel consumption is also optimal. Solving soft-landing optimization problem on an asteroid with convex programming has an advantage in computational effectiveness and can potentially be implemented in the future asteroid mission. [ABSTRACT FROM AUTHOR]

Rotary steerable system (RSS) is a directional drilling technique which has been applied in oil and gas exploration under complex environment for the requirements of fossil energy and geological prospecting. The nonlinearities and uncertainties which are caused by dynamical device, mechanical structure, extreme downhole environment and requirements of complex trajectory design in the actual drilling work increase the difficulties of accurate trajectory tracking. This paper proposes a model-based dual-loop feedback cooperative control method based on interval type-2 fuzzy logic control (IT2FLC) and actor-critic reinforcement learning (RL) algorithms with one-order digital low-pass filters (LPF) for three-dimensional trajectory tracking of RSS. In the proposed RSS trajectory tracking control architecture, an IT2FLC is utilized to deal with system nonlinearities and uncertainties, and an online iterative actor-critic RL controller structured by radial basis function neural networks (RBFNN) and adaptive dynamic programming (ADP) is exploited to eliminate the stick–slip oscillations relying on its approximate properties both in action function (actor) and value function (critic). The two control effects are fused to constitute cooperative controller to realize accurate trajectory tracking of RSS. The effectiveness of our controller is validated by simulations on designed function tests for angle building hole rate and complete downhole trajectory tracking, and by comparisons with other control methods. [ABSTRACT FROM AUTHOR]

The hypersonic interception in near space is a great challenge because of the target's unpredictable trajectory, which demands the interceptors of trajectory cluster coverage of the predicted area and optimal trajectory modification capability aiming at the consistently updating predicted impact point (PIP) in the midcourse phase. A novel midcourse optimal trajectory cluster generation and trajectory modification algorithm is proposed based on the neighboring optimal control theory. Firstly, the midcourse trajectory optimization problem is introduced; the necessary conditions for the optimal control and the transversality constraints are given. Secondly, with the description of the neighboring optimal trajectory existence theory (NOTET), the neighboring optimal control (NOC) algorithm is derived by taking the second order partial derivations with the necessary conditions and transversality conditions. The revised terminal constraints are reversely integrated to the initial time and the perturbations of the co-states are further expressed with the states deviations and terminal constraints modifications. Thirdly, the simulations of two different scenarios are carried out and the results prove the effectiveness and optimality of the proposed method. [ABSTRACT FROM AUTHOR]

In a direct local collocation method, a trajectory optimization problem is transcribed into a nonlinear programming (NLP) problem. Solving this NLP as efficiently as possible requires thai the sparsity of ihe NLP derivatives should be exploited and the derivatives should he efficiently calculated. In this paper, a computational efficient melhod is developed for computing the first derivatives of the NLP functions arising from a local discretization of a trajectory optimization problem. Specifically, the expressions are derived for the NLP objective function gradient and constraint Jacobian. It is shown that the NLP derivatives can be reduced to the first derivatives of the functions in the trajectory optimization problem. As a resull, the method derived in this paper reduces signiiioantly the amount of computalion required to obtain the first-derivatives required by a NLP solver. The approach derived in this paper is flemonstrated by an example with dist-rete aerodynamic data and ihmst data where it is fomnd that the time required to solve the NLP is reduced lo less than 4% compared with the direct flif'ferentiation of the NLP functions using a finite difference method,and the efficiency improvement is more significant as llie number of the grid points increases. [ABSTRACT FROM AUTHOR]

Based on the analytical solutions of T-H equations and its state transition matrix form, the open-loop control method of spacecraft impulsive relative hovering was studied， which is promising for practical engineering use. The true anomaly intervals of the hovering impulse were optimized by the nonlinear mathematical programming. Based on the calculation of collision probability, the method of safety analysis and risk management was proposed. The numerical simulations show that the introduced relative hovering method can be used for circular and elliptical reference orbits hovering. Furthermore, the local optimal solution can be obtained by applying the true anomaly intervals optimization method. The maximum collision probability and the minimum relative distance nearly appear at the same time. And, the smaller the relative distance is, the larger the collision probability. [ABSTRACT FROM AUTHOR]

This paper presents a combined strategy to solve the trajectory online optimization problem for unmanned combat aerial vehicle (UCAV). Firstly, as trajectory directly optimizing is quite time costing, an online trajectory functional representation method is proposed. Considering the practical requirement of online trajectory, the 4-order polynomial function is used to represent the trajectory, and which can be determined by two independent parameters with the trajectory terminal conditions; thus, the trajectory online optimization problem is converted into the optimization of the two parameters, which largely lowers the complexity of the optimization problem. Furthermore, the scopes of the two parameters have been assessed into small ranges using the golden section ratio method. Secondly, a multi-population rotation strategy differential evolution approach (MPRDE) is designed to optimize the two parameters; in which, "current-to-best/1/bin", "current-torand/ 1/bin" and "rand/2/bin" strategies with fixed parameter settings are designed, these strategies are rotationally used by three subpopulations. Thirdly, the rolling optimization method is applied to model the online trajectory optimization process. Finally, simulation results demonstrate the efficiency and real-time calculation capability of the designed combined strategy for UCAV trajectory online optimizing under dynamic and complicated environments. [ABSTRACT FROM AUTHOR]

The optimal control problem of spacecraft rendezvous and formation reconfiguration without radial thrust is investigated by using the state dependent Riccati equation (SDRE) method. Considering the thrusters installed in the circumferential and normal directions，the nonlinear relative motion equations influenced by J2 perturbation are derived and presented via the state-dependent coefficient (SDC). Then the optimal feedback control law is derived by using the SDRE method, and an approximate analytical solution of the state dependent Riccati differential equation is obtained. Moreover, a numerical version of the obtained analytical solution is extended to solve the formation reconfiguration problem. Compared with the Gauss pseudospectral spectral method，the proposed method does not need an initial guess. Numerical results show that the analytical solution of the Riccati differential equation method has high precision for the near circular orbits，and the error of the optimal index of the numerical method is less than 6% even for the elliptical orbits with an eccentricity approaching to 0. 3. [ABSTRACT FROM AUTHOR]

We propose a new dynamic extension of the thrust variable in the quadcopter dynamics that preserves the positive sign of the thrust. This extension not only eliminates the positive sign constraint on the thrust variable, but also leads to global chartwise feedback linearization of the quadcopter dynamics. For the latter, an atlas is first constructed on the entire state space of the quadcopter and then the dynamically extended quadcopter system is transformed to a 14-dimensional linear controllable system on each chart in the atlas. Based on the chartwise dynamic feedback linearization, a global tracking strategy is proposed for the quadcopter and its excellent performance is demonstrated with a simulation. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of synchronizing a group of identical linear time-invariant agents that exchange information through a communication network. The agents may only broadcast information at discrete-time instants and the decision to execute a broadcast is based on an event-triggered communication protocol. We prove that with the proposed control architecture the state of each agent converges to and remains in a neighborhood of a desired reference signal and the closed-loop system does not exhibit Zeno solutions. A self-triggered implementation of the proposed event-triggered communication protocol is also derived. [ABSTRACT FROM AUTHOR]

We present necessary and sufficient optimality conditions for finite-time optimal control problems for a class of hybrid systems described by linear complementarity models. Although these optimal control problems are difficult in general due to the presence of complementarity constraints, we provide a set of structural assumptions ensuring that the tangent cone of the constraints possesses geometric regularity properties. These imply that the classical Karush–Kuhn–Tucker conditions of nonlinear programming theory are both necessary and sufficient for local optimality, which is not the case for general mathematical programs with complementarity constraints. We also present sufficient conditions for global optimality. We proceed to show that the dynamics of every continuous piecewise affine (PWA) system can be written as the optimizer of a mathematical program, which results in a linear complementarity model satisfying our structural assumptions. Hence, our stationarity results apply to a large class of hybrid systems with PWA dynamics. We present simulation results showing the substantial benefits possible from using a nonlinear programming approach to the optimal control problem with complementarity constraints instead of a more traditional mixed-integer formulation. [ABSTRACT FROM AUTHOR]

We study a distributed consensus-based stochastic gradient descent (SGD) algorithm and show that the rate of convergence involves the spectral properties of two matrices: The standard spectral gap of a weight matrix from the network topology and a new term depending on the spectral norm of the sample covariance matrix of the data. This data-dependent convergence rate shows that distributed SGD algorithms perform better on datasets with small spectral norm. Our analysis method also allows us to find data-dependent convergence rates as we limit the amount of communication. Spreading a fixed amount of data across more nodes slows convergence; for asymptotically growing datasets, we show that adding more machines can help when minimizing twice-differentiable losses. [ABSTRACT FROM AUTHOR]

Hybrid optimal control problems are studied for a general class of hybrid systems, where autonomous and controlled state jumps are allowed at the switching instants, and in addition to terminal and running costs, switching between discrete states incurs costs. The statements of the Hybrid Minimum Principle and Hybrid Dynamic Programming are presented in this framework, and it is shown that under certain assumptions, the adjoint process in the Hybrid Minimum Principle and the gradient of the value function in Hybrid Dynamic Programming are governed by the same set of differential equations and have the same boundary conditions and hence are almost everywhere identical to each other along optimal trajectories. Analytic examples are provided to illustrate the results. [ABSTRACT FROM AUTHOR]

The robust trajectory tracking of fully actuated Lagrange systems is studied. Exogenous perturbations as well as parameter uncertainties are taken into account. A family of set-valued passivity-based controllers is proposed, including first-order sliding-mode schemes. The existence of solutions and the stability of the closed-loop system are established in continuous time. An implicit discretization approach is proposed and the well posedness and the stability of the closed-loop system are studied. Numerical simulations illustrate the effectiveness of the proposed discrete-time controller. [ABSTRACT FROM AUTHOR]

We study three existing issues associated with optimization of long-run average rewards of time-nonhomogeneous Markov processes in continuous time with continuous state spaces: 1) the underselectivity, i.e., the optimal policies do not depend on their actions in any finite time period; 2) its related issue, the bias optimality, i.e., policies that optimize both long-run average and transient total rewards, and 3) the effects of a nonsmooth point of a value function on performance optimization. These issues require considerations of the performance in the entire period with an infinite horizon, and therefore are not easily solvable by dynamic programming, which works backwards in time and takes a local view at a particular time instant. In this paper, we take a different approach called the relative optimization theory, which is based on a direct comparison of the performance measures of any two policies. We derive tight necessary and sufficient optimality conditions that take the underselectivity into consideration; we derive bias optimality conditions for both long-run average and transient rewards; and we show that the effect of a wide class of nonsmooth points, called semismooth points, of a value function on the long-run average performance is zero and can be ignored. [ABSTRACT FROM AUTHOR]