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Aviation is often seen as an innovative sector eager to seek technological solutions. It is often assumed that aircraft cockpits have panels with electronic displays and many buttons, some labeled "auto." While this might be the case for commercial aviation (Figure 1), the vast majority of noncommercial civil aircraft cockpits look nothing like these modern cockpits. Non-commercial civil aircraft, known as general aviation (GA), typically have cockpits that mainly consist of analog instruments that show the heading of the aircraft, the altitude, and the air speed (Figure 2). While flying, the pilots make calculations using a compass and an aviation calculator. Due to the slow entry rate of new, glass-cockpit aircraft [1], the composition of the GA aircraft fleet is not going to change any time soon. [ABSTRACT FROM AUTHOR]

In today's world, with time at a premium, people increasingly use aircraft for travel and for transporting goods from one place to another. This has resulted in greater chances of aircraft accidents. There are many reasons for these accidents, ranging from pilot error to instrument or machinery failure. Basically, there are five phases of air travel during which these accidents can occur: take off, initial climb, en-route, landing-approach, and touchdown. In the last few years, the accidents during the en-route phase are on the increase, e.g., from 2012 till 2014 they have increased from 3 in 2012 to 8 in 2013 to 13 in 2014. [ABSTRACT FROM PUBLISHER]

Constrained multiobjective optimization problems (CMOPs) are frequently encountered in real-world applications, which usually involve constraints in both the decision and objective spaces. However, current artificial CMOPs never consider constraints in the decision space (i.e., decision constraints) and constraints in the objective space (i.e., objective constraints) at the same time. As a result, they have a limited capability to simulate practical scenes. To remedy this issue, a set of CMOPs, named DOC, is constructed in this paper. It is the first attempt to consider both the decision and objective constraints simultaneously in the design of artificial CMOPs. Specifically, in DOC, various decision constraints (e.g., inequality constraints, equality constraints, linear constraints, and nonlinear constraints) are collected from real-world applications, thus making the feasible region in the decision space have different properties (e.g., nonlinear, extremely small, and multimodal). On the other hand, some simple and controllable objective constraints are devised to reduce the feasible region in the objective space and to make the Pareto front have diverse characteristics (e.g., continuous, discrete, mixed, and degenerate). As a whole, DOC poses a great challenge for a constrained multiobjective evolutionary algorithm (CMOEA) to obtain a set of well-distributed and well-converged feasible solutions. In order to enhance current CMOEAs’ performance on DOC, a simple and efficient two-phase framework, named ToP, is proposed in this paper. In ToP, the first phase is implemented to find the promising feasible area by transforming a CMOP into a constrained single-objective optimization problem. Then in the second phase, a specific CMOEA is executed to obtain the final solutions. ToP is applied to four state-of-the-art CMOEAs, and the experimental results suggest that it is quite effective. [ABSTRACT FROM AUTHOR]

This paper presents an active learning strategy for robotic systems that takes into account task information, enables fast learning, and allows control to be readily synthesized by taking advantage of the Koopman operator representation. We first motivate the use of representing nonlinear systems as linear Koopman operator systems by illustrating the improved model-based control performance with an actuated Van der Pol system. Information-theoretic methods are then applied to the Koopman operator formulation of dynamical systems where we derive a controller for active learning of robot dynamics. The active learning controller is shown to increase the rate of information about the Koopman operator. In addition, our active learning controller can readily incorporate policies built on the Koopman dynamics, enabling the benefits of fast active learning and improved control. Results using a quadcopter illustrate single-execution active learning and stabilization capabilities during free fall. The results for active learning are extended for automating Koopman observables and we implement our method on real robotic systems. [ABSTRACT FROM AUTHOR]

This paper proposes a model predictive current control (MPCC) method of six-phase permanent magnet synchronous hub motor (PMSHM) using auxiliary voltage vectors. First of all, 26 voltage vectors can be obtained from the switching states of six inverters, and 24 auxiliary voltage vectors can be generated by combining them. Second, four virtual voltage vectors can be found through the required locations, and the two vectors with the lowest cost will be selected by cost function. Then, additional current error correction module can better track the current. Finally, the proposed method is compared with the conventional MPCC method and another method with virtual voltage vectors. The innovation of this paper is that the recombination of vectors can better track and predict the current. A current error compensation module is added to reduce the error between the actual current and the predicted current. And the experimental results show that the proposed method has better performance such as smaller torque ripple and current THD both in steady and dynamic states. Finally, the strategy proposed in this paper was applied to the vehicle model through HIL test platform. The possibility of applying the strategy proposed in this paper to pure electric vehicles was verified under New European Driving Cycle (NEDC). [ABSTRACT FROM AUTHOR]

This article proposes a hierarchical learning architecture for safe data-driven control in unknown environments. We consider a constrained nonlinear dynamical system and assume the availability of state-input trajectories solving control tasks in different environments. In addition to task-invariant system state and input constraints, a parameterized environment model generates task-specific state constraints, which are satisfied by the stored trajectories. Our goal is to use these trajectories to find a safe and high-performing policy for a new task in a new, unknown environment. We propose using the stored data to learn generalizable control strategies. At each time step, based on a local forecast of the new task environment, the learned strategy consists of a target region in the state space and input constraints to guide the system evolution to the target region. These target regions are used as terminal sets by a low-level model predictive controller. We show how to i) design the target sets from past data and then ii) incorporate them into a model predictive control scheme with shifting horizon that ensures safety of the closed-loop system when performing the new task. We prove the feasibility of the resulting control policy, and apply the proposed method to robotic path planning, racing, and computer game applications. Note to Practitioners—This paper was motivated by the challenge of designing safe controllers for autonomous systems navigating through new environments. We consider scenarios where trajectory data from control tasks in different environments is available to the control designer. Possible applications include autonomous vehicles racing on new tracks or robotic manipulators performing tasks in the presence of new obstacles. Existing approaches to model-based control design for new environments generally use trajectory libraries, systematically adapting stored trajectories to the constraints of the new environment. This typically requires a priori knowledge of the entire task environment as well as resources to store and maintain the growing library. This paper suggests a new hierarchical control approach, in which stored trajectories are used to learn high-level strategies that can be applied while solving the new task. The strategies are learned offline, and only the parameterized strategy function needs to be stored for online control. Strategies only require knowledge of the nearby task environment, and provide navigation guidelines for the system. In this paper we show how to find such strategies from previous task data and how to integrate them into a low-level controller to safely and efficiently solve the new task. We also show how to adapt the modular framework as needed for a user’s desired application. Simulation experiments in robotic manipulator, autonomous vehicle, and computer game examples suggest that our approach can be used in a wide range of applications. In future research, we will address how to adapt the method for time-varying or stochastic environments. [ABSTRACT FROM AUTHOR]

This paper develops an intelligent grid-interactive building controller, which optimizes building operation during both normal hours and demand response (DR) events. To avoid costly on-demand computation and to adapt to non-linear building models, the controller utilizes reinforcement learning (RL) and makes real-time decisions based on a near-optimal control policy. Learning such a policy typically amounts to solving a hard non-convex optimization problem. We propose to address this problem with a novel global-local policy search method. In the first stage, an RL algorithm based on zero-order gradient estimation is leveraged to search for the optimal policy globally, due to its scalability and the potential to escape some poor performing local optima. The obtained policy is then fine-tuned locally to bring the first-stage solution closer to that of the original unsmoothed problem. Experiments on a simulated five-zone commercial building demonstrate the advantages of the proposed method over existing learning approaches. They also show that the learned control policy outperforms a pragmatic linear model predictive controller (MPC) and approaches the performance of an oracle MPC in testing scenarios. Using a state-of-the-art advanced computing system, we demonstrate that the controller can be learned and deployed within hours of training. [ABSTRACT FROM AUTHOR]

The application of inter-vehicle wireless communication offers significant advantages in terms of improving traffic efficiency and safety while also suffering from unreliable communication links. This paper deals with the problem of event-triggered vehicular platoon control with communication time delay and package dropout, aiming to achieve a suitable trade-off between communication utilization and vehicle following performance. A disturbance based platoon model with communication time delay, package dropout and event-triggered scheme is established. Subsequently, the criterion for co-designing the event-trigger parameters and CACC controller parameters is derived by adopting the parameter space approach with the requirements to ensure individual vehicle internal stability, following accuracy and platoon string stability. To achieve a superior trade-off between communication resource utilization and following performance under unreliable communication condition, the adaptive event-triggered threshold is designed for the varying failure probability. The effectiveness of the proposed adaptive CACC strategy is verified using numerical simulations of a six-vehicle platoon during the FTP 72 drive cycle. The comparison results of three different scenarios show that the adaptive event-triggered scheme guarantees the following performance even when failure probability is 0.4. [ABSTRACT FROM AUTHOR]

This work investigates energy-optimal motion planning for a class of multiaxis motion systems where the system dynamics are linear time-invariant and decoupled in each axis. Solving the problem in a reliable and efficient manner remains challenging owing to the presence of various constraints on control and states, nonconvexity in its cost function, and obstacles. This paper shows how the cost function can be convexified by considering the system dynamics, while decomposing decision variables to obtain a convex representation of collision avoidance constraints. With the convexified cost function and constraints, the original problem is decomposed into two quadratic programming (QP) problems. An alternating quadratic programming (AQP) algorithm is proposed to solve both the QP problems alternatingly and iteratively till convergence. Requiring an initial feasible trajectory as a guess, AQP necessarily converges to an energy-efficient solution that is homotopic to the initial guess. Under certain circumstances, AQP is guaranteed to produce a local optimum. Simulation demonstrates that AQP is computationally efficient and reliable while claiming comparable energy saving as the mixed-integer QP approach. Note to Practitioners—This paper presents an energy-optimal motion planning algorithm that can be easily implemented on a class of multiaxis motion systems. Main advantages of the proposed algorithm are: 1) it produces a trajectory resulting in lower but comparable energy efficiency as the global optimum; 2) it is guaranteed to provide an energy-efficient and constraint-compliant trajectory, and thus is reliable; 3) it requires a low computational load and can be deployed on a wide range of applications; and 4) its implementation is straightforward to any engineer with basic knowledge of numerical methods. [ABSTRACT FROM AUTHOR]

This paper proposes a new motion-copying system that is capable of preserving and reproducing the motion of human operators. In conventional motion-copying systems, when the operator moves quickly, the reproduced force does not correspond to the stored force, owing to the large accelerations. Therefore, the motion-copying system proposed in this paper uses acceleration responses provided by acceleration observers, so the force is reproduced more accurately. The acceleration observer is based on a disturbance observer; it estimates the acceleration of an actuator without acceleration sensors. Furthermore, the estimation performance is not affected by the mechanical impedance of the target environment. In this article, the proposed motion-copying system is compared with current methods, and the validity of the method is demonstrated. [ABSTRACT FROM PUBLISHER]

In this paper, we present a modular framework for solving a motion planning problem among a group of robots. The proposed framework utilizes a finite set of low-level motion primitives to generate motions in a gridded workspace. The constraints on allowable sequences of motion primitives are formalized through a maneuver automaton. At the high level, a control policy determines which motion primitive is executed in each box of the gridded workspace. We state general conditions on motion primitives to obtain provably correct behavior so that a library of safe-by-design motion primitives can be designed. The overall framework yields a highly robust design by utilizing feedback strategies at both the low and high levels. We provide specific designs for motion primitives and control policies suitable for multirobot motion planning; the modularity of our approach enables one to independently customize the designs of each of these components. Our approach is experimentally validated on a group of quadrocopters. [ABSTRACT FROM AUTHOR]

The use of electrical vertical takeoff and landing (eVTOL) aircraft to provide efficient, high-speed, on-demand air transportation within a metropolitan area is a topic of increasing interest, which is expected to bring fundamental changes to the city infrastructures and daily commutes. NASA, Uber, and Airbus have been exploring this exciting concept of Urban Air Mobility (UAM), which has the potential to provide meaningful door-to-door trip time savings compared with automobiles. However, the ability to manage many of these eVTOL aircraft safely in a congested urban area presents a challenge unprecedented in air traffic management. In order to enable safe and efficient autonomous on-demand free flight operations in UAM, a computational guidance algorithm with collision avoidance capability is designed and analyzed. The approach proposed in this paper is to formulate this problem as a Markov Decision Process (MDP) and solve it using an online algorithm Monte Carlo Tree Search (MCTS). For illustration, a high-density free flight airspace simulator is created to test the proposed algorithm’s performance. Numerical experiment results show that this proposed algorithm has fewer conflicts and near mid-air collisions than Optimal Reciprocal Collision Avoidance (ORCA), a state-of-the-art collision avoidance strategy. [ABSTRACT FROM AUTHOR]

This paper discusses a new control approach for robotic-assisted tele-echography. The control architecture follows a hierarchical approach, where explicit Cartesian force control arises as the primary task while orientation control is designed in the null space. The robot dynamics is driven by a 3-D time-of-flight camera and a force sensor. Based on depth camera and force data, contact stiffness is anticipated, allowing control adaptation before contact. This approach is adequate for tele-echographic tasks since it reduces robot dynamics before contact, enabling a smooth transition from free space to contact and vice versa. In contact, the environment stiffness is estimated online using force data and the manipulator inertial properties. A strong correlation between the stiffness perceived by the controller and the effective mass exists, being this correlation used in the estimation algorithm to improve force control performance. A set of experiments assess the control architecture, highlighting the relation between perceived stiffness and robot effective mass, including also clinical validation. Note to Practitioners—The teleoperation control architecture described in this paper relies on computed-torque techniques that take into account robot dynamics, contact stiffness estimation, task-space formulation, and null-space design. Our application is on the medical field, where a physician telecontrols a robot to obtain ultrasound images from a patient. The robot exhibits compliant behaviors in Cartesian space (for safety reasons) managed by the task-space formulation, and stiffer behaviors in orientation control managed by null-space design. This approach allows accurate orientation control with compliant positioning which is adequate for this type of teleoperation tasks. Due to free space and contact interactions, the proposed control approach anticipates the contact stiffness relying on vision sensing, adapting the control gains accordingly. While in contact, the perceived stiffness has a strong correlation with the effective mass, which is used together with force data for its estimation. This strategy allows slowing down robot dynamics toward contact, guaranteeing a smooth behavior, entailing also high-quality force tracking while in contact. All control details are described in this paper, allowing easy implementation not only for this specific context, but also for other telemanipulation architectures that require remote delicate handling, without jeopardizing force control and orientation control performances. [ABSTRACT FROM AUTHOR]

In this paper, we present the swarm intelligence (SI) concept and mention some metaheuristics belonging to the SI. We present the particle swarm optimization (PSO) algorithm and the ant colony optimization (ACO) method as the representatives of the SI approach. In recent years, researchers are eager to develop and apply a variety of these two methods, despite the development of many other newer methods as Bat or FireFly algorithms. Presenting the PSO and ACO we put their pseudocode, their properties, and intuition lying behind them. Next, we focus on their real-life applications, indicating many papers presented varieties of basic algorithms and the areas of their applications. [ABSTRACT FROM PUBLISHER]

Lighting consumes the largest amount of energy in buildings. Recently, many studies of energy-efficient lighting systems with a variety of sensor and communication technologies have been conducted as a way to increase the cost efficiency of lighting. However, earlier studies have mostly focused on energy efficiency, whereas they have not significantly considered the occupant’s satisfaction. Therefore, this paper proposes an energy-saving lighting control system considering the occupant’s satisfaction. The proposed system improves the energy efficiency and the occupant’s satisfaction by controlling lighting control parameters considering the characteristics of space and the occupant’s behavior patterns. Moreover, this paper deployed the proposed lighting systems in a building and operated them in a real work environment to evaluate the performance. The results showed that the proposed system reduced energy consumption up to 43% by replacing the existing fluorescent lights with the proposed lighting control systems. [ABSTRACT FROM PUBLISHER]

Finite-control-set model predictive current control (FCS-MPCC) always has large steady-state fluctuation and computational burden. In this paper, a novel FCS-MPCC without a modulator to drive permanent magnet synchronous hub motors (PMSHMs), which combines virtual vectors expansion scheme and duty cycle control was proposed. The lack of a modulator reduces the complexity of the control system. The virtual vectors are synthesized by using active vectors, which improve the accuracy of voltage selection, and further improve PMSHM's steady-state performance and reduce current harmonics. The duty cycle control uses a zero vector to obtain better steady-state performance. However, the duty cycle of the virtual vectors is limited by the synthesis method, and further analysis is needed. A new calculation process is proposed to reduce the amount of calculation. The deadbeat principle is used to get reference voltage which determines sectors. Then, the best voltage vector in the selected sector is determined by the predetermined cost function. The traditional MPCC and the duty cycle MPCC (DCMPCC) are used as a comparison item to compare with the proposed method to illustrate its effectiveness. Results confirm that improved MPCC has good steady-state performance while maintaining a fast dynamic response. [ABSTRACT FROM AUTHOR]

Multiple aircraft collision avoidance is a challenging problem due to a stochastic environment and uncertainty in the intent of other aircraft. Traditionally a layered approach to collision avoidance has been employed using a centralized air traffic control system, established rules of the road, separation assurance, and last minute pairwise collision avoidance. With the advent of Urban Air Mobility (air taxis), the expected increase in traffic density in urban environments, short time scales, and small distances between aircraft favor decentralized decision making on-board the aircraft. In this paper, we present a Markov Decision Process (MDP) based method, named FastMDP, which can solve a certain subclass of MDPs quickly, and demonstrate using the algorithm online to safely maintain separation and avoid collisions with multiple aircraft (1-on-n) while remaining computationally efficient. We compare the FastMDP algorithm’s performance against two online collision avoidance algorithms that have been shown to be both efficient and scale to large numbers of aircraft: Optimal Reciprocal Collision Avoidance (ORCA) and Monte Carlo Tree Search (MCTS). Our simulation results show that under the assumption that aircraft do not have perfect knowledge of other aircraft intent FastMDP outperforms ORCA and MCTS in collision avoidance behavior in terms of loss of separation and near mid-air collisions while being more computationally efficient. We further show that in our simulation FastMDP behaves nearly as well as MCTS with perfect knowledge of other aircraft intent. Our results show that FastMDP is a promising algorithm for collision avoidance that is also computationally efficient. [ABSTRACT FROM AUTHOR]

With the large-scale wind power integration into the power system, the inertia and damping characteristics of the system have been weakened, which is not conducive to fulfill the grid-friendly operation of wind power. Aiming at this, an adaptive control scheme combining virtual inertia control (VIC) and supplementary damping controller (SDC) is proposed in this paper. Firstly, the mathematical model of wind grid-connected power system with VIC is established. Secondly, the polytopic linear parameter varying (LPV) system is built to describe the uncertainty of system operating points, and the gap metric-based nonlinear evaluation method is used for polytope vertex configuration. Next, Youla-Kucera parametrization are extended to polytopic LPV system to design an adaptive damping control, in which VIC and SDC are coordinated to provide damping and virtual inertia simultaneously. Finally, the proposed adaptive control scheme is compared with several existing control techniques to validate its effectiveness and advantages in suppressing system oscillation in the modified IEEE 39-bus system integrated with 2 wind turbines. [ABSTRACT FROM AUTHOR]

In a human–robot coexisting environment, reaching the target place efficiently and safely is pivotal for a mobile service robot. In this paper, a Risk-based Dual-Tree Rapidly exploring Random Tree (Risk-DTRRT) algorithm is proposed for the robot motion planning in a dynamic environment, which provides a homotopy optimal trajectory on the basis of a heuristic trajectory. A dual-tree framework consisting of an RRT tree and a rewired tree is proposed for the trajectory searching. The RRT tree is a time-based tree, considering the future trajectory predictions of the pedestrians, and this tree is utilized to generate a heuristic trajectory. However, the heuristic trajectory is usually nonoptimal. Then, a line-of-sight (LoS) control checking algorithm is proposed to detect whether two time-based nodes can be rewired with the least cost. On the basis of the LoS control checking algorithm, a tree rewiring algorithm is proposed to optimize the heuristic trajectory. The tree generated in the tree rewiring process is called the rewired tree. The trajectory generated by the Risk-DTRRT algorithm proves to be optimal in the homotopy class of the heuristic trajectory. The navigation run time and the lengths of the planned trajectories are selected to demonstrate the effectiveness of the proposed algorithm. The experimental results in both simulation studies and real-world implementations reveal that our proposed method achieves convincing performance in both static and dynamic environments. Note to Practitioners—This paper is motivated by planning optimized trajectories for the mobile service robots in dynamic environments with pedestrians. In this area, the sampling-based motion planning algorithms have been widely used for their high efficiency and robustness. However, the real-time optimality of the motion planning cannot be guaranteed due to the challenges caused by the moving pedestrians. In this paper, we propose a dual-tree framework to solve this problem. First, a classic Rapidly exploring Random Tree (RRT) is constructed to generate a heuristic trajectory. Then, instead of reconnecting the nodes on the heuristic trajectory directly, a rewired tree is built to optimize the heuristic trajectory. This proposed dual-tree framework can fully exploit the information of the RRT tree and ensure the completeness of the motion planning. The proposed motion planning algorithm also considers the constraints of the nonholonomic mobile robots, and it can be applied in most mobile service robots to improve their motion planning quality. [ABSTRACT FROM AUTHOR]

Steered particles offer a method for targeted therapy, interventions, and drug delivery in regions inaccessible by large robots. For example, magnetic actuation of particles has the benefits of requiring no tethers, being able to operate from a distance, and in some cases allows imaging for feedback (e.g., MRI). This paper investigates position control of particles using uniform forces (the same force is applied everywhere in the workspace). Given a controllable field that can generate bidirectional forces in three orthogonal directions, steering one particle in three-dimensional (3-D) is trivial. Adding additional particles to steer makes the system underactuated because there are more states than control inputs. However, the walls of in vivo and artificial environments often have surface roughness such that the particles do not move unless actuation pulls them away from the wall. In the previous work, we showed that the individual two-dimensional (2-D) position of two particles is controllable using global inputs in a square workspace with nonslip wall contact. Because in vivo environments are usually not square, this paper extends the previous work to all convex workspaces, and shows how this could be extended to 3-D positioning of neutrally buoyant particles. We investigate analytically an idealized variant of this problem with nonslip boundaries and control inputs that are applied uniformly to all particles in the workspace. This paper also implements the algorithms in 2-D using a hardware setup inspired by the gastrointestinal tract. [ABSTRACT FROM AUTHOR]

This paper investigates the integral sliding mode control problem of T–S fuzzy interconnected descriptor system based on a reduced-order compensator. A canonical equivalent form for T–S fuzzy interconnected descriptor system is first introduced to facilitate the reduced-order compensator design. In terms of the measurable output variables of the T–S fuzzy interconnected descriptor system and the state variables of the proposed reduced-order compensator, an integral sliding mode control scheme is then developed for the T–S fuzzy interconnected descriptor system. The sliding motion is defined in the augmented space formed by the state variables of the original T–S fuzzy interconnected descriptor system and the resulting error system. It is shown that the designing parameters in the switching function can be simultaneously solved and the original T–S fuzzy interconnected descriptor system has no requirement to be relative degree one. Since the estimate error is bounded by an auxiliary dynamical system, when ideal sliding mode occurs, the resulting closed-loop system is asymptotically stable rather than uniformly ultimately bounded. Finally, a double-inverted pendulum system and a ball and beam system are numerically simulated to demonstrate the effectiveness and merits of the method proposed. [ABSTRACT FROM AUTHOR]

The optimal control of the heating, ventilation, and air-conditioning (HVAC) system in buildings has a significant energy saving potential and therefore is of a great practical interest. An event-based HVAC control adjusts control actions when certain events occur, which may be faster and more scalable than state-based or time-driven control methods. However, events may capture either local or global changes in the rooms. The choice of events is a tradeoff between the computational efficiency and the control performance. This challenging problem remains open. We consider this as an important problem in this paper and make three major contributions. First, we define local and global events for the HVAC control problem. The complexity of these event-based control policies is defined. Second, based on hypothesis testing, we develop a method to select events that capture a sufficient state information and with a relatively small event space. Third, we demonstrate the performance of this method on two groups of examples, including one group of small-scale problems for the proof of concept and the other group of large-scale problems in the HVAC control. It is shown that our method outperforms the Levin search, which is a traditional complexity-based search method and finds event-based HVAC control policies with a good performance. Note to Practitioners—When there are multiple rooms in a building, the HVAC control may achieve a significant energy saving and an indoor comfort satisfaction in the same time through exploring the coupling among the rooms. By appropriately defining the events, the size of the event space is usually much smaller than the state space. Therefore, an event-based control is more scalable and preferred in practice. Local events capture the state changes of rooms in a small neighborhood, which leads to a small event space but limited information. Global events capture the state changes of rooms in a large neighborhood, which leads to more information but a large event space. It remains open how to select events in large-scale HVAC control problems, especially when the computing budget is limited. In this paper, we define the complexity of an event-based control policy by the number of neighboring rooms considered. We develop a method based on hypothesis testing to select events with a proper complexity in order to achieve a good system performance. The performance of this method is demonstrated on an HVAC control problem. [ABSTRACT FROM AUTHOR]

The papers in this special section were presented at the 2017 Avionics and Vehicle Fiber-Optics and Photonics Conference (AVFOP). These papers examine recent advancements in the fiber-optics and photonics arena relevant to aerospace, seaborne, and land-based vehicular applications. [ABSTRACT FROM AUTHOR]

In this paper, an active suspension system employing a new nonlinear control law is proposed to address the problem of achieving the dual objective of providing ride comfort and trying to keep the suspension deflection within the constraint of rattle space. The control is a nonlinear function of the magnitude of the suspension deflection and an estimate of the effect of the road disturbance. The control scheme is analyzed and assessed for the large classes of road profiles through simulation and by experimentation on a laboratory setup. The performance of the proposed scheme is compared with a passive suspension system. [ABSTRACT FROM PUBLISHER]

An optimized control approach for the distributed cooperative vehicular platoon was proposed in this paper, considering actuator delay and nonideal communication condition. A hierarchical structure is used to model the vehicular platoon, where the desired acceleration is optimized by CACC controller and the longitudinal tracking controller is proposed to follow it. In the design process of CACC controller, a parameter space approach and linear quadratic regulator (LQR) method are applied to guarantee the string stability and optimized performance. The influence of communication time delay and dropout rate is revealed by the changing of string stable region. Analytical results are verified by numerical simulations of a six-vehicle platoon where the leading vehicle follows the FTP-75 drive cycle speed profile. Furthermore, a Hardware-in-the-Loop (HIL) test is carried out with Carsim to simulate a high-fidelity vehicle dynamic model and dedicated short range communication (DSRC) modems as hardware in the loop as well as the control unit to achieve the inter-vehicle communication. The results show that the designed CACC platooning controller exhibits favorable performance in highway driving scenarios. The platooning vehicles display excellent car-following behavior with high accuracy, driving comfort and safety. [ABSTRACT FROM AUTHOR]

A hierarchical model predictive control (MPC) approach is developed for energy management of aircraft electro-thermal systems. High-power electrical systems on board modern and future aircraft perform a variety of mission- and flight-critical tasks, while thermal management systems actively cool these electronics to satisfy component-specific temperature constraints, ensuring safe and reliable operation. In this paper, coordination of these electrical and thermal systems is performed using a hierarchical control approach that decomposes the multi-energy domain, constrained optimization problem into smaller, more computationally efficient problems that can be solved in real-time. A hardware-in-the-loop (HIL) experimental testbed is used to evaluate the proposed hierarchical MPC in comparison to a baseline controller for a scaled, laboratory representation of an aircraft electro-thermal system. Experimental results demonstrate that the proposed approach outperforms the baseline controller across a range of electrical loading in terms of both efficient energy management and constraint satisfaction. [ABSTRACT FROM AUTHOR]

Most traditional methods for solving the kinematic control problem of redundant manipulators are designed from a signal processing perspective. However, such a perspective may make the resultant design difficult for practitioners to understand. If the problem is addressed from an energy perspective, the resultant design may be more comprehensive, because energy is a universal concept and can be used to describe complex large-scale industrial systems. Passivity is a property of engineering systems, which is characterized through energy transformation. In this paper, a passivity-based approach is proposed for the kinematic control of redundant manipulators, where the joint velocity limit of manipulators is also considered. The performance of the approach is theoretically guaranteed. In addition, simulative examples are presented to validate the efficacy of the approach and the theoretical results. [ABSTRACT FROM AUTHOR]

In this paper, an improved linear quadratic tracking control strategy using a new receding horizon expanded state space (ESS) model is proposed. Unlike traditional state space model, the new ESS model first facilitates the combination of the system state variables and tracking errors, then a subsequent new receding horizon improved linear quadratic tracking control (RHLQTC) is designed with more degrees of freedoms for the adjustment of control parameters, which yields improved control performance compared with traditional RHLQTC. Finally, simulations on a typical reverse process are done in comparison with traditional RHLQTC in terms of both servo and regulatory performance, where results show that the proposed controller provides improved performance under both situations. [ABSTRACT FROM AUTHOR]

This paper provides a novel and better understanding of the performance potential of a nonsingleton (NS) fuzzy system over a singleton (S) fuzzy system. It is done by extending sculpting the state space works from S to NS fuzzification and demonstrating uncertainties about measurements, modeled by NS fuzzification: first, fire more rules more often, manifested by a reduction (increase) in the sizes of first-order rule partitions for those partitions associated with the firing of a smaller (larger) number of rules—the coarse sculpting of the state space; second, this may lead to an increase or decrease in the number of type-1 (T1) and interval type-2 (IT2) first-order rule partitions, which now contain rule pairs that can never occur for S fuzzification—a new rule crossover phenomenon—discovered using partition theory; and third, it may lead to a decrease, the same number, or an increase in the number of second-order rule partitions, all of which are system dependent—the fine sculpting of the state space. The authors' conjecture is that it is the additional control of the coarse sculpting of the state space, accomplished by prefiltering and the max–min (or max-product) composition, which provides an NS T1 or IT2 fuzzy system with the potential to outperform an S T1 or IT2 system when measurements are uncertain. [ABSTRACT FROM AUTHOR]

This paper presents a novel method, advisory dynamic programming (AD-DP), for power management of a fuel cell hybrid vehicle (FCHEV). The presented method embraces a new understanding of vehicle states as driver-dependent and -independent states in time domain to define a suitable state space to be used for dynamic programming. Driver-dependent states are defined in terms of multiple characteristic parameters for vehicle speed and power demand, corporately. Driver-independent states are defined in terms of discrete values for supercapacitor's state of charge $SoC_{sc}$. Transition costs between all states in the state space are calculated offline and tabulated in look-up tables for online implementation. A state predictive model is developed based on the transition statistics of driver-dependent states for a suitable number of driving cycles. Backward calculation of the total transition cost for the predicted horizon in state space is used to define optimal power split strategy for the powertrain. The formulation of optimal control problem, in terms of situation-based solutions related to vehicle states, enables a significant reduction of computational steps and hence addresses the main challenge of real-time applications. The algorithm is adapted in terms of optimization horizon and number of discrete states for DIS to suit the real-time application. Experimental application of AD-DP, using an emulation test-rig, is conducted over different driving cycles. The obtained results reveal an improvement in energy efficiency up to 29% compared to the adaptive rule-based method. The contribution of this paper can be identified as: first, development of a corporate definition for vehicle states, that can be further implemented in optimization-based power management methods. Second, the formulation of an adaptive DP that requires lower computational steps and hence suits real-time applications in hybrid electric vehicles. [ABSTRACT FROM AUTHOR]

Temperature effect inversion (TEI) phenomenon in ultralow power (ULP) very large scale integration circuits has been identified as an important effect by both academia and industry. Although a number of ULP methods that attempt to exploit the TEI phenomenon have been proposed, the small size of the design exploration space when applying these methods to ULP circuits hinders them from achieving their full potential. This is mainly due to the limited granularity of the supply voltage level control. Starting with an intuition that the body biasing (BB) technique is a key to overcome this limitation, this paper exploits the BB technique along with the TEI-aware voltage scaling (TEI-VS) method and TEI-aware frequency scaling (TEI-FS) method, so as to substantially increase the design spaces of these methods. Techniques for optimally combining the BB technique with TEI-VS and TEI-FS are introduced. Simulation results with the latest commercial CMOS process technologies for ULP designs demonstrate the effectiveness of the proposed methodology. [ABSTRACT FROM AUTHOR]

Tolerance is an essential part of design and manufacturing. It plays a key role in product quality and manufacturing costs. Understanding and controlling production variations on key geometric features can provide firms with a competitive edge. A model to link production variation to tolerance is highly desirable but difficult to build, especially for deformable parts with complex surfaces. Inspired by an innovative idea of volumetric space envelope (constructed from a base parametric curve), this paper proposes a novel spatial tolerance model. In this proposal, a volumetric envelope is superimposed onto the target manufacturing part, whose deformation and deviation (during manufacturing or assembly) are viewed as spatial variation, and this variation is modeled and linked to movements of envelope’s control points. This unique model design bypasses direct modeling of complex intrapart interactions, which is nonlinear in general and a major source of inaccuracy and low efficiency of many existing methods. The adopted indirect modeling brings many benefits. It can handle complex shapes and surfaces, and is able to take into account form errors. Also, it is capable of modeling both global and local variations observed in many practical cases. The new method is illustrated and verified through an example on a deformable vehicle door hinge plate. The proposed model shows application potential in every major stage of production, and makes possible to build a coherent cross-production life-cycle tolerancing framework from the early stage design, to manufacturing, to postproduction quality inspection. Note to Practitioners—Tolerance is a matter of everyday life to manufacturing and assembly engineers and designers. It directly impacts the product quality and production costs. Driven by the client’s ever-increasing variety needs, more and more deformable parts with complex surfaces have been entering into production in the past decade. However, existing degree of freedom (DOF) concept-based models, surrounding the idea of six DOFs of a rigid body, have difficulty in handling. While the decomposed type of models may help provide useful insight into the geometric variation, they are dependent on reliable measurement data, which may not be readily available. Motivated by the idea of deforming a manufacturing part indirectly through reshaping its surrounding (purposely designed) parametric space envelope (PSE), this paper proposes a novel spatial model for tolerancing. The proposed model is intuitive and is able to provide insight into geometric variations by visualizing them. Due to its novel model design, the proposed method can handle some fairly complex parts and surfaces (be it parametric or implicit) and is able to take into account form errors. The base curve to construct the PSE can be a Bezier curve, which is commonly available in computer-aided design/computer-aided manufacturing systems and is familiar to many practitioners (designers and engineers). This could substantially lower the application hurdle and open up a potentially wide application for the proposed method. [ABSTRACT FROM AUTHOR]

A method to reduce the dynamic order of linear parameter-varying (LPV) systems in grid representation is developed in this paper. It consists of an oblique projection and is novel in its use of a parameter-varying nullspace to define the direction of this projection. Parameter-varying state transformations in general lead to parameter rate dependence in the model. The proposed projection avoids this dependence and maintains a consistent state space basis for the reduced-order system. This extension of the projection framework lends itself very naturally to balanced truncation and related approaches that employ Gramian-based information to quantify the importance of subspaces. The proposed method is first compared to LPV balancing and truncation on a numerical example and then used to approximate two LPV systems: the longitudinal dynamics model of an aeroservoelastic unmanned aerial vehicle and the far wake model of a wind turbine. [ABSTRACT FROM AUTHOR]

A parity space approach to monitoring and fault detection and identification of systems in nuclear power plants (NPPs) can be beneficial. However, if the number of fault classes exceeds the total independent residual signatures, the parity space method needs to be further enhanced to achieve the optimal fault classification. This situation happens frequently in NPP applications, where the safety and reliability are paramount. A possible enhancement proposed in this paper is to combine Fisher discriminant analysis with the parity space method to maximize the scatter among different fault classes, while minimizing the scatter within each class. Under identical conditions, the proposed technique can achieve optimal separation among different fault classes. Design, real-time implementation, and experimental evaluation of the proposed method are detailed in this paper. The implemented system has been validated on the Nuclear Power Control Test Facility to demonstrate the feasibility. The test results have revealed many salient features of the proposed method with potential applications in NPPs. [ABSTRACT FROM AUTHOR]

This paper explicitly addresses the multi-modal traffic signal control problem in the shared space street (SSS), where there are multiple travel modes (e.g. passenger cars, buses, and light rails) competing for their spaces in the same lane. SSS widely exists in central business districts where the road space is limited and the multi-modal travel demand is high. An optimization framework with a multi-modal cell transmission model (M-CTM) is developed to model the multi-modal traffic in the network. Also, this study models the passenger’s choice of choosing among different travel modes based on travel costs. Regarding multi-modal signal coordination, a cycle-based traffic signal plan selection model is developed to choose the best offline optimized signal plan to minimize the total travel cost of all three modes. Therefore, the computation burden is significantly reduced in the optimization model. Moreover, a particle swarm optimization (PSO) method is implemented to solve the proposed optimization model. A case study in downtown Buffalo validates the proposed model with microscopic traffic simulation VISSIM. [ABSTRACT FROM AUTHOR]

Knowledge transfer is a promising concept to achieve real-time decision-making for autonomous vehicles. This paper constructs a transfer deep reinforcement learning (RL) framework to transform the driving tasks in the intersection environments. The driving missions at the unsignalized intersection are cast into a left turn, right turn, and running straight for automated vehicles. The goal of the autonomous ego vehicle (AEV) is to drive through the intersection situation efficiently and safely. This objective promotes the studied vehicle to increase its speed and avoid crashing other vehicles. The decision-making policy learned from one driving task is transferred through three transfer rules in another driving mission and evaluated. Simulation results reveal that the decision-making strategies related to similar tasks are transferable and have a high success rate. It indicates that the presented control framework could reduce time consumption and realize online implementation. Therefore, the transfer RL concept is helpful for establishing the real-time decision-making policy for autonomous vehicles. [ABSTRACT FROM AUTHOR]

The use of electromagnetic launch (EML) technology in the future launching mode is an inevitable trend. Based on the analysis of the common characters of EML technology, this paper presents the status of three technological branches such as EM aircraft launch, EM rail gun, EM thrust launch, and discusses some concrete problems and related research achievements in detail. Furthermore, clues and important points proposed in this paper will provide valuable reference for developing EML technology. [ABSTRACT FROM PUBLISHER]

In this paper, a novel robust fault detection (FD) approach called parity space vector machine (PSVM) is proposed for linear discrete-time systems. Aiming to achieve a tradeoff between false alarm rate (FAR) and FD rate (FDR) simultaneously, we focus our study on an integrated design of parity space-based FD in the context of residual generation and residual evaluation. Without a prior knowledge of the distribution of the unknown inputs, we propose to construct a PSVM model and formulate the underlying FD problem as a distribution-free Bayes optimal classifier, where the FAR and FDR indicate the worst-case classification accuracies of future residuals for the fault free case and faulty case. Then a bank of parity space vectors and corresponding thresholds can be designed integratedly by applying the techniques of the minimum error minimax probability machine and, at the same time, an optimal tradeoff between FAR and FDR is achieved. Finally, the effectiveness of the proposed approach is demonstrated on a longitudinal control system of unmanned aerial vehicle and further comparison with a traditional parity space-based FD is also addressed. [ABSTRACT FROM AUTHOR]

In this paper, the modeling and control problem of an overhead crane equipped with a three-dimensional (3-D) variable length flexible cable is discussed. In order to achieve high control performance, a partial differential equation (PDE) model is deduced with exactly preserving high frequency modes of the original system. For the sake of dealing with the effect of input amplitude and rate constraints, a boundary control scheme is carried out to drive the payload to a desired position with the 3-D vibration reduction of the variable length cable by applying the backstepping technology. The exponential stability of the closed-loop system is demonstrated based on the Lyapunov’s direct method. The simulation results verify the effectiveness of the proposed control strategy. [ABSTRACT FROM AUTHOR]

This article presents an optimal model predictive flux control (MPFC) for a two-level inverter fed induction motor. Integrating discrete SVM into FCS-MPFC enhances the performance of the IM drive. However, conventional DSVM-MPFC requires to enumerate and evaluate a higher number of virtual vectors in the prediction loop. In this paper, a high-efficient and low complexity voltage selection method is proposed to reduce the number of candidate voltage vectors from 38 to 15 without any suboptimality. Both steady-state and transient performances of the proposed method remain the same as the 38-vector based conventional DSVM-MPFC, producing the same cost-function values in all operating conditions. Furthermore, an online switching frequency reduction technique is proposed to achieve a minimum commutation per inverter vector change within each sampling cycle and between adjacent cycles. By appropriately arranging the sequence of real voltage vectors in each sampling cycle, a lower average switching frequency is achieved. The proposed switching frequency reduction method decreases the switching losses without compromising the performance of DSVM-MPFC as only the applied sequences of the real voltage vectors are optimized. Experimental studies are conducted to verify the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The more electric aircraft concept aims to improve the fuel consumption, the weight, and both the maintenance and operating costs of the aircraft, by promoting the use of electric power in actuation systems. According to this scenario, electromechanical actuators for flight control systems represent an important technology in next generation aircraft. This paper presents a linear-geared electromechanical actuator for secondary flight control systems, where the safety and availability requirements are fulfilled by replicating the electric drive acting on the drivetrain. Indeed, the architecture considered consists of two power converters feeding as many electrical machines coupled to the same mechanical system. The design of both the permanent magnet synchronous machine and the power converter are addressed. Preliminary results on the electric drive prototype are also provided and compared to the design requirements. Finally, the electromechanical actuator performance at system-level is evaluated in Dymola environment, analyzing different operating modes. [ABSTRACT FROM AUTHOR]

This paper deals with the innovative “variable-voltage” bus concept for future more-electric aircraft platforms. Using new functionalities and opportunities offered by innovative sources actively controlled by power electronics, there is an opportunity for a significant increase of their output power (within powertrain capabilities, machine thermal limits, and power converter maximum current) to supply increased load demands under certain conditions. This paper investigates applications of this concept to satisfy power demands for the wing ice-protection system for business jet. The study includes a detailed study into the control challenges of the proposed approach and suggests corresponding theoretical solutions. Furthermore, the controller design aspects are explored with considerations to achieve stable operation. The concept is tested in simulation environment and validated through experimental studies. This paper might be of interest for the researchers and engineers focused on innovative solutions for future aircraft platforms. [ABSTRACT FROM AUTHOR]