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Considering the load uncertainty and unmodeled dynamics in multicylinder hydraulic systems, this paper proposes a balance control algorithm based on safe reinforcement learning to release the restrictions of classical model-based control methods that depend on fixed gain. In this paper, the hydraulic press is controlled by a trained agent that directly maps the system states to control commands in an end-to-end manner. By introducing an action modifier into the algorithm, the system states are kept within security constraints from the beginning of training, making safe exploration possible. Furthermore, a normalized exponential reward function has been proposed. Compared with a quadratic reward function, the precision is greatly improved under the same training steps. The experiment shows that our algorithm can achieve high precision and fast balance for multicylinder hydraulic presses while being highly robust. To the best of our knowledge, this research is the first to attempt the application of a reinforcement learning algorithm to multi-execution units of hydraulic systems. [ABSTRACT FROM AUTHOR]

This paper introduces a novel approach for designing estimators to achieve consensus in uncertain multi-agent systems (MAS), even when various fault conditions are present and communication is assumed to be undirected and connected. The method includes an adaptive fault detection technique to detect faults and a unique adaptation in the unscented Kalman filter (UKF) to adjust noise covariance matrices and reconstruct uncertain states in the MAS is proposed in the framework of Q-learning. Additionally, it involves training neural network internal parameters using previous measurements. A Chebyshev neural network (CNN) is employed to model the uncertain plant, and a hyperbolic tangent-based robust control term is used to mitigate neural network approximation errors. This novel approach is known as reinforced UKF (RUKF). The paper also discusses the asymptotic stability of the proposed method and presents numerical simulations to demonstrate its effectiveness with reduced computational load. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of integral sliding mode disturbance observer (ISDOB)-based preview repetitive control (PRC) for a class of nonlinear systems subject to external disturbances and nonlinearity. First, an ISDOB-based PRC law is presented to estimate and compensate the exogenous disturbances and nonlinearity. Next, by using a new equality constraint, a continuous-discrete two-dimensional (2D) system is established. Then, the stability of the combined controller-observer system is derived on the basis of a Lyapunov analysis, 2D system theory, and the singular-value-decomposition technique. By solving an linear matrix inequality (LMI), the gains of the controller and state observer can be obtained. Finally, two numerical examples are provided to illustrate the effectiveness and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

Recently, a class of adaptive schemes has been developed for rejecting sinusoidal output disturbances with unknown frequencies, phases, and amplitudes. These adaptive schemes require an accurate model of the affected system's dynamics to function, and the controller performance degrades as the modeling error increases. While linear time-invariant (LTI) dynamics can adequately model the dynamics of some systems, a time-varying model of the system dynamics is more appropriate in certain scenarios. This paper proposes a robust adaptive scheme for rejecting disturbances that affect the output of a system described by a known linear time-varying model. The disturbances are described by an unknown number of sinusoidal terms with unknown time-varying frequencies. Analysis is done in ideal case where the disturbance frequencies are perfectly known to provide conditions guaranteeing the existence of a controller and a corresponding set of parameters which reject the disturbance. Analysis is then done in the more realistic adaptive scenario where the disturbance frequencies are unknown, and a robust adaptive scheme for selecting controller parameters which reject the disturbance is proposed. Performance of the controller with respect to the speed of variation in the plant model parameters is assessed via simulation. [ABSTRACT FROM AUTHOR]

Virtual coupling of railway trains is an emerging technology that has the potential to significantly increase railway operational efficiency by reducing the train following distance from absolute braking distances to relative braking distances. Current research in this topic is mainly focused on passenger trains and uses distance-based headways. This paper studied virtual coupling for heavy haul freight trains and demonstrated that the distance headway scheme was challenging and sometimes impractical for heavy haul trains to achieve virtual coupling. A time-based headway scheme was then proposed to set the follower train to be a certain time behind the schedule of the leader train rather than a distance headway. The time-based headway required the follower train to reproduce the leader train's operational status at the same track location. This also allowed the follower train to copy any optimized train driving strategies from the leader train. Demonstrative simulations were carried out without the consideration of communication errors and train localization errors. The results show that a conventional distance headway simulation had maximum distance and speed errors of 716 m (36%, reference 2 km) and 24 km/h (66%, reference 36 km/h), respectively. A time-based headway simulation reduced the maximum distance and speed errors to 0.07 m (0%, reference 2 km) and 0.1 km/h (9%, reference 1.18 km/h), respectively. [ABSTRACT FROM AUTHOR]

In many nonlinear systems, the observability of the system is dependent on its state and control input. Thus, incorporating observability into a control scheme can enhance an observer's ability to recover accurate estimates of unmeasured states, minimize estimation error, and ultimately, allow the original control objective to be achieved. The accommodation of observability, however, may conflict with the original control goal at times. In this paper, we propose the use of control barrier functions (CBFs) to enforce observability and thereby facilitate the convergence of the state estimate to the true state while accommodating the original control objectives. Motivated by practical applications for autonomous robots operating in global positioning system-denied environments, we focus on the problem of target tracking for a unicycle model when only the distance to the target is measured. The proposed approach is compared in simulation with a model predictive control (MPC) approach that treats an observability-related metric as part of the cost function, where several different options for the observability metric are explored. It is found that the CBF-based approach achieves control and estimation performance that is comparable to that of the MPC approach, but with significantly less computational complexity. These findings are further experimentally verified in range-based target tracking with a swimming robotic fish. [ABSTRACT FROM AUTHOR]

Advanced control strategies for delivering heat to users in a district heating network have the potential to improve performance and reduce wasted energy. To enable the design of such controllers, this paper proposes an automated plant modeling framework that captures the relevant system dynamics, while being adaptable to any network configuration. Starting from the network topology and system parameters, the developed algorithm generates a state-space model of the system, relying on a graph-based technique to facilitate the combination of component models into a full network model. The accuracy of the approach is validated against experimental data collected from a laboratory-scale district heating network. The verification shows an acceptable average normalized root-mean-square error of 0.39 in the mass flow rates delivered to the buildings, and 0.15 in the network return temperature. Furthermore, the ability of the proposed modeling technique to rapidly generate models characterizing different network configurations is demonstrated through its application to topology optimization. The optimal design, obtained via a branch and bound algorithm, reduces network heat losses by 15% as compared to the conventional length-minimized topology. [ABSTRACT FROM AUTHOR]

Data-driven fault diagnosis method is widely used in the field of engine health management, which uses engine sensor data as input and engine faulty components as output for component-level fault diagnosis of the engine. The application premise of the general data-driven fault diagnosis method is that all data come from the same working conditions, that is, they belong to the same distribution. However, this assumption is not valid in the actual engine fault diagnosis, because the engine state will change with the increase of running time. In the meantime, collecting engine data is usually expensive, time-consuming, and laborious. To solve these problems, extreme learning machine (ELM)-based two transfer learning methods for fault diagnosis of turboshaft engines are proposed in this paper. One is joint solving ELM (JSELM), which regards the information of the target domain and source domain as similar and different parts, respectively, and knowledge is extracted from them at the same time. The other is model transfer-based ELM (MTELM), which uses the idea of pretraining. First, a general ELM classifier is trained with the source domain data and then fine-tuned with the target domain data. Both methods have a good real-time performance as the traditional ELM. When there are a few data in the target domain, they achieve much better classification accuracy than traditional ELM. Finally, experiments are carried out with turboshaft engine simulation data. The results show that both methods are effective, especially MTELM, which has better classification accuracy. [ABSTRACT FROM AUTHOR]

The predictor feedback has been demonstrated to be quite effective in large delay compensation. However, few researches in the field of predictor feedback for large delays focused on output feedback control (OFC). This paper develops the previous work to design high-gain-observer-based predictive output feedback for nonlinear systems with large delays. Two methods are employed for large delay compensation: the backstepping-based partial differential equation (PDE) method and the reduction-based ordinary differential equation (ODE) method. It appears that, for continuous-time control, the first method leads to simpler linear matrix inequality (LMI) conditions and deal with larger delays, whereas the second method is easily exploited for sampled-data implementation under continuous-time measurement. Lyapunov-Krasovskii method is presented to guarantee the exponential stability of the nonlinear systems under predictor-based controllers. Through a simulation example of pendulum, the proposed methods are demonstrated to be efficient when the input delays are too large for the system to be stabilized without a predictor. [ABSTRACT FROM AUTHOR]

This paper considers an integrated control method combining autonomous driving and active steering. By using artificial flow guidance (AFG), the steering at all five axles on a tractor-semitrailer are synchronized for precise low-speed path tracking and improved lateral stability. AFG is a motion planning method which uses velocity vectors to guide the motion of the vehicle; here, two modification on AFG are proposed to form the integrated-AFG which improves the transient performance and generalizes the choice of the tracking points. Simulations rendered in TruckMaker show submillimeter level steady-state off-tracking for a broad speed range, with maximum deviation being 1 cm for a high-speed lane change; amplification of trailer lateral motion is also suppressed, with rearward amplification (RWA) < 1 maintained throughout. This control method is robust to changes in key parameters such as trailer mass and road friction coefficient. The methods presented provide a new foundation for implementing automated freight transportation on highways. [ABSTRACT FROM AUTHOR]

In this work, exergy management strategies (ExMSs) for hybrid electric ground vehicles (HEVs) are developed. The main advantage of using the exergetic framework is the possibility of pursuing unconventional optimization goals that are inaccessible to the standard energy management strategy (EMS). For instance, in military applications, the critical goal of preventing thermal imaging detection from adversary units does not seem achievable with the conventional EMS. On the other hand, the exergy-based framework can be adopted to reduce the vehicle thermal emissions through the minimization of exergy terms related to heat exchange. Moreover, the overall efficiency of the vehicle can be increased through the minimization of the exergy destruction, a quantity that is not quantifiable by energy-based methods. In this paper, the exergetic model of a series hybrid electric military truck and the exergetic model of the electric induction generator are developed and used to formulate and solve two novel exergy management strategies aiming to minimize genset exergy destruction and thermal emissions, respectively. The optimal solutions to the EMS and ExMSs control problems are obtained through Dynamic Programming over two driving missions. The results show that ExMS for the minimization of exergy destruction achieves similar results to the standard EMS, while the ExMS for the minimization of thermal emissions obtains significantly lower thermal emissions compared to the EMS, effectively reducing the thermal imaging detection risk. [ABSTRACT FROM AUTHOR]

This paper proposes a new condition indicator (CI) for cost-effective industrial direct current (DC)-powered solenoid valves (SVs). First, a literature review of the failure modes for SVs is reported. The failure regarding the impeded movement of the valve plunger is considered. The proposed CI for this failure mode is computed from only some significant points of the SVs excitation current and their time instants. The proposed CI is evaluated by a large endurance campaign on a set of SVs. Then, a Remaining Useful Life (RUL) estimation algorithm is proposed and evaluated on the experimental data. Experimental results show the goodness of the proposed CI for RUL estimation of cost-effective industrial DC SVs. [ABSTRACT FROM AUTHOR]

This paper develops an algorithm for synthesizing negative imaginary (NI) systems with a specified H∞-norm bound by exploiting the convex relaxation technique in the static output-feedback (SOF) control design framework. This scheme improves the robustness objective since the present framework can handle both NI and H∞-norm-bounded unstructured uncertainty of the system. The non-strict inequality involved in the NI system description that creates a major difficulty in the SOF control design, can efficiently be tackled by the developed convex relaxation-based iterative algorithm. The advantages of the algorithm are shown using several examples and the results are compared with the existing works. [ABSTRACT FROM AUTHOR]

Thermal energy storage (TES) devices use phase change materials (PCMs) to store and release thermal energy. Control-oriented models are needed to predict the behavior of TES devices and experimental validation is necessary to demonstrate the predictive capabilities of these models. This paper presents an experimental validation of a switched moving boundary (MB) approach for modeling TES devices, where the dynamics of the device are captured with fewer states than traditional models. A graph-based modeling approach is used to model heat flow, while the moving boundary captures the time-varying liquid and solid regions of the TES. The model uses a finite state machine (FSM) to switch between four modes of operation based on the state-of-charge (SOC) of the TES. Results show that the switched MB approach has similar accuracy and lower computational cost compared to traditional modeling approaches when predicting the SOC of an experimental TES device. [ABSTRACT FROM AUTHOR]

For discrete time nonlinear networked control systems, a novel self-triggered adaptive model predictive control (MPC) strategy is developed. Different from the existing self-triggered MPC methods that determine the triggering instants based on the difference between the optimal and real states at one single instant, the proposed approach updates the MPC system according to the differential form of the state error of two consecutive sampling moments to effectively reduce the computation and communication burden while maintaining the ideal control performance. In addition, this paper introduces a new adaptive prediction horizon mechanism to the self-triggered MPC, so that the amplitude of prediction horizon contraction is sufficiently large to further reduce the computational burden of the MPC method. Finally, the recursive feasibility and robust stability of this proposed strategy are proved strictly by theoretical analysis, and the simulation comparison results are shown to verify the proposed framework. [ABSTRACT FROM AUTHOR]

In this paper, an improved model-free adaptive control strategy is proposed for the trajectory tracking problem of the wheeled mobile robot (WMR) with time-delay and bounded disturbance. Firstly, the original nonlinear time delay system is transformed into a data model by applying the full-form dynamic linearization method (FFDL). Secondly, the discrete-time extended state observer (DESO) is applied to estimate the unknown residual nonlinear time-varying term. A full-form dynamic linearization model-free adaptive control scheme based on discrete-time extended state observer (DESO-based FFDL MFAC) is proposed. In addition, a full-form dynamic linearization event-triggered model-free adaptive control based on discrete-time extended state observer (DESO-based FFDL ET-MFAC) is established by designing an event-triggering condition to assure Lyapunov stability. The control input signal is updated only if the system indicator meets the provided event-triggering condition; otherwise, the control input remains unchanged which can address limited communication bandwidth effectively. Finally, the effectiveness of the proposed method is verified by simulation. [ABSTRACT FROM AUTHOR]

This paper presents the exploration and optimization of a hybridized opposed piston (OP) engine. In this work, the exhaust crankshaft lead (ECL) is introduced as a controllable parameter in the hybridized OP engine enabled by eliminating the conventional geartrain linking the two crankshafts of an OP engine. This allows for variation in the effective compression and expansion ratio of the engine, along with scavenging performance. This novel control actuator as well as the adjustable speed and load setpoint in a series hybrid OP engine powertrain architecture necessitates an intensive calibration effort to realize any possible efficiency improvements. However, the OP engine within this series hybrid powertrain does not need to operate in highly transient conditions, but rather its operating point is fixed or slowly varying. This property permits the use of online calibration techniques. After manually sweeping speed and ECL values at two power setpoints, the use of an extremum-seeking type intercycle optimization algorithm to optimize the operating setpoint is validated, showing that near optimal speed and ECL setpoints can be selected despite the relatively flat operating map of the OP engine. [ABSTRACT FROM AUTHOR]

This paper presents a technique for tracking the high-speed motion of a multilink system using inertial measurement units (IMUs) in a new sensor arrangement, an approach which is referred to as dynamic measurements fusion. The proposed technique incorporates accelerometers with traditional gyroscopes to measure joint angular velocities, while joint angles are measured with magnetometers. Comparative studies with conventional techniques show that the proposed technique tracks the motion of a multilink system accurately at both low (0.5 m/s) and high (5 m/s) speeds. Further analysis with different levels of measurement noise demonstrates the robustness of the proposed technique and its overall capability for tracking joint angular velocities and angles. [ABSTRACT FROM AUTHOR]

Lumped parameter modeling is a consolidated technique for analyzing the fluid dynamic behavior of positive displacement machines, owing to their computational swiftness and the ease of integrating other physical domains affecting the operation of the machine. With some very limited exceptions, this modeling technique typically neglects fluid inertia and momentum effects. This paper proposes an approach to study the effects of fluid inertia affecting the pressurization and depressurization of the tooth space volumes of an external gear pump. The approach is based on considering the fluid inertia in the pressurization grooves and inside the control volumes with a peculiar subdivision. Further, frequency-dependent friction is also modeled to provide realistic damping of the fluid inside these channels. Validation of the model has been performed by comparing the lumped parameter model with a full three-dimensional Navier-Stokes solver. The quantities compared, such as tooth space volume pressures and outlet volumetric flow rate, show a good match between the two approaches for varying operating speeds. A comparison with the experiments supports the modeling approach as well. The paper finally, also discusses which operating conditions and geometries play a significant role that governs the necessity to model such fluid inertia effects in the first place. [ABSTRACT FROM AUTHOR]

In this paper, we propose a globally stable adaptive controller for the human shank motion tracking problem that appears in neuromuscular electrical stimulation systems. The control problem is complicated by the fact that the mathematical model of the human shank dynamics is nonlinear and the parameters enter in a nonlinear and nonseparable form. To solve the problem, we first derive a nonlinearly parameterized regressor equation (NLPRE) that is used with a new parameter estimator specifically tailored for this NLPRE. This estimator is then combined with a classical feedback linearizing controller to ensure the tracking objective is globally achieved. A further contribution of the paper is the proof that parameter convergence, and consequent global tracking, is guaranteed with an extremely weak interval excitation requirement. A simulation study comparing the proposed adaptive controller with existing ones in the literature shows comparable human shank tracking performance but with fewer parameter estimates and without requiring knowledge of bounds for the unknown parameters. [ABSTRACT FROM AUTHOR]

The paper proposes the digital redesign technique called plant-input-mapping (PIM) method for a feedback system described in the state-space form. The PIM method, which was originally presented in the transfer function form, focuses on the plant input signal via the plant input transfer function (PITF) and discretizes it so as to satisfy the control zero principle in the resulting discrete-time closed-loop system, which leads to guaranteeing the closed-loop stability for any nonpathological sampling interval. In accordance with this approach, the proposed PIM method focuses on the control zeros included in the plant input signal. The paper proves that the matched-pole-zero discrete-time model of the plant input state-equation (PISE) satisfies the control zero principle with the step-invariant model of the plant. Then, when the matched-pole-zero model is set as the target of model matching, the parameters of the state-space PIM controller employing the observer-based dynamic state-feedback can systematically be determined from the underlying continuous-time closed-loop system with guaranteed stability. This discretization process can immediately be applied to a state-feedback system and a class of multi-input multi-output systems without any modification, which cannot be discretized by the conventional PIM methods. The discretization performance of the proposed PIM method is evaluated through illustrative examples with comparable digital redesign methods, which reveal that the proposed method performs a good reproduction of the characteristics of the underlying closed-loop system. [ABSTRACT FROM AUTHOR]

This paper examines the problem of simultaneously optimizing the spooling and cross-current flight trajectory of a tethered marine hydrokinetic kite using an analytic solution of its inverse dynamics. Tethered kites hold considerable promise for energy production, especially when undergoing cross-current motion. The novelty of this work lies in the use of an analytic solution of the inverse dynamics of the kite to solve the trajectory optimization problem. The term "inverse dynamics" refer to the process of obtaining an exact solution for the actuator inputs from the position, velocity, and acceleration of the kite. While the literature on tethered kites explores trajectory optimization in great detail, most of the work exploits the forward dynamics of the kite, and does not simultaneously optimize the spooling motion and cross-current trajectory. This paper formulates the co-optimization of the kite spooling and cross-current trajectory using a three degrees-of-freedom kite model, paired with an inelastic tether model. The analytic solution of the inverse dynamics is solved in terms of the roots of a fourth-order polynomial in terms of the angle of attack. A simulation study validates the optimization approach and shows that the kite is able to achieve significant energy production. [ABSTRACT FROM AUTHOR]

Conventional emulated damping, in which velocity feedback is employed in combination with motor current control to emulate damping, is used in a number of mechatronic applications. As known in the field, although such damping should be strictly passive, several implementation factors render this emulation nonpassive and subject to instability, especially with increasing damping ratios. This paper describes an alternative implementation of emulated damping that employs passive motor control, which does not draw energy from a battery or any other power source. The method is specifically described in the context of a brushless direct-current (BLDC) motor; an unique controller architecture is used along with a particular MOSFET switching scheme which employs only a subset of the standard BLDC motor driver and does not require electronic commutation. This paper employs analytical and experimental means to compare damping control with the described passive motor approach, relative to using a conventional BLDC motor control approach. Stability considerations for each scheme are discussed. Benchtop testing demonstrates the advantage of the passive control scheme in terms of providing smooth behavior, enhancing control robustness, preventing energy leaks, and providing accurate behavior. The special cases of unilateral and asymmetric behaviors (i.e., different damping command for each direction of rotation) are also considered. [ABSTRACT FROM AUTHOR]

Vehicle sideslip and tyre/road friction are crucial variables for advanced vehicle stability control systems. Estimation is required since direct measurement through sensors is costly and unreliable. In this paper, we develop and validate a sideslip estimator robust to unknown road grip conditions. Particularly, the paper addresses the problem of rapid tyre/road friction adaptation when sudden road condition variations happen. The algorithm is based on a hybrid kinematic-dynamic closed-loop observer augmented with a tyre/road friction classifier that reinitializes the states of the estimator when a change of friction is detected. Extensive experiments on a four wheel drive electric vehicle carried out on different roads quantitatively validate the approach. The architecture guarantees accurate estimation on dry and wet asphalt and snow terrain with a maximum sideslip estimation error lower than 1.5 deg. The classifier correctly recognizes 87% of the friction changes; wrongly classifies 2% of the friction changes while it is unable to detect the change in 11% of the cases. The missed detections are due to the fact that the algorithm requires a certain level of vehicle excitation to detect a change of friction. The average classification time is 1.6 s. The tests also indicate the advantages of the friction classifiers on the sideslip estimation error. [ABSTRACT FROM AUTHOR]

This paper proposes an analytical framework describing how initial capacity and resistance variability in parallel-connected battery cells may inflict additional variability or reduce variability while the cells age. We derive closed-form equations for current and SOC imbalance dynamics within a charge or discharge cycle. These dynamics are represented by a first-order equivalent circuit model and validated against experimental data. To demonstrate how current and state of charge (SOC) imbalance leads to cell degradation, we developed a successive update scheme in which the intercycle imbalance dynamics update the intracycle degradation dynamics, and vice versa. Using this framework, we demonstrate that current imbalance can cause convergent degradation trajectories, consistent with previous reports. However, we also demonstrate that different degradation assumptions, such as those associated with SOC imbalance, may cause divergent degradation. We finally highlight the role of different cell chemistries, including different OCV function nonlinearities, on system behavior, and derive analytical bounds on the SOC imbalance using Lyapunov analysis. [ABSTRACT FROM AUTHOR]

Closed-loop control of commercial building heating, ventilating, and air conditioning (HVAC) for demand response requires measurements used as feedback to the controllers. Demand response effectiveness is usually measured as a power deviation from baseline, but the building automation system (BAS) does not usually collect power measurements, and whole-building electric meters typically measure power at intervals of 15 min, which may be too slow for some types of demand response. Demand response strategies are sometimes focused on components of building HVAC systems, e.g., the response of supply/return fans to temperature set-point changes, but these components are usually not submetered. Fan power can be estimated from physics-based models leveraging BAS data, e.g., airflow measurements; but our ability to effectively close the loop on these estimates is not clear. In this paper, we introduce a massive dataset that contains both submetered fan power data and BAS data for several building HVAC systems during typical operation and demand response events. Through a case study we show that models leveraging BAS data alone do not provide accurate estimates of fan power during event transients, making it unlikely that closed-loop control of commercial building HVAC components for demand response would be effective using BAS data alone. This demonstrates the value of submetering HVAC components. More broadly, our dataset will enable future research bridging the gap between building control and power systems research. [ABSTRACT FROM AUTHOR]

Adding battery locomotives to traditional diesel locomotives to form a hybrid consist has the potential to reduce fuel consumption and emissions in freight rail operations. This paper provides route-based estimates of diesel (hpd/ton) and battery (hpb/ton) power requirements and diesel (gal/ton) and battery (hpbhr/ton) energy requirements for hybrid consists. Given a route-specific power profile, the power split between the battery pack and the diesel engine is optimized to minimize fuel consumption by running the diesel engine at maximum efficiency during the entire route. Simulations for 200-mile round trips between Chicago and Harrisburg show that the diesel power is maximum in low-gradient regions at 1.05 (hpd/ton) and battery power is maximum in hilly regions at 2 (hpb/ton) for discharge and 6.5 (hpb/ton) for charge. Maximum fuel efficiency gains of approximately 60% are seen in the mountains, where 2.09 (hpbhr/ton) of battery energy is needed. Minimum fuel economy gains are observed in flat regions, with 20% fuel consumption reduction. With battery current and voltage limits, the battery pack size increases, and fuel savings decrease. [ABSTRACT FROM AUTHOR]

Boiler-turbine unit is a highly nonlinear, coupled, and ill-conditioned system. Moreover, the presence of physical constraints, such as actuator magnitude and rate limits, makes the system difficult to control. This paper employs a fixed multivariable proportional-integral (PI) controller of the I-P structure for robust linear quadratic (LQ) compensation of a nonlinear boiler-turbine benchmark. In order to ensure that a single PI controller works for the whole operating region of this nonlinear system, the linearized model of the system is represented as a norm-bounded, time-varying uncertain system. The ranges of the uncertain parameters of this linearized model are determined from different operating points of the nonlinear system. To design the PI controller for the uncertain system, first, it is transformed into a state feedback design for an augmented uncertain system and then the state feedback gains satisfying some LQ performance limit are computed by solving a linear matrix inequality (LMI) problem. As the uncertainty in the feedforward matrix of the linearized model cannot be considered in the above design process, an LMI-based method is developed to check if the designed PI controller performance in sense is close to the one if the neglected uncertainty is included. The performance of the controller is tested on the nonlinear boiler-turbine unit under several operating conditions and physical constraints. Comparisons are also made with some existing PI controllers, to show the superiority of the proposed robust PI controller. [ABSTRACT FROM AUTHOR]

With their inherent compliance, flexible manipulator systems (FMSs) are increasingly finding new applications, such as robotic surgeries requiring safe interaction with the environment. While force-controlled FMSs are desirable, an added passive, compliant element can enhance safety while preserving performance. This paper explores the effects of added compliance on tip-actuated FMSs. A detailed analysis compares the impacts of additional compliance, and comparisons are made with a baseline no-compliance case. It is observed that the series compliance provides added safety but with some compromise on performance and stability. In contrast, parallel compliance improves stability and performance without compromising safety in interaction. While similar insights have been well-understood in the context of rigid-link robots, it is interesting to observe that additional series compliance in tip-actuated flexible manipulators is undesirable, unlike for rigid-link robots. [ABSTRACT FROM AUTHOR]

Due to the breadth and complexity of spatial motion, and the nonlinear coupling caused by multiple degree-of-freedom, the control difficulties of spatial cooperative multirobot system are two mainly aspects, the modeling method and the dispose of uncertainty. For modeling, the process of directly using ordinary methods such as Lagrange mechanics are very complex. For control, the external disturbances and imperceptible errors will lead to the instability of dynamic system, these factors are collectively referred to as uncertainty. In this paper, a universal modeling method based on spatial cooperative multirobot system was proposed, and an adaptive robust controller was designed to solve the control problems. Simplify the connection link into a load and divide the system into two symmetrical subsystems by disconnecting the load center. The inherent physical position and given trajectories were unified as constraints. The constraints applied on system are creatively converted into consistent standard form through the Udwadia-Kalaba (U-K) method, and the corresponding constraints can be generated easily from the Udwadia-Kalaba basic equations. In addition, a nominal term to stable the system and an adaptive term was added to the controller. The adaptive term works through an adaptive parameter, the parameter estimates the uncertainty bound, thus, only the boundedness of uncertainty should be known, and more accurate control input can be gained. Eventually, the correctness of the model and controller has been confirmed through numerical simulation. [ABSTRACT FROM AUTHOR]

The free-piston electromechanical hybrid power system (FEHS) affords the advantages of a simple construction and high thermal efficiency due to the removal of the crankshaft. However, the unrestricted trajectory of the piston linkage assembly (PLA) also gives rise to challenges for stable operation during the process of startup or operation. In order to realize the stable operation of free-piston electromechanical hybrid power system, this paper proposed a load control strategy. First, a dynamic model is established through thermodynamic and electromagnetic theory, and its effectiveness is verified by experiment and simulation. On this basis, a coupling load control model based on linear active disturbance rejection control (ADRC) is developed. The reliability of the proposed load control strategy is validated under different interference fluctuations. The simulation results demonstrate that no matter whether the interference occurs during the startup process or the operation process, the proposed control strategy exerts effective limiting function over the piston linkage assembly and maintain its stable operation. Moreover, compared with the proportion integral differential (PID) control strategy, the proposed strategy exhibits faster response times and a smoother startup process. The compression ratio fluctuation range was reduced from 0.1 to 0.001, and the control accuracy has been greatly improved. [ABSTRACT FROM AUTHOR]

The fixed-point stopping of high-speed trains in stations is generally accomplished through manual operation in China. This situation often leads to a failure to stop at fixed-point signs and causes a fluctuation in the longitudinal acceleration due to the lack of experience of the drivers. To achieve precise, stable, and automatic stopping, a distributed multiparticle precise stopping control model based on the distributed model predictive control (MPC) algorithm is developed in this paper. A two-level hierarchical control structure for the subcontroller of each vehicle is adopted to bring itself to a controlled stop. In the upper control of subcontroller, the MPC algorithm is designed in turn based on the multiparticle mechanism model of the train. In the lower control of subcontroller, the target input from the upper control is converted and distributed. The controlled object, a comprehensive numerical computing model including the spatial dynamic model of the train and its electropneumatic blending braking model, is established and controlled by the corresponding subcontroller and employed to verify the performance of the controller. The influence of the model control parameters on the stopping performance is discussed, and the optimal combination of control parameters is selected. The proposed control model using the optimization parameters is tested and verified through the comprehensive numerical computing model. The results indicate that the stopping error is 0.0075 m, which is much less than the accuracy requirements for fixed-point stopping. The computing time of each subcontroller in real-time is stable at 0.09 s. The coupler impact force between two adjacent vehicles can also be effectively inhibited and eliminated. Its control performance outperforms a proportional-integral-derivative (PID) algorithm. The proposed precise stopping model and comprehensive numerical computing model provide references for the application and algorithm optimization of automatic train operation technology in high-speed trains. [ABSTRACT FROM AUTHOR]

The best exoskeleton control strategy depends on the task, motivating the development and analysis of an exoskeleton capable of position, force, and impedance control. In this paper, we implement and compare the three controller types via sliding mode control on a custom-built pneumatic cylinder ankle exoskeleton. To evaluate each controller's performance, the exoskeleton was tested on the benchtop and with human subject experiments. With the position controller, the exoskeleton achieved an root-mean-square error (RMSE) of under 5 deg for both the benchtop and human tests. It had a bandwidth of approximately 12 rad/s. The force controller tracked sinusoidal trajectories acceptably well at low frequencies (15% of force range at 6 rad/s), with a bandwidth of approximately 24 rad/s. The error was significantly higher (RMSE of 24% of force range) for the more biologically relevant trajectory. The impedance controller demonstrated the desired spring-like behavior, with position RMSE generally under 5 deg compared to expected position. All three controllers worked equally well for benchtop and human tests. Thus, a pneumatically powered ankle exoskeleton with sliding mode control is capable of accurate position and impedance control and is capable of marginally acceptable force control. [ABSTRACT FROM AUTHOR]

In this paper, we show how autonomous vehicles performing a certain task can be controlled from one command center in order to achieve certain goals. The command center provides individual feedback gains to each vehicle in the form of one compounded feedback gain that in fact carries feedback gain information for each individual vehicle. Vehicles may be of different nature: satellites, cars, tanks, drowns, robots, unmanned aircraft, underwater vehicles, and ships. It is assumed that their dynamic is weakly coupled, namely, dynamics of each individual vehicle is weakly coupled to dynamics of all other vehicles and that they have the same control input. The weak coupling among the vehicles is represented either by physical interactions among vehicles or coordinating or communication signals. The results obtained are applicable to vehicles having different mathematical linear/linearized state space models. In the case when no interaction/coordination/communication among vehicles is present, and all feedback signals come from the command center (central controller) controller, the vehicle mathematical models can be identical. An important feature of the presented results is that different local feedback controllers can be facilitated for different vehicles using a unique global feedback controller signal designed by the command center. In this paper, we will consider cases of two and three vehicles, but the presented multistage feedback design methodology can be potentially extended to N vehicles. The presentation is done in continuous time. Similar derivations hold in the discrete-time domain. [ABSTRACT FROM AUTHOR]

Powder bed fusion (PBF) is an additive manufacturing (AM) process that builds parts in a layer-by-layer fashion out of a bed of metal powder via the selective melting action of a laser or electron beam heat source. Despite its transformational manufacturing capabilities, PBF is currently controlled in the open loop and there is significant demand to apply closed-loop process monitoring and control to the thermal management problem. This paper introduces a controls theoretic analysis of the controllability and observability of temperature states in PBF. The main contributions of the paper are proofs that certain configurations of PBF are classically controllable and observable, but that these configurations are not strongly structurally controllable and observable. These results are complemented by case studies, demonstrating the energy requirement of state estimation under various, industry relevant PBF configurations. These fundamental characterizations of controllability and observability provide a basis for realizing closed-loop PBF temperature estimation. [ABSTRACT FROM AUTHOR]

This paper presents a novel model-free adaptive sliding mode control (MFA-SMC) algorithm, which is employed to actively adjust the amplitude and frequency of spindle speed variation (SSV) for chatter suppression in turning. The SSV technique is effective for mitigating regenerative chatter, which however is not widely applied due to its poor adaptability to time-varying characteristics of machining dynamics and cutting conditions. The online adjustment of SSV parameters has been reported in previous works, whereas the proportional integral differential (PID)-type controller used cannot compensate for the interactions when abruptly changing the reference input and the parameter tuning procedure consumes much time. The proposed method integrates the model-free adaptive control (MFAC) with the global sliding mode control (GSMC). The method is data-driven and only dependent on the measured input/output data of the cutting process in this paper, which are the normalized wavelet packet entropy and SSV parameters, respectively. The bounded-input bounded-output stability and the tracking error convergence of the proposed control algorithm are guaranteed by theoretical analysis. The effectiveness of the proposed chatter suppression method is investigated with numerical simulations. Finally, experimental results demonstrate that chatter vibration under different cutting conditions can be effectively mitigated by the proposed method. [ABSTRACT FROM AUTHOR]

The paper presents a method for enabling feedback control of a mechatronic system without a dedicated controller by tailoring structural modes of the mechanical structure for loop shaping by design. Structural modes are in general unwanted effects in positioning systems and are by design typically well separated from the frequency range over which control is effective. They can, however, also be deliberately integrated to shape the resulting loop gain of the control loop, to overcome the need for a dedicated position controller and enable unity gain feedback. The method employs a structural mode with antiresonance-resonance-pattern together with a damping mechanism to obtain the required phase lead for feedback control of a low-stiffness positioning system. It is validated by an experimental setup with one degree-of-freedom (DoF), designed for a crossover frequency of 130 Hz and a phase margin of 50 deg. An adjustable electromagnetic damper is integrated to study the effects of parameter variations. The designed closed-loop controlled system achieves a bandwidth of 240 Hz, and is capable of tracking step inputs and rejecting external impacts with settling times a factor of 2-5 smaller than in the detuned case. [ABSTRACT FROM AUTHOR]

This paper proposes an offset-free Quasi-Infinite Horizon Nonlinear Model Predictive Controller (QIH-NMPC) using online parameter adaptation. In the proposed method, the adaptation law is modeled by a first order differential equation as a function of the tracking error and subsequently combined with a QIH-NMPC algorithm for online updating of the unknown parameter. The effectiveness of the proposed control scheme is demonstrated on a continuous stirred tank reactor (CSTR) and an experimental cascaded three-tank system with uncertain model parameters, structural plant/model mismatch and noisy measurements. For the purpose of comparison, the state-of-the-art online state and parameter estimators such as moving horizon estimation (MHE) and extended Kalman filter (EKF) were also incorporated into QIH-NMPC algorithm. The simulation and experimental results obtained showed the efficacy of the proposed adaptation scheme as it demonstrated a comparable performance to standard estimators (MHE and EKF) although with a lesser computational time. [ABSTRACT FROM AUTHOR]

Automated fabrication of concrete elements requires precise control of outgoing volume flow. Since measurements of the volume flow or the total extruded volume are challenging during production, a feedforward control is required, which in turn necessitates a model of the dynamics at hand. In this paper, a concrete conveyance system with two pumps is considered. These are mounted at the inlet and outlet of the hose, respectively. Based on previous work, a dynamical model with concentrated parameters is derived, which is motivated on the general distributed dynamics of two-phase flow. The model is validated by experiments, with parameters calculated by a tailored estimation approach. Based thereon, a feedforward control approach is presented and tested in simulation, exploiting the approximate flatness of the system. The flat parametrization is then used for constrained, smooth setpoint transition of the volume flow with minimal transition time. [ABSTRACT FROM AUTHOR]

Model-based decoupling control methods have been widely used in the register control of Roll-to-Roll (R2R) printing systems due to their advantage in dealing with couplings. While they achieve a certain decoupling effect, their registrations are influenced by the accuracy of the model and decoupling compensation. In this paper, a hybrid model is presented to calculate an appropriate compensation to eliminate the complex velocity-error relations in the R2R system, which consists of a general register error model and a data-based supplement model. Then, an adaptive decoupling control (ADC) method is proposed, in which the feedback gains are determined by solving a Riccati equation. The effectiveness of the hybrid model is validated by experiments, while the superior performance of the ADC is demonstrated by simulations. The results indicate that the hybrid model is more accurate than the general model and ADC method can rapidly reduce the register errors into a range of ±10μm. In addition, simulated comparisons of ADC, direct decoupling proportional differential (DDPD), and improved DDPD control methods also show that the ADC owns the highest register precision and smoothest control signal. [ABSTRACT FROM AUTHOR]

In this paper, a boundary control with barrier term is proposed for a typical three-dimensional flexible riser system by integrating backstepping technique with barrier Lyapunov functions. The proposed boundary control does not depend on the exact value of interference and can effectively restrain the vibration of the riser and constrain the angle between the riser and the platform. The proposed control has a small amount of calculation and strong robustness, and can effectively restrain the riser vibration when the environmental disturbance changes. In addition, based on the proposed boundary control method, the stability of the closed-loop system in space and time is proved by Lyapunov stability theorem when the initial conditions are satisfied. Numerical simulation shows the effectiveness and superiority of the controller. [ABSTRACT FROM AUTHOR]

Neural networks have been widely applied in system dynamics modeling. One particular type of networks, hybrid neural networks, combines a neural network model with a physical model, which can increase rate of convergence in training. However, most existing hybrid neural network methods require an explicit physical model constructed, which sometimes might not be feasible in practice or could weaken the capability of capturing complex and hidden physical phenomena. In this paper, we propose a novel approach to construct a hybrid neural network. The new method incorporates the physical information to the structure of network construction, but does not need an explicit physical model constructed. The method is then applied to modeling of bit-rock interaction in the down-hole drilling system as a case study, to demonstrate its effectiveness in modeling complex process and efficiency of convergence in training. [ABSTRACT FROM AUTHOR]

Latent thermal energy storage (TES) devices could enable advances in many thermal management applications, including peak load shifting for reducing energy demand and cost of HVAC or providing supplemental heat rejection in transient thermal management systems. However, real-time feedback control of such devices is currently limited by the absence of suitable state of charge estimation techniques, given the nonlinearities associated with phase change dynamics. In this paper, we design and experimentally validate a state-dependent Riccati equation (SDRE) filter for state of charge estimation in a phase change material (PCM)-based TES device integrated into a single-phase thermal-fluid loop. The advantage of the SDRE filter is that it does not require linearization of the nonlinear finite volume model; instead, it uses a linear parameter-varying system model which can be quickly derived using graph-based methods. We leverage graph-based methods to prove that the system model is uniformly detectable, guaranteeing that the state estimates are bounded. Using measurements from five thermocouples embedded in the PCM of the TES and two thermocouples measuring the fluid temperature at the inlet and outlet of the device, the state estimator uses a reduced-order finite volume model to determine the temperature distribution inside the PCM and in turn, the state of charge of the device. We demonstrate the state estimator in simulation and on experimental data collected from a thermal management system testbed to show that the state estimation error converges near zero and remains bounded. [ABSTRACT FROM AUTHOR]

This paper describes the design and successful implementation of a constrained model predictive controller with integral action for the control of a Twin Rotor MIMO System (TRMS). The integral action guarantees zero steady-state error in set-point tracking with robustness toward perturbations of the nominal system parameters. In addition to saturation constraints on the input variables, hard constraints are imposed on the controlled output variables, i.e., on pitch and yaw angular positions, to avoid collisions with obstacles. The model predictive controller was designed using a high-fidelity nonlinear model of the TRMS developed in previous work. As an intermediate step, exact linearized models of the TRMS are obtained and their closed-form expressions are reported. The controller was tested experimentally, also showing its effectiveness in ensuring actual collision avoidance by the TRMS when physical obstacles were present. [ABSTRACT FROM AUTHOR]

Digital hydraulics is a novel alternative to proportional or servovalve-controlled systems in fluid power engineering, providing hydraulic systems with high-energy efficiency, good controllability, and insensitivity to contamination. Switched inertance hydraulic converters (SIHCs) are new digital hydraulic devices that can adjust flow and pressure by digital switching instead of throttling the flow. In this paper, an efficient closed-loop control system is proposed for SIHCs subject to time-varying loading conditions in which the load pressure and/or flow varies with time. The control system is designed to operate SIHCs at optimized switching frequencies and ratios that maximize system efficiency when the load varies. With the proposed controller, the SIHC can effectively adapt to the time-varying load and has achieved up to 10% efficiency improvement and up to 65% pressure ripple reduction without affecting the system's dynamic responses, compared with using a nonoptimized controller. The work shows the feasibility and advantages of simultaneously controlling the switching ratio and switching frequency of SIHCs with a time-varying load. As time-varying loading conditions are commonly found in hydraulic applications, the research outcomes constitute an important aspect in the design and development of highly efficient SIHCs and their practical use in hydraulic machinery. [ABSTRACT FROM AUTHOR]

For off-road vehicles such as excavators and wheel loaders, a large portion of energy is consumed to overcome the soil resistant force in the digging process. For optimal control of the digging tool, a high-fidelity model of the soil-tool interaction force is important to reduce energy consumption. In this paper, an on-line soil resistant force prediction method is proposed. In this method, a hybrid model, which combines a physical model and a data-driven model, is used for the force prediction. In addition, the parameters of the hybrid model can be updated on-line based on real-time data. Comparisons with experimental data demonstrate that the proposed prediction method has an average error of around 12.7%. [ABSTRACT FROM AUTHOR]

Hydraulic actuation systems have a broad range of applications covering almost all areas of manufacturing and production and also the service industry. However, it is usual for systems to have low energy efficiency. In recent decades, due to global environmental concerns, research in the field of digital hydraulics to develop more efficient hydraulic systems has increased. In this paper, an analysis of the possible combinations of chambers in a multichamber cylinder is presented. A mathematical expression is derived to verify the total number of combinations of chambers that it is possible to achieve for a cylinder with four or more chambers and that result in different constant velocities without cavitation occurring. In addition, a digital hydraulic pump is considered a supply unit and, since it has discrete output flow rates, an analytical method to combine the pump with a multichamber cylinder is developed resulting in a total number of cylinder velocities with a required resolution. For application in a positioning system, design guidelines for an actuator using a cylinder with four chambers and a digital hydraulic pump with three fixed displacement units are presented in which an optimization algorithm for the calculation of the cylinder areas and the volumetric displacement of the pumps is applied. The proposed approaches were applied to design a proof-of-concept and the experimental values presented very good accuracy when compared with the results obtained with the model. Closed-loop position control responses show that the system can achieve the required setting time with a smooth movement and steady-state error of around 1 mm. [ABSTRACT FROM AUTHOR]

In this paper, the non-zero finite positive state consensus problem of leader-following multi-agent systems (MASs) connected over a directed network is investigated. The multi-agent network consists of homogeneous linear time-invariant (LTI) positive agents whose minimal positive state-space realization is assumed to be known. In this paper, a state-feedback hierarchical control protocol is proposed where the local controller synthesizes a singular, Lyapunov stable, and Metzler system matrix with a simple dominant eigenvalue at origin. The obtained consensus vector is the positive eigenvector associated with the zero eigenvalue of the synthesized system matrix. The controller gain matrices are obtained by solving a set of necessary and sufficient conditions derived in linear programing framework. A numerical example is given to elucidate the usefulness of the proposed results. [ABSTRACT FROM AUTHOR]

This paper solves the inverse dynamics of a tethered kite analytically. Specifically, the paper presents a procedure for determining the angle of attack, induced roll angle, and tether tension magnitude needed to achieve a desired combination of translational kite position, velocity, and acceleration. The focus of the paper is on energy harvesting kites. However, the underlying approach is applicable to other kite systems, such as kites for propulsion (e.g., SkySails, Hamburg, Germany). Solving inverse kite dynamics analytically is valuable for trajectory optimization, online state estimation, and the analysis of fundamental limitations on kite maneuvers. Previous work in the literature presents several models of kite dynamics, with varying degrees of fidelity and complexity. However, the nonlinearity of these models often makes them difficult to use for optimization, estimation, and control. The paper shows that, under reasonable assumptions, inverse kite dynamics can be solved in terms of the roots of a fourth-order polynomial function of angle of attack. This function has a geometric interpretation, providing insight into the multiplicity of resulting solutions. Moreover, for special cases including a kite with noncambered wings, these solutions can be computed analytically. A simulation validates the success of the proposed approach in computing inverse kite dynamics for a cross-current trajectory. [ABSTRACT FROM AUTHOR]

Model predictive control (MPC) is the mainstream method in the motion control of autonomous vehicles. However, due to the complex and changeable driving environment, the perturbation of vehicle parameters will cause the steady-state error problem, which will lead to the degradation of controller performance. In this paper, the offset-free MPC control method is proposed to solve the steady-state error problem systematically. The core idea of this method is to model the model mismatch, control input offset, and external disturbances as disturbance terms, then use filters to observe these disturbances and finally eliminate the influence of these disturbances on the steady-state error in the MPC solution stage. This paper uses the Kalman filter as an observer, which is integrated into our latest designed MPC solver. Based on state-of-the-art sparse quadratic programming (QP) solver operator splitting solver for quadratic programs (OSQP), an offset free model predictive control (OF-MPC) framework based on disturbance observation and MPC is formed. The proposed OF-MPC solver can efficiently deal with common model mismatch problems such as tire stiffness mismatch, steering angle offset, lateral slope disturbance, and so on. This framework is very efficient and completes all calculations in less than 7 ms when the horizon length is 50. The efficiency and robustness of the algorithm are verified on our newly designed robot operating system (ROS)-Unreal4-CARSIM real-time cosimulation platform and real vehicle experiments. [ABSTRACT FROM AUTHOR]