Search Results (79)

To address the pervasive challenges of resource inefficiency and static management in the hospitality sector, this study proposes a novel management framework that synergistically integrates Model Predictive Control (MPC) with Green Human Resource Management (GHRM). Methodologically, the framework establishes a dynamic closed-loop architecture that cyclically links environmental sensing, predictive optimization, plan execution and organizational learning. The MPC component generates data-driven forecasts and optimal control signals for resource allocation. Crucially, these technical outputs are operationally translated into specific, actionable directives for employees through integrated GHRM practices, including real-time task allocation via management systems, incentives-aligned performance metrics, and structured environmental training. This practical integration ensures that predictive optimization is directly coupled with human behavior. Theoretically, this study redefines hospitality operations as adaptive sociotechnical systems, and advances the hospitality energy-saving management framework by formally incorporating human execution feedback, predictive control theory, and dynamic optimization theory. Empirical validation across a sample of 40 hotels confirms the framework’s effectiveness, demonstrating significant reductions in daily average water consumption by 15.5% and electricity usage by 13.6%. These findings provide a robust, data-driven paradigm for achieving sustainable operational transformations in the hospitality industry. Full article

This paper presents a method to design a preview controller with virtual disturbance and an active suspension system for ride comfort improvement and motion sickness mitigation. Quarter-car and half-car models are selected as the vehicle model. With those models, an LQ optimal preview controller is designed in the discrete-time domain. In the controller, feedback controllers are designed with LQ static output feedback (SOF) control. In real driving environments, it is hard to exactly measure a bump profile, which causes performance deterioration. To cope with difficulties and uncertainties in measuring a real bump, a virtual disturbance is used instead of a real bump. In the LQ optimal preview controller, the virtual disturbance, used for the feedforward control, is optimized with a simulation-based optimization method. To show the effectiveness of the proposed method, a simulation is performed on a vehicle simulation package. The simulation results show that the LQ SOF controller decreases the vertical acceleration and pitch rate of the sprung mass by 28% and 66%, respectively, whereas the preview controllers with the optimized virtual disturbance yield reductions of 41% and 84%, respectively. Those results demonstrate that the proposed preview controller with the optimized virtual disturbance can effectively enhance ride comfort and mitigate motion sickness. Full article

This paper presents a method to design a static output feedback active suspension controller with a quarter-car model for motion sickness mitigation. To mitigate motion sickness in a vehicle, it has been known that the vertical acceleration and pitch rate of a sprung mass should be reduced over the frequency range from 0.8 to 8 Hz. For this purpose, a half-car model has been used with linear quadratic optimal control for controller design because it can describe the pitch motion of a sprung mass. However, a controller design procedure with the half-car model is relatively more complex than the quarter-car one. To cope with this problem, a quarter-car model is used for controller design in this paper. The half-car model consists of two quarter-car models. Based on this fact, a controller designed with a quarter-car model can be applied to the front and rear suspensions in the half-car one. To avoid the full-state feedback in a real vehicle, a static output feedback structure is selected. To find the gains of the controllers for the quarter-car models in the front and rear suspensions, linear quadratic optimal control and a simulation-based optimization method are applied. To validate the proposed method, the controllers designed with the quarter-car and half-car models are simulated on a vehicle simulation package. From the simulation results, it is shown that the static output feedback active suspension controller designed with the quarter-car model is quite effective for motion sickness mitigation. Full article

This article presents a gear selection methodology for electric vehicle powertrains employing two identical Permanent Magnet Synchronous Machines (PMSMs) arranged in a twin-drive configuration. Both machines are coupled through a shared output shaft and operate with coordinated torque–speed characteristics, enabling efficient utilization of the available gear stages. The proposed approach establishes a control-oriented drivetrain framework that incorporates mechanical dynamics together with real-time thermal states and loss mechanisms. Unlike conventional strategies, which rely mainly on static or speed-based shifting rules, the method integrates detailed thermal and electromagnetic loss modeling directly into the gear-shifting logic. By accounting for the dynamic thermal behavior of PMSMs under variable load conditions, the strategy aims to reduce cumulative drivetrain losses, including electromagnetic, thermal, and mechanical, while maintaining high efficiency. The methodology is implemented in a MATLAB/Simulink R2024a and LabVIEW 2024Q2 co-simulation environment, where thermal feedback and instantaneous efficiency metrics dynamically guide gear selection. Simulation results demonstrate measurable improvements in energy utilization, particularly under transient operating conditions. The resulting efficiency maps are broader and flatter, as the motors’ operating points are continuously shifted toward zones of optimal performance through adaptive gear ratio control. The novelty of this work lies in combining real-time loss modeling, thermal feedback, and coordinated gear management in a twin-motor system, validated through experimentally motivated efficiency maps. The findings highlight a scalable and dynamic control framework suitable for advanced electric vehicle architectures, supporting intelligent efficiency-oriented drivetrain strategies that enhance sustainability, thermal management, and system performance across diverse operating conditions. Full article

High penetration of photovoltaic (PV) generation presents new challenges for voltage regulation in distribution networks (DNs), primarily due to output intermittency and constrained reactive power capabilities. This paper introduces a distributed voltage control method leveraging reactive power compensation from PV inverters. Instead of relying on centralized computation, the proposed method allows each inverter to make local decisions using real-time voltage measurements and delayed communication with neighboring PV nodes. To account for practical asynchronous communication and feedback delay, a Distributed Online Primal–Dual Push–Sum (DOPP) algorithm that integrates a fixed-step delay model into the push–sum coordination framework is developed. Through extensive case studies on a modified IEEE 123-bus system, it has been demonstrated that the proposed method maintains robust performance under both static and dynamic scenarios, even in the presence of fixed feedback delays. Specifically, in static scenarios, the proposed strategy rapidly eliminates voltage violations within 50–100 iterations, effectively regulating all nodal voltages into the acceptable range of [0.95, 1.05] p.u. even under feedback delays with a delay step of 10. In dynamic scenarios, the proposed strategy ensures 100% voltage compliance across all nodes, demonstrating superior voltage regulation and reactive power coordination performance over conventional droop and incremental control approaches. Full article

This study introduces a novel control strategy tailored to nonlinear systems with non-minimum phase (NMP) characteristics. The framework leverages reinforcement learning within a cascade control architecture that integrates an Actor–Critic structure. Controlling NMP systems poses significant challenges due to the inherent instability of their internal dynamics, which hinders effective output tracking. To address this, the system is reformulated using the Byrnes–Isidori normal form, allowing the decoupling of the input–output pathway from the internal system behavior. The proposed control architecture consists of two nested loops: an inner loop that applies input–output feedback linearization to ensure accurate tracking performance, and an outer loop that constructs reference signals to stabilize the internal dynamics. A key innovation in this design lies in the incorporation of symmetry principles observed in both system behavior and control objectives. By identifying and utilizing these symmetrical structures, the learning algorithm can be guided toward more efficient and generalized policy solutions, enhancing robustness. Rather than relying on classical static optimization techniques, the method employs a learning-based strategy inspired by previous gradient-based approaches. In this setup, the Actor—modeled as a multilayer perceptron (MLP)—learns a time-varying control policy for generating intermediate reference signals, while the Critic evaluates the policy’s performance using Temporal Difference (TD) learning. The proposed methodology is validated through simulations on the well-known Inverted Pendulum system. The results demonstrate significant improvements in tracking accuracy, smoother control signals, and enhanced internal stability compared to conventional methods. These findings highlight the potential of Actor–Critic reinforcement learning, especially when symmetry is exploited, to enable intelligent and adaptive control of complex nonlinear systems. Full article

This paper investigates the event-triggered dissipative fuzzy tracking control problem of nonlinear networked systems with dynamic quantization and stochastic deception attacks, where the Takagi–Sugeno (T-S) fuzzy system theory is utilized to represent the studied nonlinear networked systems. The event-triggered scheme and the dynamic quantization scheme with general online adjustment rule are employed to significantly decrease the data transmission amount and achieve the rational use of the limited communication and computation resources. A stochastic variable satisfying the Bernoulli random binary distribution is utilized to model the phenomenon of the stochastic deception attacks. The main purpose of this paper is to develop a secure event-triggered quantized tracking control scheme. This scheme guarantees the stochastic stability and prescribed dissipative tracking performance of the closed-loop system under stochastic deception attacks. Moreover, the design conditions for the desired static output feedback tracking controller are formulated in the form of linear matrix inequalities based on the matrix inequality decoupling strategy. Finally, two examples are exploited to illustrate the effectiveness of the developed tracking control scheme. Full article

This paper presents a method to design a parameter adaptive suspension controller to boost ride comfort and to reduce motion sickness. According to recently published papers, combined motions of a sprung mass (SPMS) along heave and pitch directions tend to make motion sickness severe. To reduce motion sickness, it is necessary to design a controller which can reduce the heave and pitch vibrations of a SPMS. To avoid full-state feedback which is very difficult to implement in a real vehicle, a static output feedback (SOF) control is chosen as a feedback structure. With the SOF structure, linear quadratic SOF and parameter adaptive controllers are designed. When designing parameter adaptive controllers, an extended Kalman filter (EKF), equivalent to recursive least square (RLS), is selected for parameter adaptation. To verify performance of the controllers, simulation is performed on vehicle simulation tool. From simulation responses, it is checked whether the proposed parameter adaptive controllers are effective or not and which is the best controller, with respect to ride comfort and motion sickness. Full article

This paper presents a method for designing a static output feedback integrated path tracking controller for autonomous vehicles. For path tracking, state–space model-based control methods, such as linear quadratic regulator, H_∞ control, sliding mode control, and model predictive control, have been selected as controller design methodologies. However, these methods adopt full-state feedback. Among the state variables, the lateral velocity, or the side-slip angle, is hard to measure in real vehicles. To cope with this problem, it is desirable to use a state estimator or static output feedback (SOF) control. In this paper, an SOF control is selected as the controller structure. To design the SOF controller, a linear quadratic optimal control and sliding mode control are adopted as controller design methodologies. Front wheel steering (FWS), rear wheel steering (RWS), four-wheel steering (4WS), four-wheel independent braking (4WIB), and driving (4WID) are adopted as actuators for path tracking and integrated as several actuator configurations. For better performance, a lookahead or preview function is introduced into the state–space model built for path tracking. To verify the performance of the SOF path tracking controller, simulations are conducted on vehicle simulation software. From the simulation results, it is shown that the SOF path tracking controller presented in this paper is effective for path tracking with limited sensor outputs. Full article

This paper studies the composite anti-disturbance static output feedback control problem of networked nonlinear Markov jump systems with general transition probabilities subject to multiple disturbances and deception attacks. The transition probabilities cover the known, uncertain with known bounds, and unknown cases. The unmatched disturbance and deception attacks are attenuated by the static output controller, while the matched disturbance is observed and compensated by the disturbance observer. Then, a composite anti-disturbance static output controller, including a linear part and a nonlinear part, is constructed to satisfy the stochastic $H_{\infty }$ stability. By using the Finsler lemma, sufficient conditions formed as symmetric linear matrix inequalities are derived to design the gains of disturbance observer and the output feedback controller. Finally, some simulations are given to illustrate the feasibility of the developed strategy. Full article

Cable-driven actuators (CDAs) are extensively used in the rehabilitation field because of advantages such as low moment of inertia, fast movement response, and intrinsic flexibility. Accurate control of cable force is essential for achieving precise movement control, especially when the movement is generated by multiple CDAs. However, velocity-induced disturbances pose challenges to accurate force control during dynamic movements. Several strategies for direct force control have been investigated in the literature, but time-consuming tests are often required. The aim of this study was to develop a force feedback controller and a speed feedforward compensator for a CDA with a convenient experiment-based approach. The CDA consisted of a motor with a gearbox, a cable drum, and a force sensor. The transfer function between motor torque and cable force was estimated through an open-loop test. A PI force feedback controller was developed and evaluated in a static test. Subsequently, a dynamic test with a reference force of 100 N was conducted, during which the cable was pulled to move at different speeds. The relationship between the motor speed and the cable force was determined, which facilitated further development of a speed feedforward compensator. The controller and compensator were evaluated in dynamic tests at various speeds. Additionally, the system dynamics were simulated in MATLAB/Simulink. The static test showed that the PI force controller produced a mean force control error of 4.7 N, which was deemed very good force-tracking accuracy. The simulated force output was very similar to the experiment (RMSE error of 4.0 N). During the dynamic test, the PI force controller alone produced a force control error of 9.0 N. Inclusion of the speed feedforward compensator improved the force control accuracy, resulting in a mean error at various speeds of 5.6 N. The combined force feedback controller and speed feedforward compensator produced a satisfactory degree of accuracy in force control during dynamic tests of the CDA across varying speeds. Additionally, the accuracy level was comparable to that reported in the literature. The convenient experiment-based design of the force control strategy exhibits potential as a general control approach for CDAs, laying the solid foundation for precise movement control. Future work will include the integration of the speed compensator into better feedback algorithms for more accurate force control. Full article

This paper presents a comparative study on active suspension controllers for ride comfort. Two types of active suspension controllers are designed and compared in terms of ride comfort: static output feedback (SOF) and parameter adaptive ones, which have identical controller structure. A quarter-car model is selected as a vehicle model. To date, LQR has been used as an active suspension controller. LQR is hard to implement in real vehicles due to the full-state measurement requirement. To avoid the full-state measurement of LQR, SOF control is selected as a controller structure in this paper. Suspension stroke and its rate are selected as sensor outputs for SOF and parameter active controllers. Two types of SOF controllers are designed. The first is the LQ SOF controller, designed with the state-space model and LQ cost function. The second is SOF controllers, designed by simulation-based optimization (SBOM) for the quarter-car model with nonlinear spring and damper. A parameter adaptive controller is designed with the recursive lease square (RLS) algorithm and its equivalent extended Kalman filter (EKF). For comparison, LQR is designed and used as a baseline. From simulation results, it is shown that the static output feedback and parameter adaptive controllers are equivalent to each other in terms of controller structure and ride comfort and which conditions are needed for better control performance on those controllers. Full article

The working accuracy of space optical payloads and sensitive components carried on space aircraft greatly depends on the pointing accuracy and stability of the platform. Based on Disturbance Free Payload (DFP) technology, non-contact maglev technology is proposed in this paper, achieving dynamic and static isolation of the platform module and payload module, so that the vibration and interference of the platform module with movable and flexible components will not be transmitted to the payload module, thereby achieving the effect of vibration isolation. High-precision active control of the payload module is adopted at the same time; the platform module follows the master–slave collaborative control strategy of the payload module, meeting the requirements of high-performance payloads. A primary and backup redundant controller is designed, using a one-to-four architecture. The control board achieves high-speed and high-precision driving current control, voltage output, and outputs current feedback signal sampling. Based on uniform magnetic field design, high-precision force control performance is ensured by adjusting current accuracy. Interdisciplinary joint simulation of electric, magnetic, and structural aspects was conducted on the magnetic levitation isolation system. By conducting physical testing and calibration and designing a testing and calibration system, it has been proven that the system meets the design requirements, achieving high-precision current control technology of 0.15 mA and driving force control technology of 0.5 mN. Full article

The steering system plays a critical role in the vehicle’s handling and directly influences its lateral dynamics. Faults or abnormal behavior in this system can affect performance, cause vehicle instability, and even lead to accidents. Therefore, considering these potential events is essential for designing robust controllers for autonomous vehicles. For this reason, in this work, a fault-tolerant path-tracking Static Output-Feedback controller is designed to handle steering actuator faults in autonomous vehicle steering systems. The controller adopts a Linear Parameter Varying approach to effectively handle nonlinearities associated with varying vehicle speeds and tire behavior. Furthermore, it only uses information from sensors, avoiding estimation stages. This controller can operate in two modes: a no-fault mode where only the steering is controlled to follow the reference path and a fault mode where the controller manages both the steering and torque vectoring. In fault mode, torque vectoring compensates for faults in the steering actuator. The design of the controller is completed considering gain faults in the steering system. The simulation results show that the proposed controller successfully maintains vehicle stability and significantly reduces tracking errors during high-risk maneuvers, achieving reductions of up to 50.65% in lateral error and 47.26% in heading error under worst-case fault scenarios. Full article

Population growth and rising mobility demands have significantly increased traffic congestion and extended travel times. To address these challenges, traffic flow can be optimized by organizing vehicles into clusters, known as vehicle platoons, where cars travel closely together in a co-ordinated manner. Although the concept of vehicle platoon control holds great promise for improving traffic efficiency and reducing fuel consumption, its practical implementation faces several issues. Variations in vehicle specifications, such as differences in mass, can destabilize platoons and negatively impact overall performance. This paper introduces a novel method to maintain stable vehicle co-ordination despite such uncertainties. The proposed method utilizes a static output feedback control strategy, which simplifies the communication architecture within the platoon, as only partial state information from each vehicle is required. The simulation results demonstrate that this method effectively minimizes spacing errors and ensures platoon stability. This approach not only enhances safety but also improves traffic flow, making it a viable strategy for future intelligent transportation systems. Full article