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This study proposes an adaptive fuzzy state observer-based command filtering technique for a pneumatic active suspension considering the effects of uncertain parameters, unmeasured states, and non-ideal characteristics of the air spring actuator. Firstly, a mathematical model is constructed to investigate the dynamic behavior and examine external disturbances and system errors. The problems of input saturation and unknown dead zone are also considered in this work. By using fuzzy logic systems, unknown nonlinear functions of parametric uncertainties and various passenger masses are approximated. Then, a serial-parallel state observer is designed not only to estimate unmeasured state variables of the suspension system but also to eliminate the prediction errors of observer estimation. To enhance the suspension performance, a prescribed performance function (PPF) is applied to ensure the convergence rate of vertical displacement within specified boundaries. Besides, the “explosion of complexity” issue is eliminated by employing the command filtering technique. An adaptive fuzzy output feedback controller is then developed to reduce the tracking error of sprung mass displacement under the effect of unknown parameters, unmeasured states, and a non-ideal actuator. Moreover, the Lyapunov theorem is applied to prove the stability of the designed controller. Finally, comparative simulation examples will help verify the effectiveness and reliability of the developed approach.

In this paper, an adaptive neural command filtered backstepping scheme is proposed for the pneumatic active suspension with the vertical displacement constraint of sprung mass and actuator saturation. A quarter car model with a pneumatic spring is first fabricated on the basis of thermodynamic theory to describe the dynamic characteristics. To overcome the lumped unknown nonlinearities and enhance the requirement of modeling precision, the radial basis function neural networks (RBFNNs) are proposed to approximate unknown continuous functions caused by the uncertain body mass and other factors of pneumatic spring. To solve the explosion of complexity problem in the traditional backstepping designs, a proposed command filter control is applied by using the Levant differentiators which approach the derivative of the virtual control signals. Nussbaum gain technique is then incorporated into the controller to avoid the problem of the completely unknown control gain and control directions of a pneumatic actuator. In addition, the prescribed performance function (PPF) is suggested to guarantee that the tracking error of the sprung mass displacement does not violate the constraint boundaries. Based on the command filtered backstepping control with PPF, the Lyapunov theorem is then applied to indicate the system stability analysis. Finally, the comparative simulation examples for the pneumatic suspension are given to verify the effectiveness and reliability of the proposed control.

This paper proposes an adaptive fuzzy observer based fault tolerant controller for a pneumatic active suspension system considering unknown parameters, actuator failures, and displacement constraints. A pneumatic spring is used for a quarter car model to enhance the vibration attenuation performance. Since the pneumatic system contains uncertain nonlinearities, fuzzy logic systems are utilized to approximate unknown nonlinear functions of unmodeled dynamics and various masses of passengers. Besides, a nonlinear disturbance observer is proposed to estimate the effects of the actuator failures, approximation errors, and external disturbances. By utilizing the disturbance estimation and fuzzy approximation techniques, an adaptive fault tolerant control (FTC) is designed to enhance the output performance of the vehicle suspension. Meanwhile, the command filtered scheme is introduced to solve the explosion of complexity problem in the traditional backstepping approach by avoiding virtual controller derivatives. In contrast to previous results, the proposed control can handle the fault tolerant problem and ensure the tracking error of vertical displacement converges into a small-predefined boundary by introducing the prescribed performance function. Moreover, the stability of the closed-loop system is analyzed according to the Lyapunov theory. Finally, comparative simulation examples and experimental studies are performed on the active pneumatic suspension test bench to verify the feasibility of the proposed scheme.

Pneumatic actuator is one of the key technologies in the field of active suspension due to its low cost, cleanliness, and high power-to-weight ratio characteristics. However, the dynamic models and control strategies of the pneumatic suspension have not been well demonstrated because they are nonlinear systems. Besides, the vertical displacement stability of sprung mass is very important for ensuring ride comfort, but accurate control is still a challenging problem in the presence of parametric uncertainties. In this study, an adaptive neural networks backstepping scheme is designed for the stability control of the pneumatic suspension. Firstly, a mathematical model of the pneumatic system is studied to investigate the dynamic system behavior and to obtain the control algorithm. Secondly, an extended state observer is applied to estimate uncertain parameters, unmodeled dynamics, and external disturbances. Thirdly, unknown masses of various load passengers are approximated by using radial basis function neural networks (RBFNNs). To enhance the system stability, a proposed control with a prescribed performance function (PPF) is designed to guarantee the vertical displacement of the chassis. Adaptive backstepping control with PPF is developed to stabilize the perturbed system and guarantee tracking performance. Finally, the simulation examples for the pneumatic suspension are employed to investigate the effectiveness of the proposed method.

This research introduces an air spring vibration isolator system (ASVIS) based on a negative-stiffness structure (NSS) to improve the vehicle seat’s vibration isolation performance at low excitation frequencies. The main feature of the ASVIS consists of two symmetric bellows-type air springs which were designed on the basis of a negative stiffness mechanism. In addition, a crisscross structure with two straight bars was also used as the supporting legs to provide the nonlinear characteristics with NSS. Moreover, instead of using a vertical mechanical spring, a sleeve-type air spring was employed to provide positive stiffness. As a result, as the weight of the driver varies, the dynamic stiffness of the ASVIS can be easily adjusted and controlled. Next, the effects of the dimension parameters on the nonlinear force and nonlinear stiffness of ASVIS were analyzed. A design process for the ASVIS is provided based on the analytical results in order to achieve high static–low dynamic stiffness. Finally, numerical simulations were performed to evaluate the effectiveness of the ASVIS. The results obtained in this paper show that the values of the seat displacement of the ASVIS with NSS were reduced by 77.16% in comparison with those obtained with the traditional air spring isolator without NSS, which indicates that the design of the ASVIS isolator with NSS allows the effective isolation of vibrations in the low-frequency region.

By replacing conventional supplies such as fossil fuels or internal combustion engines (ICEs), this paper presents a new configuration of hybrid power sources (HPS) based on the integration of a proton-exchange membrane fuel cell (PEMFC) with batteries (BATs) and supercapacitors (SCs) for hydraulic excavators (HEs). In contrast to conventional architectures, the PEMFC in this study functions as the main power supply, whereas the integrated BAT–SC is considered as an auxiliary buffer. Regarding shortcomings existing in the previous approaches, an innovative energy management strategy (EMS) was designed using a new mapping fuzzy logic control (MFLC) for appropriate power distribution. Comparisons between the proposed strategy with available approaches are conducted to satisfy several driving cycles with different load demands and verify the strategy’s effectiveness. Based on the simulation results, the efficiency of the PEMFC when using the MFLS algorithm increased up to 47% in comparison with the conventional proposed EMS and other approaches. With the proposed strategy, the HPS can be guaranteed to not only sufficiently support power to the system even when the endurance process or high peak power is required, but also extend the lifespan of the devices and achieves high efficiency.

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Construction machines are heavy-duty equipment and a major contributor to the environmental pollution. By using only electric motors instead of an internal combustion engine, the problems of low engine efficiency and air pollution can be solved. This paper proposed a novel energy management strategy for a PEM fuel cell excavator with a supercapacitor/battery hybrid power source. The fuel cell is the main power supply for most of the excavator workload while the battery/supercapacitor is the energy storage device, which supplies additional required power and recovers energy. The whole system model was built in a co-simulation environment, which is a combination of MATLAB/Simulink and AMESim software, where the fuel cell, battery, supercapacitor model, and the energy management algorithm were developed in a Simulink environment while the excavator model was designed in an AMESim environment. In this work, the energy management strategy was designed to concurrently account for power supply performance from the hybrid power sources as well as from fuel cells, and battery lifespan. The control design was proposed to distribute the power demand optimally from the excavator to the hybrid power sources in different working conditions. The simulation results were presented to demonstrate the good performance of the system. The effectiveness of the proposed energy management strategy was validated. Compared with the conventional strategies where the task requirements cannot be achieved or system stability cannot be accomplished, the proposed algorithms perfectly satisfied the working conditions.

Ischemic colitis (IC) is an underreported chronic disease characterized by the hypoperfusion of the bowel mucosa. The diagnosis and treatment may be challenging because its clinical course resembles other colitis or even colorectal malignancies. This paper reports an untypical case to underline the diversity of IC manifestation. A 68-year-old man with several comorbidities was admitted because of abdominal pain with a 6-month duration and a mass in the left lower quadrant. Colonoscopy revealed erosive pseudomembranous colitis narrowed colon segments with ulcerated mucosa mimicking colorectal cancer and inflammatory bowel disease. The stool cultures and Clostridium difficile toxin tests were negative. After the failure of conservative therapy, the Hartmann procedure with temporary ileostomy was performed uneventfully. The histological results of the surgical specimens revealed IC with focal pseudomembranous areas.

Background/AimsTo compare the performance of a modified Glasgow Blatchford score (mGBS) to the Glasgow Blatchford score (GBS) and the pre-endoscopic Rockall score (RS) in predicting clinical interventions in Vietnamese patients with acute nonvariceal upper gastrointestinal bleeding (AN-VUGIB).Methods : A prospective multicenter cohort study was conducted in five tertiary hospitals from May 2013 to February 2014. The mGBS, GBS, and pre-endoscopic RS scores were prospectively calculated for all patients. The accuracy of mGBS was compared with that of GBS and pre-endoscopic RS using area under the receiver operating characteristic curve (AUC). Clinical interventions were defined as blood transfusions, endoscopic or radiological intervention, or surgery.Results : There were 395 patients including 128 (32.4%) needing endoscopic treatment, 117 (29.6%) requiring blood transfusion and two (0.5%) needing surgery. In predicting the need for clinical intervention, the mGBS (AUC, 0.707) performed as well as the GBS (AUC, 0.708; p=0.87) and outperformed the pre-endoscopic RS (AUC, 0.594; p