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.

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.

This paper treats the problem of a pneumatic active suspension considering uncertain parameters and displacement constraints under the presence of sensor failure and unmeasured states. A quarter car model is established using an air spring to provide flexible stiffness and an active force to suppress the chassis vibrations. To approximate unknown nonlinear functions of pneumatic actuator dynamic, neural networks are employed as a function approximator. The sensor fault is investigated while all system states of the controlled suspension are assumed to be unmeasurable. Then, a neural state observer is constructed to estimate unmeasured states and compensate for the partial loss of effectiveness of sensor failure simultaneously. An adaptive fault tolerant control is proposed via the command filter backstepping technique to alleviate the effects of sensor failure and solve the explosion of complexity problem. In addition, to improve the tracking accuracy and get the ride comfort, this study is concerned with a prescribed performance function so that the vertical displacement of sprung mass does not violate the predefined boundary. The Lyapunov theorem is then applied to demonstrate that all system signals are semi-globally uniformly ultimately bounded while error variables can converge to the small neighborhood of zero. Finally, comparative simulation examples are analyzed to verify the effectiveness of the designed control with sensor noise and unmeasurable variables.

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 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.

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.