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N2 - The main goal of this study is to design a full-car active suspension controller with a reduced order observer for vehicles so as to improve ride comfort and reduce the suspension deflection. AB - The main goal of this study is to design a full-car active suspension controller with a reduced order observer for vehicles so as to improve ride comfort and reduce the suspension deflection.

Department of Electrical Engineering. Abstract The main goal of this study is to design a full-car active suspension controller with a reduced order observer for vehicles so as to improve ride comfort and reduce the suspension deflection. Fingerprint Active suspension systems. Sliding mode control.

Fuzzy control. We model the disturbances as random position change of P 11 and P 7.

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Since the PID controller is designed based on a linearized model where the piston position is in the middle of the chamber and it does not take advantage of measured actual piston displacement, the stand alone PID force control is indeed no match to the SMC controller. The simulation results are shown in Figure 9. Without the external position control loop, the SMC controller is effective, while the PID controller cannot handle the source pressure noise.

Then, their performances within the position controller loop are further compared in simulations. When excitation source of cabin displacement is sinusoidal movement with amplitude 0. As shown in Figure 10 , the SMC controller is faster and more robust. To further evaluate the performance, we apply logged data from a field test as the excitation source of cabin displacement and compare the tracking performance.

As shown in Figures 11 and 12 , the SMC controller still shows better performance than the PID controller in both force and position tracking, respectively. The simulation results justify that the SMC controller has better performance in both force and position tracking than traditional PID controller under current setup and therefore we implement SMC as the inner loop controller.

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The comparison of computational time and position tracking of the three different controllers is shown in Table 2. The simulation time is 10 seconds. The simulations are conducted on a laptop computer with 4 cores, 2. The hardware implementation contains two identical pneumatic actuation subsystems for vertical suspension and two for lateral suspension.

Pressure sensors PRS are applied to monitor the pressures in each chamber and air supply with current signal as output. The position of cabin floor is obtained via an inertial measurement unit IMU and another IMU is mounted on the seat bracket to measure seat vibration.

To avoid all the drawbacks brought by distributing control algorithms, one ECU is the master unit, collecting all the information and conducting control calculation while the other one is the slave unit, receiving command from the master via CAN bus to control the valves connected by it. All data and commands are collected and sent to a computer via CAN bus as well. The seat undercarriage is mounted on a cabin suspension which is attached to a Stewart platform from Rexroth.

Relevant information regarding the test rig can be found in [ 41 ]. The hardware installed in the system is listed in Table 3. Therefore, proposed controller can be directly connected to the interface units for software development after simulation.

Presentation Description

More details can be found in [ 42 ]. The performance of the proposed cascade controller is evaluated in experiments based on a test rig of the suspension. However, the effectiveness of reducing vibrations for simple harmonics indicates the capability of reducing vibrations composed by corresponding frequencies. The force tracking controller is implemented via SMC technique.

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It lays the foundation for the controllability of the suspension. Force tracking is monitored under vertical, lateral and comprehensive vibration. When the cabin floor is exposed to vertical vibration, only vertical cylinders, namely cylinders 2 and 3, are monitored since the lateral ones only need to keep still. The excitation source follows sinusoidal movement. The amplitudes are set to be 0. The testing results of cylinder 3 under 0.

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The SMC controller is effective at both cases and the tracking performance is better at high frequency due to the choice of slope vector k i. When the cabin is subject to only lateral vibration, force tracking of lateral cylinders is monitored. The amplitude of vibration is set to 0.

The testing results of cylinder 4 under 0. Similar conclusion can be drawn from the testing results. The SMC controller works as expected in both cases and has better performance at 0. If the undercarriage is exposed to both vertical and lateral vibrations simultaneously, the reference force would be much more irregular compared to the one under vibration with single direction.

As a result, it would be more challenging for the controller. A series of tests have been carried out and the results are similar to the ones stated above where better results are found in high frequencies. Figure 15 illustrates the force tracking of the system under 0.

The tracking errors are smaller at 0. From these results, we draw conclusion that SMC provides satisfactory performance for force tracking in this context. Although the nature of switching control brings chattering problem, it can be reduced by careful choice of control parameters. The chattering performance of force tracking here is also due to unstable air pressure, as shown in Figure 7 , because the air supply is from a pneumatic network instead of a dedicated source. Therefore, with stable air supply pressure, we expect much better performance. Under the similar vibration condition, the piston positions of all cylinders are also monitored and the results of cylinder 3 and 4 are shown here.

Figure 16 shows the performance of relevant cylinders under both vertical and lateral vibrations. The normalized RMSE values of position tracking under the vibrations at the selected frequencies are listed in Table 5. The RMSE here are mainly caused by tracking delays. The adequate position tracking performance justifies the feasibility of proposed control approach. Besides, when the system is exposed to vibrations in both directions, the system performs as expected, indicating that there is no substantial interference when both vertical and lateral systems are functioning at the same time.

To evaluate the feasibility of length allocation unit and to tell if the whole system performs as intended, both the cabin floor and seat coordinates are measured in the earth frame via IMUs. The tests are conducted under vibrations in only one direction and in both directions. Figure 17 shows the suspension performance when the cabin is exposed to vibrations only in vertical direction. The vibration amplitudes have been significantly reduced by the active suspension. Figure 18 shows the suspension performance when the cabin is exposed to vibrations only in lateral direction.

The performance is not as satisfactory as the one in vertical directions due to the joint friction and the fact that the suspension is not symmetric because of poor manufacture. Yet noticeable reductions of vibrations are achieved by the proposed controller. When the cabin floor is exposed to vibrations in both directions, the suspension could still reduce the amplitude of vibration. Figure 19 shows the suspension function under vibrations on both directions. The reduction of seat vibration amplitudes divided by the floor vibration amplitudes, namely normalized reduction, under vibrations shown in Figures 17 , 18 and 19 are listed in Table 6.

Relatively big vibration reductions are achieved via the active suspension under the proposed controller. The paper presents a cascade control approach to the active suspension of forest machines using pneumatic actuators and parallel mechanical structure. The controller is designed to reduce both vertical and lateral vibrations between 0. In the future, other techniques for position control of cylinders would be investigated to increase the suspension performance under varying mass load.

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Force and stiffness tracking will also be investigated further. Yuchao Li received the B. He is currently a Ph. His research interests include control of automated vehicles, reinforcement learning and model predictive control. Lei Feng received the B. Yu Wang received the B. He is currently pursuing his Ph. His main research interests include systems biology and robust control. Volume 21 , Issue 1. Special Issue: SMC based observation, identification, uncertainties compensation and fault detection.

The full text of this article hosted at iucr. If you do not receive an email within 10 minutes, your email address may not be registered, and you may need to create a new Wiley Online Library account. If the address matches an existing account you will receive an email with instructions to retrieve your username. Asian Journal of Control.

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