## 5 Experimental Verification  
We used a TFC35 forklift provided by Hefei Banyitong Science and Technology Development Co., Ltd. (Hefei, China) as the research object, and the data of the power motor, deceleration mechanism, rack, gear and steering torque of EPS system are collected. The wheel speed sensor, the torque sensor and the current sensor were installed during the experiment. The experiment was carried out in a warehouse with an indoor relative humidity of 20%. The experimental platform is shown in Figure 2. The speed of the forklift is \(\
u_{\text{u}}=15\) km/h. The LMI toolbox in MATLAB is used to solve the observer gain matrix \(L,\ G,\ H,\ M\) and the controller gain \(K_{x}\). The experiment was conducted manually. In the process of the experiment, the calculated information is loaded into the controller chip dsPIC3 SEP, and the data acquisition card collects the actuator information. The controller receives the sensor signal and regulates the steering torque by power motor voltage which is calculated by the NUIO and adaptive sliding mode algorithm. In order to verify the effectiveness of the proposed integrated FE and FTC method, FE/FTC is integrated for the most frequent actuator partial failure faults. For complete failure and out-of-control cases, the FTC can only be achieved through redundant hardware at present. Figures 3-5 show the collected steering torque information, including the no-fault condition and the fault condition. The fault value is added manually, such as reducing the motor voltage, increasing the gap between the deceleration mechanism, the rack and the gear and increasing the wear. If actuator failure occurs, the steering torque will change at this time. On the contrary, according to the collected power torque information, we can also judge what fault will occur at this time. Figures 3-5 pave the way for the subsequent actuator failure. According to Equations (5) and (6), the output expressions of different types of fault actuators are:  
\[u_{q}=\beta u(t)+u_{0}=u(t)+f_{a}(t)\]  
when \(\beta=1,u_{0}\
eq 0\), it is constant deviation fault and belongs to partial failure fault; if the actuator has a constant deviation fault, the steering torque is shown in Figure 3.  
Figure 3: Constant deviation fault. Figure 2: Experimental platform. When \(0<\beta<1,u_{0}=0\), it is a gain variation fault and belongs to a partial failure fault; if the actuator has a gain variation fault, the steering torque is shown in Figure 4.  
When \(0<\beta<1,u_{0}\
eq 0\), it is a constant deviation and gain variation mixed fault, belonging to partial failure fault; if the actuator has hybrid faults with constant deviation and gain variation, the steering torque is shown in Figure 5.  
For the above three faults, suppose that the ideal power assist torque we need is: \(T_{d}=4 sin(0.4\pi t)\). Ideally, the power torque is proportional to the power motor voltage, that is \(u(t)=4K*sin(0.4\pi t)\) (\(K\
eq 0\)), just for the verification experiment, so \(K\) is going to be 1. The system has the following actuator failure and distribution matrix. In order to satisfy hypothesis 1, and according to references [4; 5; 6], in order to ensure that the influence of fault can be well compensated by control input, it is assumed that matrix \(F\) is equal to matrix \(B\). The values of the actuator faults need be set manually according to Figures 3-5. Additionally, according to the actuator failure, Equation (\(\mathcal{T}\)):  
\[f_{a}(t)=(\  
Failure case 3. When \(0<\beta<1,u_{0}\
eq 0\), we take \(\beta=0.75\), \(u_{0}=-1\); this corresponds to the situation in Figure 5, then the hybrid faults with constant deviation and gain variation is as follows:  
\[f_{a}(t)=(\beta-1)u(t)+u_{0}=\left\{ \begin{array}{cc}0&0\leq t<5\\ -sin(0.4\pi t)-1&5\leq\ t<50\end{array} \right.\]  
It can be seen from Figures 6-8 that in the process of the experiment, the fault estimation value of the actuator collected by the data acquisition card can track the fault setting curve accurately.