In this study, two types of **controller** have been developed for an **automatic** **braking** **system**. The brake model presented manages to achieve an acceptable brake torque for the vehicle. To achieve this, a brake **system** model was developed considering brake pedal, valve, air flow dynamics and brake **system** hydraulics. This model was shown to provide relationship between the outputs **braking** torque and applied pedal force. Then, the development of SMC and PID **controller** to control the pedal force for an **automatic** **braking** has been demonstrated. Results show that both controllers are applicable in an **automatic** **braking** **system**. The results obtained were evaluated and it was shown that the output of the brake torque using both controllers followed the desired brake torque. However, SMC **controller** is proposed to be the best **controller** in this application since it has the better performance and lower percentage error compared to the PID **controller**. Then, SMC **controller** was undergoing an optimization process by chosen PSO as an optimization method, where the brake torque has a better performance compared to only using SMC **controller**.

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(2) This type of control **system** design is known as feedback linearization and should not be confused with conventional linearization. In the case of feedback linearization, the complete nonlinear model in conjunction with state feedback is used to transform the **system** into a linear form, whereas the latter technique makes a linear approximation of the **system** about a given equilibrium point. Feedback linearization can be used to derive linear control systems for many nonlinear dynamic systems but cannot be applied universally. Some disadvantages of feedback linearization are that it requires full state feedback and it is not robust to modeling uncertainty or unmodeled dynamics. In this paper, nonlinear **sliding** **mode** and MPCs are introduced and several applications are presented.

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Dynamic performance is improved of an IM is enabled due to the development of Vector Control analysis. Using vector control strategy, the torque and ﬂux components can be controlled independently like dc motor [2]. To analyses of vector control, we have developed a dynamic model of the induction motor. For that we need to convert the 3-ϕ quantities into 2-axes **system** called the d-axis and the q-axis. This type of conversion is called axes transformation. The d-q axes can be chosen to be stationary or rotating. Then, the rotating frame can either be the rotor oriented or magnetizing ﬂux oriented. However, synchronous reference frame where the d-axis is aligned with the rotor ﬂux is found to be the most convenient technique for analysis point of view [8]. 3.1 Principle of Vector Control

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After observing the results, we are quite clear about the fact that the gain k plays an important part in the stability and working of a **controller**. The figures are showing the results of the SMC on the given plant and also verifying the reaching law condition as the **controller** is implemented on the given **system** within the reaching phase and the **sliding** phase. These implementations are done with the help of mathematical calculations shown in second and third sections of this paper. Hence, the implementation of **Sliding** **Mode** **Controller** using reaching law on a plant is verified.

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The final experiment was designed to explore the bandwidth of the SM controlled **system**. As mentioned in the introduction section, the gait rehabilitation training needs to be task specific. Hence, the **controller** and the hardware **system** need to be able to operate at a bandwidth that is similar to the average gait cycle frequency (0.67Hz) of stroke survivors [27]. The experiment was conducted with the same male subject and with the same setup as those of the first experiment. During the experiment, the subject was instructed to relax his right leg and let the mechanism guide the knee joint movement at discrete fre- quencies varying from 0.2 to 0.7 Hz with 0.1 Hz increment. The average PM pressure was regulated at 360 KPa for this ex- periment. The experimental result is shown in Figure 5. It can Fig. 4 Results of the experiments on knee joint trajectory tracking and compliance control with five healthy subjects. (A) (C) (E): the subjects were instructed to relax their right knees during the entire experiments; (B) (D) (F): the subjects were instructed to obstruct the mechanism’s guidance during the time period high- lighted in yellow. Average PM pressure was regulated at 360, 270 and 180 KPa for the results plotted in top (A, B), middle (C, D) and bottom (E, F) rows, respec- tively.

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Sensitivity analyses are carried out to compare the dynamic performance of the **system**, using FSMC to some parameter variations of the motor. Considering 50% variation of significant **system** parameters such as moment of inertia coefficient, friction viscous coefficient, and motor phase resistance. Fig. (16) shows the speed responses with 600 rpm reference speed for the FSMC, when changing the inertia and friction coefficients. This figure indicates that, the **system** speed response is insensitive to the parameter variations when applying the FSMC. However, fig. (17) shows the speed responses but with varying the motor phase resistance by ±50% for 800 rpm reference speed. The performance response illustrates that the FSMC is more robust and insensitive to phases resistance variations.

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------------------------------------------------------------------------***----------------------------------------------------------------------- Abstract – In this paper modified SEPIC converter with **sliding** **mode** approach is used to control the output from the solar panels. Modified SEPIC converter consist of an additional single inductor and capacitor which improves the efficiency. The **sliding** **mode** control is a non linear control method that alters the dynamics of a non linear **system**. Conventional PI controllers are not so efficient for different loads and disturbances. In order to overcome the drawbacks a proposed SMC based equivalent **controller** is implemented on the modified SEPIC converter. It provides a non inverting output which is an added advantage compared to conventional buck boost converters.

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The advantage of the T-S type fuzzy models is that their description permits the utilization of the state representation, and by consequence to exploit the maximum of the potential relative to this repre- sentation. The Takagi-Sugeno (T-S) type fuzzy model can be viewed as a natural expansion of piecewise linear partition for nonlinear systems. The nonlinear **system** is represented as a collection of the fuzzy IF-THEN rules, where each rule describes the local dynamics by a linear **system** model. The general

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In this study, the performance of SMC, P, PD and PID controllers are evaluated for position tracking control. The Lyapunov approach is used in theoretical analysis in developing the SMC with PID scheme and to ensure that the **system** is under stable condition. The numerical simulation study shows that the proposed **controller** provides better performance in tracking accuracy and time response. The control effort produced from SMC without the chattering effect also is practical to be used in real application. In conclusion, a simple control method without much control effort and better performance can be made with the SMC design based on PID **sliding** surface instead of using conventional PID **controller**.

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dt . The first problem considered here points out how the output load voltage v(t) of the single phase full bridge voltage source inverter, shown in Fig. 10 can be forced to track the externally given sinusoidal reference voltage V ref A sin t by applying the **sliding** **mode** control technique.

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In the above mentioned active control **system**, tire longitudinal slip ratio or so called wheel slip has greatly influences the performance of **braking**, traction and stability control. The longitudinal slip ratio is defined as the difference between the vehicle speed and the wheel speed normalized by the vehicle speed (for **braking**).As this wheel slip ratio has significant role in affecting the quality of vehicle control, it has become an active research area in the automotive field. For ABS, the wheel slip ratio is control to prevent wheel lock up and to maintain the friction coefficient within maximum range to ensure minimum stopping distance [1, 2]. As for the ESP, the wheel slip ratio is controlled such that the resultant forces required to control the vehicle states can obtained [3]. Accordingly, by controlling the wheel slip

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(13) Besides, input voltage perturbations, load variations, and the uncertain value of the parasitic resistances in all components contribute to introduce voltage drops, deviating the actual output voltage of its expected value. Therefore, to obtain a regulated output voltage it is required a closed-loop feed-back control **system**. The presence of the right half-plane zeros in the dynamics of the high-order boost derived converters precludes the use of a single-loop compensator processing the output voltage error. Therefore, the voltage regulation will be performed by a two-loop control scheme whose inner loop will process a fast variable like the input inductor current, whereas the outer loop will establish the reference of the inner loop by treating a slow variable as the output voltage error. Fig. 4 illustrates the hysteresis-based two-loop control, where the inner current loop is defined by means of a **sliding** surface and drives the converter to a stable equilibrium point. The outer loop generates the required reference I E (t) at the output of

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Several types of research on the **sliding** **mode** **controller** have been published such as [16] that developed a vehicle model based on **sliding** **mode** **controller** in order to obtain desired vehicle performance via a two degree of freedom bicycle model. Another publication, [17] investigated the application of SMC by using the nonlinear **sliding** surface for yaw rate tracking of active front steering control where it identifies the cornering stiffness as the parameter. Chuan et al. [18] developed an integral **sliding** **mode** control (ISMC) approach for in-wheel-motor driven electric vehicles steered by differential drive assistance steering. Ghani et al. [19] proposed a **sliding** **mode** **controller** for a linearized single track model with the **system** under conditions of wet and icy µ split conditions.

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Controllers are playing vital roles especially in the assistant of the engineering processes, for example shifting, shaping and lifting. Apart from being an assistant, the **controller** is especially useful in dealing with the **system** major uncertainties and disturbances. It is the fact that the practical systems are mostly intrinsically nonlinear. To overcome the existing drawback in the practical systems, high-performance control **system** is needed to reduce the actual required elements and achieved the desired response, for example, the voltage or power that generates torque to actuate the load or the application. Apart from reducing the actual effort, the high-performance **controller** can perform surprisingly in achieving the desired response even with the parameter changes along with the operation.

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In automation field, designers have proposed several enhancements A precise shortrange radar **system** was developed for anti -collision applications where **automatic** **braking** is applied in response to detection of a collision risk where a very high probability of detection is accompanied by a very low level of false alarms. A brake strategy for an **automatic** parking **system** of vehicle has proposed brake **controller** which work with the **automatic** parking **system** and make the process of parking smooth and stable. Autonomous antilock **braking** **system** (ABS) **system** which can take over the traction control of the vehicle is developed for a four wheel vehicle. ABS is a **braking** **system** that maintains control over the directional stability of the vehicle during emergency **braking** or **braking** on slippery roads by preventing wheel lock-up. There have been considerable advancesin modern vehicle **braking** systems in recent years.

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The **controller** of the variable-structure Control **system** is composed of several different continuous subsystems, and each subsystem has different parameters or different structures. The **system** switches among these subsystems according to some function rules in the working process to improve the dynamic performance of the **system**. The structure of **sliding** **mode** control (SMC) **system** is not fixed, and it can changes purposefully to force the **system** to follow the predetermined state track of **sliding** **mode** in the dynamic process, according to the current state of the **system** (each deviation and its derivatives, etc.).

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There have been considerable advances in modern vehicle **braking** systems in recent years. In automation field , designers have proposed several enhancements. A precise short range radar **system**[1] was developed for anti-collision applications where **automatic** **braking** is applied in response to detection of a collision risk where a very high probability of detection is accompanied by a very low level of false alarms. A brake strategy for an **automatic** parking **system** [2] of vehicle has proposed brake **controller** which work with the **automatic** parking **system** and make the process of parking smooth and stable. Autonomous antilock **braking** **system** (ABS) **system** [3] which can take over the traction control of the vehicle is developed for a four wheel vehicle. ABS is a **braking** **system** that maintains control over the directional stability of the vehicle during emergency **braking** or **braking** on slippery roads by preventing wheel lock-up. Auto-**Braking** **System** using Sensor [4] was proposed to prevent front-end, rear-end, right-turn and left-turn accidents on roads. This module can detect the distance between front vehicle and driver’s vehicle to keep a constant distance using a sensor and operate the brake **system**.

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2 (9) Then taking the first derivative of the Lyapunov function, asymptotically stable or even exponentially stable **system** can be achieved by designing an appropriate V. However, feedback linearization control is based on exact knowledge of the **system** model, which means f(x) and g(x) should be known. In order to design a robust **controller**, the **sliding** **mode** control method is utilized. Firstly, the first **sliding** surface which is the error between the actual state and ideal state can be presented as follows

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The setup for ongoing equipment demonstrate as above in fig. 13. Utilizing DSO and LabVIEW GUI, it's demonstrate the yield flags and some more. DSO used to demonstrate the produced PWM signal from **controller**. The both motors are associated together so it can be easy to watch speed synchronization between them.

many applications. So far, several definitions of fractional order derivatives have been presented [6]. Recently, conformable fractional order derivative as a new definition of fractional order derivative is introduced. One of important its advantages is having simple calculation [7]. Various papers have been presented based on conformable fractional order derivatives. In [8], some important laws and definitions based on conformable fractional order derivative are presented. Fractional Newtonian mechanics based on conformable fractional calculus are studied in [9]. Stability of fractional differential systems is discussed using conformable fractional order derivative in [10]. **Sliding** **mode** **controller** is an effective strategy to control systems with uncertainties and disturbances. In [11], two new nonlinear **sliding** **mode** controllers are developed. In [12], a chattering-free full-order nonlinear **sliding** **mode** **controller** is developed. In order to control type I diabetes in presence uncertainties and disturbances, a fractional order **sliding** **mode** **controller** and adaptive fractional order **sliding** **mode** **controller** are designed in [13]. For an uncertain manipulator, a fuzzy robust fractional order **controller** is developed in [14]. Fractional **sliding** **mode** schemes are presented to track and stabilize some nonlinear fractional-order systems with uncertainty in [15].

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