tion modetransformation of a mobilerobot with wheel-arms. The proposed method aims at transformation from a four-wheeled mode for high speed mobility to an invertedpendulummode, which has advantages of high viewing position and small turning radius. Since the initial state of the system is far away from the target equilibrium point of the wheeled invertedpendulum system, we use a nonlinear controller based on slidingmodecontrol. In contrast that the previous transformation methods cannot control the robot velocity until the robot body is lifted up, the proposed method can take into account the robot velocity from the beginning of the transformation, which enables to complete the transformation in a smaller space. To analyze the asymptotic stability of the control system on the sliding surface, we derive an invariant set in which the system state converges to the origin without going out. Furthermore, the effectiveness of the proposed method is demonstrated in both simulations and real robot experiments.
 J. H. Park and K. D. Kim, "Biped robot walking using gravity compensated invertedpendulummode and computed torque control," in Proceedings. 1998 IEEE International Conference on Robotics and Automation, Institute of Electrical and Electronics Engineers (IEEE).  S. Jeong and T. Takahashi, "Wheeled invertedpendulum type assistant robot: Invertedmobile, standing, and sitting motions," in 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, Institute of Electrical and Electronics Engineers (IEEE), 2007.
A two-wheeled invertedpendulum (TWIP) mobilerobot is a three-degree-of-freedom under- actuated mechanical system with highly nonlinear dynamics. It is quiet challenging to control such system due to its unstable and under-actuated nature. Numerous works on modeling and control of TWIP mobilerobot have been presented in literature. Kim et al  investigated the exact dynamics of the TWIP mobilerobot, and a Linear Quadratic Regulator (LQR) controller was developed for balancing the robot. Fiacchini et al  proposed linear and nonlinear controllers for stabilizing a personal pendulum vehicle. To compensate for the measurable disturbances, the work in  compared the performance of Model Predictive Controller and LQR. Multipoint pole placement control for velocity tracking of the TWIP is shown in . In Jones and Stol , the performance of the two wheeled mobilerobot in low-traction environment was investigated by designing a LQR controller based on linearized model of the robot which includes wheel slip effects. Pathak et al  proposed velocity and position controllers for the TWIP robot via partial feedback linearization. Dai et al  proposed slidingmode controllers for self-balancing and yaw motion and designed independently. While Kim et al  investigated a nonlinear motion control using the State-Dependent Riccati Equation (SDRE) control framework. Kharola et al  discussed a fuzzy logic control strategy for control and stabilization of TWIP.
A two wheeled invertedpendulum (TWIP) mobilerobot is a three-degrees-of-freedom under-actuated mechanical system with highly nonlinear dynamics. This makes it a perfect test- bed for various control algorithms ranging from conventional theoretical control algorithms to intelligent control algorithms. In recent years, various control methods have been reported in literature for controlling the TWIP mobilerobot. In the early work by Ha and Yuta , a linear state feedback and feed forward controller was designed and implemented for the posture and velocity control of the TWIP mobilerobot. Grasser et al  proposed a linear state feedback controller based on pole placement technique for the robot. In  Nawawi et al a TWIP mobilerobot was developed and stabilized using pole placement controller. Kim et al  investigated the exact dynamics of the TWIP, and developed a linear quadratic regulator (LQR) controller for balancing the robot. Fiacchini et al  proposed linear and nonlinear controllers for stabilizing a personal pendulum vehicle. In Seo et al  the performance of the two wheeled invertedpendulum on tilted road was investigated, and an LQR controller was proposed for the system. Nasir et al  compare the performance of LQR and PID in stabilizing two-wheeled balancing robot. In Jones and Stol , the performance of the two wheeled mobilerobot in low- traction environment investigated by designing an LQR controller based on linearized model of the robot which includes wheel slip effects. Pathak et al  proposed velocity and position controllers for the TWIP robot via partial feedback linearization. In Li and Luo  an adaptive controller was proposed for the TWIP system which deals with model uncertainties. Huang et al  proposed three fuzzy controllers based on Takagi-Sugeno and Mamdani architectures for the balancing, traveling and steering of the TWIP mobilerobot. Kausar et al , investigated the effect of terrain inclination on the performance and stability region of a two-wheeled mobilerobot. In Muhammad et al  compare the performance of a partial feedback linearization and an LQR controller in balancing and velocity tracking control of the TWIP mobilerobot.
This study has presented the application of SMC to two bioprocesses represented by different kinetic models. SMC using both deterministic ap- proach and adaptive approach in determining the switching gains, were studied. An improved SMC, combining these two approaches, is applied for sub- strate concentration control using dilution rate as the control input. The excellent performance of the hybrid approach to SMC as compared with PI con- troller, especially in dealing with disturbance rejec- tion and process parameter variation, is shown by simulation for two different fermentation models. SMC has also proven to be robust when tested with step change of more than 50 % in set point, external disturbance and parameter variation. The invariance feature of the SMC also indicates that uncertainties
ABSTRACT: This paper concentrates on the work done on slidingmodecontrol of descriptor systems. Any practical control problem there always be a discrepancy between the actual plant and its mathematical model. These discrepancies (or mismatches) mostly come from unknown external disturbances, plant parameters and unmodeled dynamics. Robust properties of the plant with these disturbances are studied with the help of slidingmodecontrol. Non-Linear systems are studied using the slidingmodecontrol. SMC design can be divided into two subparts viz. (1) the design of a stable surface and (2) the design of a control law to force the system states onto the chosen surface in finite time. The design of the surface should address all constraints and required specifications therefore it should be designed optimally to meet all requirements. The values of gain are obtained using several methods and these values are used in the regulation of the SMC. The tracking control is incorporated to the chemical processes.
Fig. 6.4 S liding mode c o n tro lle r f o r the c h o p p e r-d riv e n dc m otor. The s lid in g lin e is m ade u p o f a lin e a r co m bin ation o f the speed e r ro r a n d the a r m a tu r e c u rre n t w ith a p p ro p riate p r e f ilt e r s . O ve rrid in g o v e rc u rre n t p ro te c tio n is also in co rp o rate d .
Abstract: An InvertedPendulum is a classic control system problem. It is a very good example of an inherently unstable system, which attains stability under a few conditions. This paper studies the Control of InvertedPendulum and aims to model a MobileInvertedPendulum. Here the pendulum and its controller are mounted on a mobile cart. The linear movement of the cart stabilizes the inclination of the pendulum.
A comparison between slidingmode controller, PID controller & PI controller is to be evaluated under internal losses & input voltage variation. Slidingmode controller and PI controller have same overshoot voltage but only difference is that PI controller has more voltage drop than sliding mode.PID controller has maximum settling time as compared to slidingmode & PI controller. To test the robustness of slidingmode controller input voltage is varied from 15V to 10V.it takes place at T = 1.1 sec, though the system was already stabilized to desired voltage value.
In SMC, switching control using signum function is one of the best control strategies to handle the worst case control disturbances. Due to the simplicity and superb robustness of SMC, numerous successful applications have been carried out to a wide variety of engineering systems, such as electric drives, robotic manipulators, etc. (Slotine and Sastry, 1983). In terms of Filippov’s work (Filippov, 1964), (Filippov, 1988), in which the author derived a formal justification which is one possible technique for determining the system motion in a slidingmode, the properties and the definition of the slidingmode have been well presented in detail in (Utkin, 1978) and (Utkin, 1992). A standard SMC controller is designed in two stages. One is the sliding manifold or switching surface. The essential feature of the slidingmodecontrol is the choice of a switching surface of the state space according to the desired dynamical specifications of the closed-loop system. The plant dynamics restricted to this surface or manifold represent the controlled system’s behavior. The other is the discontinuous control law which is designed to steer the trajectories onto the sliding manifold in finite time and to keep the subsequent motion on it. In the ideal case, the resulting motion is called slidingmode. The main advantages of the slidingmodecontrol are:
advances and problems that still need to be considered for future improvements of robot success in worldwide frames. Each chapter addresses a specific area of modeling, design, and application of robots but with an eye to give an integrated view of what make a robot a unique modern system for many different uses and future potential applications. Main attention has been focused on design issues as thought challenging for improving capabilities and further possibilities of robots for new and old applications, as seen from today technologies and research programs. Thus, great attention has been addressed to control aspects that are strongly evolving also as function of the improvements in robot modeling, sensors, servo-power systems, and informatics. But even other aspects are considered as of fundamental challenge both in design and use of robots with improved performance and capabilities, like for example kinematic design, dynamics, vision integration.
In recent years, underwater vehicles have been widely used for scientific inspection of deep sea, long range survey, oceanographic mapping, underwater pipeline tracking, exploitation of underwater resources and so on [1-2]. Underwater vehicles are difficult to control, due to nonlinearity, time variance, unpredictable external disturbances such as the environmental force generated by the sea current fluctuation and the difficulty in accurately modeling the hydrodynamic effect . The well- developed linear controllers may fail in satisfying performance requirements, especially when changes in the system and environment occur during the AUV operation. Therefore, it is highly desirable to have a robust control system that has the capacities of learning and adopting to the unknown nonlinear hydrodynamic effects, parameter uncertainties, internal and external perturbations such as water current or sideslip effect. In order to deal with parametric uncertainty and highly nonlinearity in the AUV's dynamics, many researchers concentrated their interests on the applications of robust control for underwater vehicles .
Fig. 11 and Fig. 12 show two sets of results comparing the application of fuzzy control and conventional control (PID controller) techniques to the invertedpendulum problem simulation. For the same system parameters here, the PID controller proportional gain, Kp, derivative gain, Kd and integral gain, Ki are found to be 9, 14, and 0.06 respectively. The first two graph show that the fuzzy logic controller gives a smaller overshoot and shorter settling time. In the second set, the mass of the cart is changed without modifying the controllers. Fig. 14 shows that the conventional controller totally failed to balance the pendulum as it was designed for the nominal value of cart mass. On the other hand, the fuzzy logic controller exhibited small performance degradation due to this parameter change as shown in Fig. 13. This proves that fuzzy logic is not based on the mathematical model of the invertedpendulum and more robust to mass variations.
Increasing labour costs coupled with the need to improve product quality place the development of manipulator systems in the spotlight (Motoi, Shimono, Kubo, & Kawamura, 2014). The development of appropriate control systems to provide high accuracy is a key design challenge. The presence of system uncertainty and disturbances is a significant issue in achieving high control performance as there is likely to be some discrepancy between the actual manipulator system and the mathematical model used to develop the control system. In conventional controller design, the control algorithm can be based on a nonlinear compensation method but this approach is complex and costly for implementation (Chen, Zhang, Wang, & Zeng, 2007).
Abstract. It is difficult to obtain the cam profile accurately in cam grinding because of the complicated contour, the grinding force fluctuate, the tracking performance of the servo system and other interferential factors. The tracking performance of the servo system has a great influence on the cam profile error and leads to the reduction of the cam profile precision. In order to reduce the profile error of cam and improve the machining accuracy, the tracking performance of each servo axis is improved by using slidingmode variable structure control method. The slidingmode variable structure controller adopting the method of proportional switching, quasi-slidingmode and dynamic switching function are designed respectively. The simulation analysis are carried out, and the results of simulation show that the tracking error can be reduced effectively using the slidingmode variable structure control in cam.
The LuGre friction model, which is the improvement as well as the extension from the Dahl model that combines the pre-sliding friction behavior of the Dahl model with the steady-state friction characteristic of the sliding regime, will be covered in the following section.
types of fuzzy systems. The driving force for devising such complicated architectures was the fact that ADALINEs were so simple that they could not capture complex input output relations with frozen parameters. Yet it was possible to implement the ADALINE as an adaptive system, which can respond appropriately by an adaptation scheme. Given a task to be accomplished, the process describing the best evolution of the adjustable parameters is the process of learning, which is sometimes called adaptation, tuning, adjustment or optimization, all referring to the same reality in the context of adaptive systems. Many approaches have been proposed, perceptron learning rule, gradient descent, Levenberg–Marquardt technique, Lyapunov-based techniques are just to name a few; a good treatment can be found in . A common feature of all these methods is the fact that the differentiation and integration, or shortly differintegration, of quantities are performed in integer order, i.e. D :=d/dt for the differentiation with respect to t and I=D −1 for integration over t in the usual sense. A significantly different branch of mathematics, called fractional calculus, suggests operators D with ∈ [3, 4] and it becomes possible to write D f =D 1 /2 (D 1 /2 f ). Expectedly, Laplace and Fourier transforms in fractional calculus are available to exploit in closed loop control system design, involved with s or (j) generic terms, respectively.
ABSTRACT:The main objective of this paper is to design a SM Controller for a buck converter to convert a dc input voltage to the required lower dc output voltage level for lower power application to solve the problem of voltage regulation and high power loss of the linear voltage regulator circuit. The converter uses a switching scheme which operates the switch MOSFET in cutoff and saturation region to reduce power loss across MOSFET. Then, the output voltage is controlled using SM Control technique to get the desired output voltage level. The design is based on low power application such as laptop charger, mobile charger etc. The circuit is simulated using MATLAB/SIMULINK software to obtain desired response.The SM control is a type of nonlinear control introduced initially as a means for controlling variable structure systems. The main advantage of the SM control over other types of nonlinear control methods is its ease of implementation. This makes it well suited for common DC–DC power regulation purposes. The SM control is naturally well suited for the control of variable structure systems. Characterized by switching, power converters are inherently variable structure systems. It is therefore; appropriate to apply SM control on power converters.
The intersection of black tiles in the pool is selected as the origin (O) of fixed coordinate system in Figure 3. The downward direction is selected as the positive direction of X axis and the left direction is selected as the positive direction of Y axis. The red point on the center of the spherical underwater robot is selected as the origin (O) of coordinate system of spherical robot. The changes of central position are studied according to the data. Figure 4 which corresponds to Figure 3 is the trajectory of center point of spherical underwater robot in the XY plane of fixed coordinate system during the straight forward motion. The initial coordinate of spherical underwater robot which is relative to fixed coordinate system is (15, 0) and the unit is centimeter. The Figure 4 shows that the straight forward trajectory of spherical underwater robot is basically a straight line although there is a certain deviation between the actual track and the desired track, which shows that the robot with the above mentioned system basically meets the expected requirements and can achieve straight forward motion.
In this paper, a synergistic combination of Radial Basis Function Neural Network (RBFNN) with fuzzy slidingmodecontrol methodology is proposed. The slope of the sliding surface is used to adjust with Fuzzy logic. The weights of the RBFNN are adjusted according to an adaptive algorithm for the purpose of controlling the system states to hit the sliding surface and then slide along it. The proposed method and PID control are implemented on an industrial robot (Manu- tec-r15) and the results obtained from the applications are presented.