Sliding Mode Control with Minimization of the Regulation Time in the Presence of Control Signal and Velocity Constraints

Pietrala, Mateusz; Leśniewski, Piotr; Bartoszewicz, Andrzej

doi:10.3390/en14102887

Cited by 5 publications

(6 citation statements)

References 21 publications

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“…This motivated us to analyze such a sliding mode controller, that achieves a good dynamical performance in the presence of some limitations. This work extends the approach presented previously [20], by taking into account the minimization of IAE, instead of the regulation time. This approach is more advantageous, as it ensures minimizing the transient error value, and not only the convergence time to the desired state.…”

Section: Introductionmentioning

confidence: 73%

IAE Minimization in Sliding Mode Control With Input and Velocity Constraints

Pietrala¹,

Leśniewski²,

Bartoszewicz³

2022

IEEE Access

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In this work an optimal sliding mode controller for second order, nonlinear systems is proposed. First, the sliding surface is selected to obtain finite time convergence to the desired state. Moreover, to ensure robustness with respect to unknown external disturbances and model uncertainties, the surface is time-varying and at the start of the control process it intersects the point, whose coordinates are defined by the initial state. Thus, the existence of the sliding mode is ensured for the whole control process. Next, admissible values of the hyperplane parameters, that ensure satisfaction of velocity and/or control signal constraints are determined. Lastly, optimal values of these parameters, in terms of integral absolute error (IAE) are calculated. The main motivation of this paper was to obtain the good dynamical performance of the system and robustness by eliminating the reaching phase, overcoming the external, unknown disturbances and obtaining a finite-time convergence of the representative point to the desired state. The other main issue was to include some key limitations such as control signal and velocity constraints in order to facilitate the practical application of this strategy.

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Section: Introductionmentioning

confidence: 73%

IAE Minimization in Sliding Mode Control With Input and Velocity Constraints

Pietrala¹,

Leśniewski²,

Bartoszewicz³

2022

IEEE Access

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“…To achieve Objective 2, the analysis needs to be aided by using the properties of the external circle of the triangle [20], which is achieved as follows.…”

Section: Target Segmentation Methods For Repositioning Dronesmentioning

confidence: 99%

“…By the above method, the drones numbered 3, 4, 7, and 9 can be adjusted to the circumference of the circle. Thus, Objective 1 can be achieved.To achieve Objective 2, the analysis needs to be aided by using the properties of the external circle of the triangle[20], which is achieved as follows.Finally, for all drones on the circumference to be uniformly distributed, the visual analysis is shown in Figure6below.…”

mentioning

confidence: 99%

Unmanned aerial vehicle passive positioning technology based on cross-location and target splitting

Wang

Yang

et al. 2023

J. Phys.: Conf. Ser.

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In order to ensure that the UAV fleet can maintain maximum electromagnetic silence and minimise the emission of UAV electromagnetic signals during formation flight, a theoretical method of pure azimuth passive positioning can be used to optimise their relative positions. This paper analyses different formation situations of UAVs and presents the best solution for pure azimuth passive positioning of UAVs in formation flight and the corresponding positions. The paper first establishes polar coordinates based on known orientation information. It uses the sine theorem for triangles in the plane and the joint cubic equations to obtain the final result. It then uses triangulation to verify the accuracy of the specific location of the UAV by solving a least squares problem. Using the idea of cross-location, two intersecting trajectories are constructed and their intersection is used to improve the specific position of the UAV. Due to the bias in the position of the location of the signal sent by the UAV, this paper uses of the idea of target segmentation to split the target into multiple sub-targets and implement them separately. This is done in order to avoid the need for more UAVs to send their signal while adjusting the UAV position, making the Drones evenly spaced around a circumference.

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“…The sliding mode control approach is a nonlinear and robust control approach. In this controller, the desired behavior of the robot is defined in terms of a sliding surface [11,12]. The Lyapunov stability theorem is used to design the control signal to guarantee reaching the sliding surface [13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Robust Sliding Mode Fuzzy Control of Industrial Robots Using an Extended Kalman Filter Inverse Kinematic Solver

Khanesar

Branson

2022

Energies

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This paper presents a sliding mode fuzzy control approach for industrial robots at their static and near static speed (linear velocities less than 5 cm/s). The extended Kalman filter with its covariance resetting is used to translate the coordinates from Cartesian to joint angle space. The translated joint angles are then used as a reference signal to control the industrial robot dynamics using a sliding mode fuzzy controller. The stability and robustness of the proposed controller is proven using an appropriate Lyapunov function in the presence of parameter uncertainty and unknown dynamic friction. The proposed controller is simulated on a 6-DOF industrial robot, namely the Universal Robot-UR5, considering the maximum allowable joint torques. It is observed that the proposed controller can successfully control UR5 under uncertainties in terms of unknown dynamic friction and parameter uncertainties. The tracking performance of the proposed controller is compared with that of the sliding mode control approach. The simulation results demonstrate superior performance of the proposed approach over the sliding mode control method in the presence of uncertainties.

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Sliding Mode Control with Minimization of the Regulation Time in the Presence of Control Signal and Velocity Constraints

Cited by 5 publications

References 21 publications

IAE Minimization in Sliding Mode Control With Input and Velocity Constraints

IAE Minimization in Sliding Mode Control With Input and Velocity Constraints

Unmanned aerial vehicle passive positioning technology based on cross-location and target splitting

Robust Sliding Mode Fuzzy Control of Industrial Robots Using an Extended Kalman Filter Inverse Kinematic Solver

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