Coating deviation control in transverse direction for a continuous galvanizing line

Problem statement: Sensitization of robot hand is still remaining as crucial issue since most of robot hand systems nowadays are only capable to grasp a predefined specific object. It is still difficult for robot hand system to realize human-like tactile sensation. Some common problems in robot hand system are low accuracy sensing device, sensors are not robust enough for long time work and heavy duties, inconsistence tactile sensing detection and difficulties in control of sensing fusion with robot trajectory. These problems are apparently drawback the progress to commercializing robot hands as real consumer products. Approach: This study presented the application of optical three-axis tactile sensor to robot hand to improve sensitization quality in robotic hand system. The proposed tactile sensor system was designed with compliance modules to communicate with robot hand control system. The sensing principle used in this tactile sensor comparatively provides better sensing accuracy to detect contact phenomena from acquisition of three axial directions of forces. Methodology of force and slippage detection in the tactile sensor system was presented. Accordingly, the optimization of robot hand control algorithm to comply with the tactile sensor system was presented and verified in experiment of grasping and twisting. Results: The tactile sensor presented in this study is capable of detecting normal and shear force simultaneously. The proposed methodology was verified in experiment with paper cup and water, in which the result shows the robot control system managed to respond to the proposed object stiffness distinction parameters and effectively respond to sudden change of object weight during grasping. An experiment of grasping and twisting motions was conducted using a bottle cap. In order to perform simultaneous grasping and twisting tasks, optimization of the control algorithm was conducted with additional parameters to satisfy the desired tasks. Conclusion: Experimental result shows that the robot hand managed to perform grasping and twisting of bottle cap smoothly. The overall results revealed good performance of the proposed optical three-axis tactile sensor system and robot hand control algorithm for future application in a real artificial robot hand. In addition, slippage sensation measured in a robot control system could contribute a better maneuvering of the robot arm-finger system.

Application of an H/sub 2/ optimal adaptive control algorithm as a power system stabilizer is described in this paper. The algorithm deals with disturbance attenuation in the sense of H/sub 2/ norm for nonlinear systems. Results of simulation and experimental studies suggest that the H/sub 2/ optimal control algorithm could be successfully used for the control of nonlinear systems such as synchronous generators.

Optimal control algorithms usually involve a state feedback including displacements and velocities. For control of building structures subjected to strong earthquake ground motions, it is much easier to measure the acceleration response than the displacement response. In this paper, a version of the instantaneous optimal algorithm, using velocity and acceleration feedbacks, for nonlinear or hysteretic structural systems is presented. Stable controllers are obtained using the Lyapunov direct method. The Lyapunov function for the controlled nonlinear structure is obtained through an appropriate transformation. The optimal algorithm is applied to control of a hysteretic building structure equipped with an aseismic hybrid control system. It is demonstrated numerically that for the particular hybrid control system considered, the performance of the present control algorithm is as good as the previous algorithm, which uses the displacement response of the structure for the determination of the control vector.

Instantaneous optimal control with acceleration and velocity feedback

This paper discusses the development of a linear quadratic optimal control algorithm for web guides, and implementation of the control algorithm using web lateral position feedback from a new, experimental fiber optic sensor. The lateral dynamic model of the web and the measurement characteristics of the fiber optic sensor are conducive for a linear quadratic regulator design. The performance of the optimal control algorithm with web lateral position feedback from the fiber optic edge sensor is evaluated by conducting experiments on a web platform. Experiments were also conducted using the same controller but with an existing industrial infrared sensor for web lateral position measurement. Results from a series of comparative experiments indicate that the optimal control algorithm with feedback from the fiber optic sensor provides accurate lateral position regulation in the presence of disturbances, at various web transport speeds, and with web materials with different mechanical, physical and geometric properties. Based on the analysis of the web lateral dynamic model, recommendations for proper guide operation and selection of appropriate web transport conditions for good guiding performance are also discussed.

Applying optimization techniques to control systems remains a challenging task. Control system technology is emerging rapidly due to the high demands for commercialization in engineering and industrial fields, but existing optimization techniques are considered weak to cope with increasingly complex control problems. More powerful optimization techniques are urgently needed to catch up with the prerequisites for optimal control. Hence, this research is dedicated to the development of an improved optimization algorithm to solve the optimal thermoregulatory control problem for piping systems in a more efficient manner. As a research outcome, the Memetic Alligator Optimization (MeAgtrO) algorithm is proposed. On top of the mathematical hunting and relocating mechanisms, MeAgtrO adds several evolutionary operators that replicate satiety awareness, mating, generational alternation, and dispersed hunting. Unlike the standard optimizer, which only emphasizes global and local search transitions, these improved variants give the ability to shuffle, swap, replace, and disperse agent information for greater flexibility. Upon application to a simulated piping system to optimally control thermoregulation processes, MeAgtrO statistically outperformed the other compared algorithms, showing 100% accuracy, 99.99% precision, and 99.99% robustness in minimizing the tracking error, response time, and equipment burden of the system. MeAgtrO has been shown to have high processing speed for optimal application control, which corresponds to its superior convergence speed to stabilize at 40% of iterations. While showing satisfactory clustering properties, MeAgtrO also demonstrated the best step response with a rise time of 0.40s, settling time of 0.99s, 0 tracking error, 0% overshoot, and 0% undershoot. ABSTRAK: Penerapan teknik optimasi pada sistem kawalan adalah tugas mencabar. Teknologi sistem kawalan berkembang pesat disebabkan oleh permintaan tinggi bagi komersialisasi dalam bidang kejuruteraan dan industri, tetapi teknik optimasi sedia ada dianggap lemah dalam mengatasi masalah kawalan yang semakin kompleks. Teknik optimasi lebih kuat diperlukan dengan segera untuk memenuhi prasyarat kawalan optimal. Oleh itu, penyelidikan ini bertujuan bagi membangunkan algoritma optimasi yang lebih baik bagi menyelesaikan masalah kawalan termoregulator optimum pada sistem paip dengan lebih cekap. Penyelidikan ini mencadangkan algoritma Memetic Alligator Optimization (MeAgtrO). Selain dari mekanisme pemindahan dan pemburuan matematik, MeAgtrO memiliki beberapa pengendali evolusinari yang menggandakan kesedaran kenyang, mengawan, alternasi generasi, dan pemburuan tersebar. Berbeza dengan pengoptimuman standard, penekanan hanya pada peralihan global dan carian tempatan, ini membaiki varian dengan memberikan keupayaan menyusun semula, menukar, mengganti, dan menyebar maklumat ejen bagi fleksibiliti yang lebih besar. Apabila digunakan pada sistem paip bersimulasi bagi mengawal proses termoregulasi secara optimum, MeAgtrO secara statistik mengatasi algoritma lain, menunjukkan ketepatan 100%, kejituan 99.99%, dan kekuatan 99.99% dalam mengurangkan kesilapan pengesanan, masa tindak balas, dan beban sistem peralatan. MeAgtrO telah terbukti mempunyai kelajuan pemprosesan yang tinggi bagi aplikasi kawalan optimum, sejajar dengan kelajuan konvergensi yang lebih bagus bagi menstabilkan iterasi 40%. Di samping menunjukkan sifat kelompok yang memuaskan, MeAgtrO juga memiliki respons langkah terbaik kenaikan masa 0.40s, masa penyelesaian 0.99s, ralat pengesanan 0%, lebih sasaran 0%, dan kurang sasaran 0%.

This paper presents a model-free optimal tracking control algorithm for an aircraft skin inspection robot with constrained-input and input time-delay. To tackle the input time-delay problem, the original system is transformed into a delay-free system with constrained-input and unknown input coupling term. In order to overcome the optimal control problem subject to constrained-input, a discounted value function is employed. In general, it is known that the HJB equation does not admit a classical smooth solution. Moreover, since the input coupling term of the delay-free system is unknown, a model-free integral reinforcement learning(IRL) algorithm which only requires the system sampling data generated by arbitrary different control inputs and external disturbances is proposed. The model-free IRL method is implemented on an actor-critic neural network (NN) structure. A system sampling data set is utilized to learn the value function and control policy. Finally, the simulation verifies the effectiveness of the proposed algorithm.

In communication channel of a tele-operation control task, there always exists a time-delay problem which causes poor performance and even instability of the system. To overcome the time-delay problem, in this paper, the Smith predictor configuration is considered. The Smith predictor can stabilize the system by taking the time delay term out of the characteristic equation when the system model is given and a delayed time is known. Neural network is employed to identify and model the slave controlled system to form the structure of the Smith predictor. Experimental studies are conducted to regulate contact force of a mobile robot by the master robot, the Phantom haptic device.

Mystic software is designed to compute, analyze, and visualize optimal high-fidelity, low-thrust trajectories, The software can be used to analyze inter-planetary, planetocentric, and combination trajectories, Mystic also provides utilities to assist in the operation and navigation of low-thrust spacecraft. Mystic will be used to design and navigate the NASA's Dawn Discovery mission to orbit the two largest asteroids, The underlying optimization algorithm used in the Mystic software is called Static/Dynamic Optimal Control (SDC). SDC is a nonlinear optimal control method designed to optimize both 'static variables' (parameters) and dynamic variables (functions of time) simultaneously. SDC is a general nonlinear optimal control algorithm based on Bellman's principal.

In this paper, a mathematical model of flight pattern and flight attitude is established and analyzed. The flight pattern of the power patrol flight is analyzed. Based on the original linear quadratic optimal control algorithm, the motion and optimal control model of the unmanned aerial vehicle (UAV) patrol process is established to meet the unmanned aerial vehicle (UAV) inspection constraints, then a linear quadratic optimization Control based optimal control algorithm is proposed. In this paper, four-rotor aircraft as an example for simulation research, different motion models with different optimization control, and add different interference signals and the corresponding filter algorithm for antijamming performance testing. The simulation results show that the UAV can obtain good control effect by the linear quadratic optimal control, and its stability and anti - jamming performance can be improved obviously.

Workload control (WLC) systems can be seen as a natural extension of input-output control systems which allow both the control of jobs into the shop and the control of capacity which affects the flow of finished goods out of the shop. In previous research, it has been shown that WLC can help to reduce manufacturing lead times, even if the total workload processed by the shop in a given time period is maintained at a constant rate. However, the relative contributions of the input control mechanisms versus the output control mechanisms have not been clearly determined. This paper seeks to explore this which affects the flow of finished goods out of the shop. In previous research, it has been shown that WLC can help to reduce manufacturing lead times, even if the total workload processed by the shop in a given time period is maintained at a constant rate. However, the relative contributions of the input control mechanisms versus the output control mechanisms have not been clearly determined. This paper seeks to explore this issue using a simulation of a real job shop, in which the WLC system that has been developed at Lancaster University is used. It explores the use of input control alone and the use of input control plus output control. In addition, the relation between workload and various performance measures, such as lead time, queueing time and capacity utilisation, is explored. The results of the experiments show that one should consider input control and output control as two levers of control that are both to be pulled rather than relying on the single lever of stopping the input when the WIP reaches some preset level.

<div class="section abstract"><div class="htmlview paragraph">Autonomous driving related technologies have become a hot topic in academia and industry. Planning control is one of the core technologies of autonomous driving, which is conducive to vehicles safe and efficient driving. This paper proposes a novel optimal speed control algorithm, which considers the power system's energy consumption, the speed limit on the road, and the safe distance of the vehicle in front. An optimal speed control model of “From battery to wheel” energy consumption is established by constructing a performance index function based on the best-fitting formula of motor power, motor speed and torque. Based on the optimal control principle, the fourth-order ordinary differential equation of the speed control model is established, based on the indirect adjoining approach, the speed control model under the restriction of the road speed limit and safe distance of the preceding vehicle is derived and the analytical expression is obtained. Under the restriction of the road speed limit and safe distance of the preceding vehicle, the model predictive control method is introduced, the urban working conditions in the NEDC cycle are selected for simulation, and the optimal control input is solved in real-time, compared with the intelligent driver driving strategy, this algorithm is proved to be superior in the aspect of vehicle speed optimization control. Finally, the influence mechanism of the road speed limit constraint on the vehicle speed control is analyzed. This paper realizes the design of the optimal speed control algorithm for energy consumption under various constraints. The results can be used to guide the energy-saving driving of vehicles, and provide theoretical guidance for ecological driving technology in the context of intelligent transportation.</div></div>

This paper discusses Smith predictor scheme for controlling processes with time delay. The tuning method used here had improved capacities of smith predictor designed by Liu et.al for reference input tracking in presence of load disturbances. A new optimization technique, Grey Wolf Optimization Based disturbance estimator had been proposed here and has been used for tuning the parameters of the disturbance estimator and the same optimization algorithm has been used for optimizing the set point tracking controller for the plant. Comparison is done between Harmony search, Ant Colony and Particle Swarm Optimization for estimating the same parameters. Simulation examples are included to show the superiority of the proposed estimator over Maclaurin PID disturbance estimator proposed by Liu et. al as well as the other optimizing algorithm quoted above.

In wireless communication, multiple signals are utilized to receive and send information in the form of signals simultaneously. These signals consume little power and are usually inexpensive, with a high data rate during data transmission. An Multi Input Multi Output (MIMO) system uses numerous antennas to enhance the functionality of the system. Moreover, system intricacy and power utilization are difficult and highly complicated tasks to achieve in an Analog to Digital Converter (ADC) at the receiver side. An infinite number of MIMO channels are used in wireless networks to improve efficiency with Cross Entropy Optimization (CEO). ADC is a serious issue because the data of the accepted signal are completely lost. ADC is used in the MIMO channels to overcome the above issues, but it is very hard to implement and design. So, an efficient way to enhance the estimation of channels in the MIMO system is proposed in this paper with the utilization of the heuristic-based optimization technique. The main task of the implemented channel prediction framework is to predict the channel coefficient of the MIMO system at the transmitter side based on the receiver side error ratio, which is obtained from feedback information using a Hybrid Serial Cascaded Network (HSCN). Then, this multi-scaled cascaded autoencoder is combined with Long Short Term Memory (LSTM) with an attention mechanism. The parameters in the developed Hybrid Serial Cascaded Multi-scale Autoencoder and Attention LSTM are optimized using the developed Hybrid Revised Position-based Wild Horse and Energy Valley Optimizer (RP-WHEVO) algorithm for minimizing the "Root Mean Square Error (RMSE), Bit Error Rate (BER) and Mean Square Error (MSE)" of the estimated channel. Various experiments were carried out to analyze the accomplishment of the developed MIMO model. It was visible from the tests that the developed model enhanced the convergence rate and prediction performance along with a reduction in the computational costs.

ABSTRACTA controller which ensures the driving stability of a four-wheel-independent-drive electric vehicle (4WID-EV) is designed in this paper. The controller is structurally hierarchically designed. In order to keep the 4WID-EV running steadily, an upper-level controller integrating the active front-wheel steering control method (AFS) and direct yaw moment control method (DYC) is designed to keep the sideslip angle and yaw rate tracking the ideal values. A non-smooth control method is used to improve the closed-loop system's convergence and anti-disturbance performance. The additional yaw moment generated by the upper-level controller is distributed to four driving wheels by the lower-level controller. An optimal control algorithm is used in the lower-level controller to achieve the minimum sum of tire utilisation, and ensure the power performance and driving stability of the 4WID-EV. In order to verify the effectiveness of the designed controller, a simulation model of the stability control system is established based on Carsim-Matlab/Simulink. And the simulation is performed under double lane change road considering the disturbances. The results of the simulation show that the 4WID-EV with the designed controller achieves smaller sideslip angle than sliding-mode control and the actuator chatter is slight. Then the stability and safety of the 4WID-EV are greatly improved.