OPARC: Optimal and Precise Array Response Control Algorithm—Part II: Multi-Points and Applications

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.

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%.

<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>

Promotion of pure electric vehicle (EV) has become a trend, of which the braking performance is very important. In this paper, by using a family of curves of the relationship between adhesion coefficient and sliding rate of an electric vehicle on different roads, to elect the sliding rate corresponding to the peak value of adhesion coefficient, and then the mathematical models of EV braking process are established and the dynamics differential equation is obtained. According to the optimal control theory, appropriate state vector and control vector based on the dynamic mathematical model of vehicle-road system are selected, and a quadratic performance index of the minimum error between actual output and expected output with the minimum energy consumption of the hydraulic braking system in time domain are proposed as well. Typical working condition parameters are selected as the initial condition to calculate series of control parameters of the optimal control model by Riccati matrix algebra equation. In MATLAB/simulink vehicle anti-lock braking system model is established, and by simulation graphs of wheel velocity response show that, the wheel velocity can satisfy the slide requirements well by using optimal feedback control algorithm for EV with more advanced ECU system.

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.

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.

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.

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.

In this paper, a hybrid algorithm for robot's path planning is proposed. This algorithm consists of the optimal control algorithm and PSO meta-heuristic algorithm. In this method, a two-point boundary value equation is derived through optimal control method and the initial solution is needed to solve it. To solve these equations, the PSO provides preliminary solutions and optimizing the solution of equations (robot path) with optimizing initial solution concerning cost function results in global optimization. So the formulation of proposed method is presented and it is used for path planning of a 4-DOF surgical robotic system. The results show that in this algorithm, the cost function does not depend on initial guess in comparison with optimal control and PSO methods.

Instantaneous optimal control with acceleration and velocity feedback

Fuzzy optimal tracking control for nonlinear underactuated unmanned surface vehicles

1 A Survey on Computational Optimal Control.- Issues in the Direct Transcription of Optimal Control Problems to Sparse Nonlinear Programs.- Optimization in Control of Robots.- Large-scale SQP Methods and their Application in Trajectory Optimization.- Solving Optimal Control and Pursuit-Evasion Game Problems of High Complexity.- 2 Theoretical Aspects of Optimal Control and Nonlinear Programming.- Continuation Methods In Boundary Value Problems.- Second Order Optimality Conditions for Singular Extremals.- Synthesis of Adaptive Optimal Controls for Linear Dynamic Systems.- Control Applications of Reduced SQP Methods.- Time Optimal Control of Mechanical Systems.- 3 Algorithms for Optimal Control Calculations.- Second Order Algorithm for Time Optimal Control of a Linear System.- An SQP-type Solution Method for Constrained Discrete-Time Optimal Control Problems.- Numerical Methods for Solving Differential Games, Prospective Applications to Technical Problems.- Construction of the Optimal Feedback Controller for Constrained Optimal Control Problems with Unknown Disturbances.- Repetitive Optimization for Predictive Control of Dynamic Systems under Uncertainty.- Optimal Control of Multistage Systems Described by High-Index Differential-Algebraic Equations.- A New Class of a High Order Interior Point Method for the Solution of Convex Semiinfinite Optimization Problems.- A Structured Interior Point SQP Method for Nonlinear Optimal Control Problems.- 4 Software for Optimal Control Calculations.- Automated Approach for Optimizing Dynamic Systems.- ANDECS: A Computation Environment for Control Applications of Optimization.- Application of Automatic Differentiation to Optimal Control Problems.- OCCAL: A mixed symbolic-numeric Optimal Control CALculator.- 5 Applications of Optimal Control.- A Robotic Satellite with Simplified Design.- Nonlinear Control under Constraints of a Biological System.- An Object-Oriented Approach to Optimally Describe and Specify a SCADA System Applied to a Power Network.- Near-Optimal Flight Trajectories Generated by Neural Networks.- Performance of a Feedback Method with Respect to Changes in the Air-Density during the Ascent of a Two-Stage-To-Orbit Vehicle.- Linear Optimal Control for Reentry Flight.- Steady-State Modelling of Turbine Engine with Controllers.- Shortest Paths for Satellite Mounted Robot Manipulators.- Optimal Control of the Industrial Robot Manutec r3.

Stability is indispensable to haptic interfaces for the simulation of a large variety of virtual environments. On a multi-degree of freedom (multi-DOF) haptic device, the passivity condition must be satisfied in both end-effector and joint space to achieve stable interaction. In this study, a conservative passivity condition is utilized for the stability such that guaranteeing the passivity at all joints is a sufficient condition for the passivity and then stability of the whole haptic system. An optimal posture control algorithm is developed to satisfy this passivity condition and maximize the stability performance of a redundant haptic device. The algorithm optimally adjusts the device postures, which are estimated by a Golden Section Search algorithm. The proposed control algorithm was experimentally implemented on a virtual sphere by using a 7-DOF redundant haptic device. Z-width stability metric was used to evaluate the performance of the proposed algorithm. The results show that the optimal posture control approach significantly improves the stability of the redundant haptic devices.

In this article, an optimal steady-state voltage control framework is developed based on a novel linear voltage-power dependence deducted from Gaussian process (GP) learning. Different from other point-based linearization techniques, this GP-based linear relationship is valid over a subspace of operating points and, thus, suitable for a system with uncertainties such as those in power injections due to renewables. The proposed optimal voltage control algorithms, therefore, perform well over a wide range of operating conditions. Both centralized and distributed optimal control schemes are introduced in this framework. The least-squares estimation is employed to provide analytical forms of the optimal control, which offer great computational benefits. Moreover, unlike many existing voltage control approaches deploying fixed voltage references, the proposed control schemes not only minimize the control efforts but also optimize the voltage reference setpoints that lead to the least voltage deviation errors with respect to such setpoints. The control algorithms are also extended to handle uncertain power injections with robust optimal solutions, which guarantee compliance with the voltage regulation standards. As for the distributed control scheme, a new network partition problem is cast, based on the concept of effective voltage control source (EVCS), as an optimization problem which is further solved using convex relaxation. Various simulations on the IEEE 33-bus and 69-bus test feeders are presented to illustrate the performance of the proposed voltage control algorithms and EVCS-based network partition.

With the development of UAV technology, the re-search of leader-follower formation tracking has been extensively studied due to its simple formation structure and communication connection. However, the high maneuverability and dynamics of UAVs make the formation tracking control much more complicated and difficult. In this paper, the optimal formation control algorithms are proposed for both near-equilibrium and general dynamic control cases considering the time-varying leader velocity and communication delays. In near-equilibrium control case, a linear optimal control strategy is proposed, and the function coefficient can be derived by using a backward recursion. Then, the result of the near-equilibrium case is extended to the general dynamic case where the leader moves at an uncertain time-varying velocity. In each sampling period, based on the updated state information of UAVs, the optimal formation control strategy can be obtained by the same algorithm design approach as the near-equilibrium case, and the function coefficient is the convergent value deduced by using a backward recursion manner.