Computational Methods in Optimal Control Problems

In the new global economy, automation technologies play an important role in the industry, agriculture, transportation and daily life in the 4.0 era. Generally, studying the theory of automatic control involves the design of the dynamic control system. Students have to understand the system and be able to create mathematical models or models of control systems, both open-loop control system and closed-loop control systems, based on the knowledge of physics, mathematics, and differential equations. They have to use all knowledge to design a control system that provides the desired results by simulating the system with computer software, which is difficult to understand and practice their skills for real implementation. By using the teaching materials that emphasize hands-on practice along with theoretical studies, is very important in creating practical experience and skills. This research has developed learning materials in designing a basic automatic control system by systematically designing and building a controller with the appropriate actuators and sensors. The designing steps are clear and easy to understand include of application of control laws, programming the controller via microcontrollers, and using a Graphic User Interface (GUI) to display the response of the system. The learning materials can compare the results from the different control systems by adjusting the parameters of the dynamics system. Students can experiment with adjusting the parameters of the controller, which makes them understand the automatic control system through actual hands-on operations with the low cost and easy to produce teaching materials.

The increasing complexity associated with many modern engineering applications, including autonomous robot guidance and navigation, process control in sensor-rich environments, and control of biological systems, has far-reaching implications for control system design. As an example, reactive, embedded software systems, interacting among themselves and remote users over communication networks, introduce a whole new set of system-level challenges, and classic control design objectives such as stability, performance, and robustness are being complemented with a number of new questions. These include the cost of hardware implementation, measured for example not only by computational requirements such as speed and memory, but also by communication requirements such as available communication bandwidth. Moreover, the complexity associated with specifying the control procedures and with verifying the behavior of the closed-loop system increasingly plays a fundamental role, especially in safety-critical control systems arising in energy and transportation networks, and in medical applications.

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.

In the present survey paper, we discuss applications of differential and operator inclusions to some optimization and optimal control problems. The Filippov implicit function lemma is considered and its application to the optimization of a feedback control system governed by a semilinear differential equation in a Banach space is presented. We describe the construction of the oriented coincidence degree for a compact multivalued perturbation of a nonlinear Fredholm operator and apply it to an optimal control problem induced by an ordinary differential equation with the Hopf boundary condition. We study also an optimal feedback control problem for a mathematical model of the motion of weakly concentrated water polymer solutions.

The mathematical postulate of Carathéodory for the existence of the entropy function is shown to be a direct consequence of the physical postulate of the existence of a stable equilibrium state for any given set of state variables. Similarly, the existence of thermokinetic potential is shown to be a direct consequence of the physical postulate of the existence of a stable steady state for any given set of nonequilibrium state variables.

This article presents a unified approach to simultaneous solution of optimization and optimal control problems in batch distillation, operating under different modes of operation: variable, constant, or optimal reflux. The simplified, computationally efficient short‐cut method and a novel algorithm to solve the optimal control problems in batch distillation is the basis of this unified approach. The short‐cut method identifies the feasible region of operation essential for optimization and optimal control problems, and provides analytical partial derivatives of the model parameters crucial to the solution.The new algorithm for the solution of optimal combination of the maximum principle and NLP optimization techniques. It circumvents the problems associated with the maximum principle approach (iterative solution of a two‐point boundary value problem, undounded control variables, and inability to handle the simultaneous optimization and optimal control problem), and the coupled ODE discretization‐NLP optimization scheme for nonlinear models (higher system nonlinearities, multiplicity of solutions, sensitivity of convergence to initial guesses). This algorithm reduces the dimensionality of the problem, and the nature of the algorithm allows a common platform to optimal solutions of different operating conditions. This article also shows that different categories of the optimal control problems in batch distillation essentially involve the solution of the maximum distillate problem.

Advanced high quality radiation therapy techniques such as IMRT require an accurate delivery of precisely modulated fluence patterns to the target volume. Intrafractional motion, however, may considerably deteriorate the accuracy of the dose delivery. To compensate for these errors, a dynamic real-time capable MLC control system was designed and implemented which compensates during the delivery for the motion and deformation of the target volume. Specialized algorithms were developed which constantly optimize the MLC aperture based on online provided target information allowing the sparing of adjacent organs-at-risk from additional dose. These algorithms were implemented in a dynamic target tracking control system for the Siemens 160 MLCTM. Experiments using different phantom setups were performed to assess and quantify the quality of the tracking technique. The results showed that 2-dimensional target motion can be compensated for with the new control system. For a clinical IMRT dose distribution, the congruence within 2 % / 2 mm with a static reference delivery was increased from 18.75% to 76.79 %. Similar improvements were achieved for the delivery of a complete IMRT treatment fraction to a moving lung phantom. However, the quality of the tracking system is limited by the system’s latency times. The experiments proved that the tracking concept, the new algorithms and the dynamic control system allow to effectively compensate for target motion in real-time. It therefore can increase the accuracy and the quality of the treatment delivery.KeywordsAdaptive RadiotherapyTumor TrackingDynamic Multileaf CollimatorMotion Management

This paper builds up two equivalence theorems for different kinds of optimal control problems of internally controlled Schrodinger equations. The first one is an equivalence of the minimal norm and the minimal time control problems where the target sets are the origin of the state space. (The minimal time control problems are also called the first type of optimal time control problems.) The second one is an equivalence of three optimal control problems: optimal target control problems, optimal norm control problems, and the second type of optimal time control problems.

We have developed an unstable structure (a tumbler with joints) that has circular arc type bottoms as a model of a biped robot walking on rugged terrain. The attitude is controlled by using a small gyroscope. This paper describes the design of the feedback control system which controls the attitude by the support of one leg and the drive of the crotch joints, as the first step prior to a walking experiments for the robot. The basic feedback control system is a one-input two-output system. The H/sub /spl infin// control theory is used to design the compensator. The results of our experiment on the designed control system show that we have successfully stabilized the control of the tumbler system with joints, which is a basic research of biped walking robots.

Previous article Next article The Reduction of Certain Control Problems to an “Ordinary Differential” TypeJ. WargaJ. Wargahttps://doi.org/10.1137/1010036PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAbout Previous article Next article FiguresRelatedReferencesCited byDetails Stationarity conditions in optimal control problems class related to dynamical objects group control Cross Ref On the First- and Second-Order Conditions in Optimal Control Problems Related to Autonomous Objects Group lifecycle Cross Ref Optimal Control Problems with Time Delays: Constancy of the HamiltonianRichard B. Vinter25 July 2019 | SIAM Journal on Control and Optimization, Vol. 57, No. 4AbstractPDF (525 KB)On obtaining the stationarity conditions in an optimal control problem for a trajectory with a smooth contact with the phase boundary Cross Ref On one Method to Obtain Stationarity Conditions for an Optimal Control Problem Trajectory With a Smooth Boundary ContactIFAC-PapersOnLine, Vol. 51, No. 32 Cross Ref The Maximum Principle for Optimal Control Problems with Time DelaysA. Boccia and R. B. Vinter19 September 2017 | SIAM Journal on Control and Optimization, Vol. 55, No. 5AbstractPDF (407 KB)Problems with relaxed shifted controls and the pointwise maximum principleNonlinear Analysis: Theory, Methods & Applications, Vol. 75, No. 3 Cross Ref The Instability of Optimal Control Problems to Time DelayAlexey S. Matveev26 July 2006 | SIAM Journal on Control and Optimization, Vol. 43, No. 5AbstractPDF (340 KB)On the Characterization of Properly Relaxed Delayed ControlsJournal of Mathematical Analysis and Applications, Vol. 209, No. 1 Cross Ref Approximation of strongly relaxed minimizers with ordinary delayed controlsApplied Mathematics & Optimization, Vol. 32, No. 1 Cross Ref Jack Warga: In Appreciation14 July 2006 | SIAM Journal on Control and Optimization, Vol. 33, No. 2AbstractPDF (809 KB)Minimizing approximate original solutions for commensurate delayed controlsApplied Mathematics Letters, Vol. 7, No. 5 Cross Ref How to Properly Relax Delayed Controls Cross Ref A proper relaxation of shifted and delayed controlsJournal of Mathematical Analysis and Applications, Vol. 169, No. 2 Cross Ref Optimal controls and their discontinuities in quadratic problems of delay systemsIEEE Transactions on Automatic Control, Vol. 30, No. 7 Cross Ref Optimal Controls with PseudodelaysJ. Warga18 July 2006 | SIAM Journal on Control, Vol. 12, No. 2AbstractPDF (1227 KB)A conservative bound on the estimation error covariance matrix in the presence of correlated driving noise and correlated discrete measurement noiseJournal of Mathematical Analysis and Applications, Vol. 43, No. 2 Cross Ref References Cross Ref Volume 10, Issue 2| 1968SIAM Review History Submitted:18 October 1967Published online:18 July 2006 InformationCopyright © 1968 Society for Industrial and Applied MathematicsPDF Download Article & Publication DataArticle DOI:10.1137/1010036Article page range:pp. 219-222ISSN (print):0036-1445ISSN (online):1095-7200Publisher:Society for Industrial and Applied Mathematics

We prove the maximum principle for the problem outlined in the title with the aid (or more precisely in the class) of so-called sliding variations. INTRODUCTION The by-now classical maximum principle for the simplest nonlinear optimal control problem was formulated by Pontryagin and proved by Boltyanskii over 30 years ago (see [1, 2, 3] and also [8]). Since then, the maximum principle has been derived for various optimal control problems in an enormous number of studies. This is well-known and we do not give a full list of references here; we only mention [4-7, 9-24, 27-30]. In our view, the deepest and most thorough studies are those of Milyutin and Dubovitskii [9-14]. These authors advance very far in their development of the maximum . principle for problems with ordinary differential equations - first to problems with phase constraints [9] and then to the general problem with phase and arbitrary (in general, nonregular) mixed constraints [10-14]. These authors completely resolve the issue of deriving the necessary first-order conditions for these problems. Despite these achievements, we are still witnessing the publication of studies that derive the maximum principle for various problems which are particular cases of the general Dubovitskii-Milyutin problem. The authors of some of these studies possibly do not realize that they are rederiving well-established results.** This is partly due to the fact that, on the one hand, optimal control results are primarily interesting for applied engineers, while the references [9-14] are far from being accessible to every engineer, because they use a fairly complex mathematical apparatus which is not familiar to engineers; on the other hand, optimal control theory as such so far has failed to attract a sufficiently wide followign of mathematicians. "Optimizers" still feel that if a problem originates in engineering practice, then its solution (and preferably the process of its analysis) should remain in the domain of concepts which are familiar in the engineering environment (compare this situation with the totally different state of things in the theory of partial differential equations or in probability theory). This, in our view, accounts for repeated attempts to simplify the proof of the maximum principle. However, even if some studies achieve a certain simplification of the proof, this is the result of extreme simplification of the problem, and not an achievement of the particular method of analysis. The positive impact of these attempts is that they highlight the complexity of the problem. They have shown that the problem cannot be solved by elementary techniques, without invoking new methods (specifically, methods of functional analysis). We are thus forced to admit that the complexity of the work of Dubovitskii-Milyutin is not contrived: it is inherent to the complexity of the underlying problem. Yet there is a fairly wide class of problems for which the maximum principle can be derived relatively simply. These are the problems with pure phase constraints and so-called "regular" mixed constraints. They cover most of the problems considered in the literature. The maximum principle for these problems was derived by Milyutin and Dubovitskii back in the 1960s, immediately after their derivation of the maximum principle for problems with phase constraints [9]. However, it has never been published because, in the authors' opinion, the addition of regular mixed constraints to phase constraints did not constitute a significant advance.

In this paper, we consider a class of optimal control problems in which the system is to be determined in an optimal way. This class of problems involves the choice of a fixed number of switching time points which divide the system’s time horizon into a number of time periods. For each of these time periods, a subsystem is selected, from a finite number of given candidate subsystems, to run during that time periods. The choice of the switching points and the selection of the subsystems are carried out in such a way that a given cost functional is minimized. We consider only problems involving ordinary differential equations over a finite time horizon. A computational method is developed for solving these problems. In our method, the candidate subsystems are combined into a single system by considering their ‘linear combination’. By introducing suitable constraints on the coefficients of the linear combination and using a time rescaling technique, the original problem is transformed into an equivalent optimal control problem with system parameters. An algorithm is proposed for solving this transformed problem and the required gradient formulae are derived. To show the effectiveness of the method, a numerical example is solved.

In this paper, an iterative adaptive dynamic programming (ADP) algorithm is developed to solve the optimal cooperative control problems for residential multi-battery systems. To avoid solving high-dimensional optimal control problems, we first constrain all the batteries at their worst performance, which transforms the multi-input optimal control problem into a single-input one. Based on the worst-performance optimal control law, the optimal cooperative control law for the residential multi-battery systems is obtained, where in each iteration, only a single-input optimization problem is implemented. Finally, numerical results are given to illustrate the performance of the developed algorithm.

Programming control in industrial design is by its nature expert upon an example being Programmable Logic Controller (PLC). Such programmes are unsuitable for children or novices as they cannot understand how to use the programme. This research seeks to present tangible programming for a basic control system in new frameworks in engineering education for children. Such programmes could be for use in kindergartens, primary schools or general teaching where knowledge about basic control is required. Normally open-loop and closed-loop control system programming is taught at university and college level. This may be late as far as acquiring knowledge of basic control systems is concerned. Using tangible programming without a computer but instructions and interface, relay and motor could result in children in kindergartens and primary schools being able to programme open-looped control systems which mix chemicals or closed-loop control systems which control conveyor belts. However, the children would not be able to undertake programming using programmable control in a similar scenario.

Turnpike results hold a central position in the area of applied mathematics, offering profound insights into the long-time behavior of optimal control systems. They play an important role in guiding decision-making processes in dynamic systems. Often dynamic systems depend on uncertain data and/or parameters. In this paper, optimal boundary control problems for the transport equation with random initial data and random source term are considered. The paper begins with the numerical analysis of the turnpike phenomenon for an optimal boundary control problem with nonlinear time-varying feedback control, where the initial data are perturbed by randomized Fourier series and the source term is perturbed by a random variable. Further, an integral turnpike result is presented for the optimal controls and the corresponding optimal states of an optimal boundary control problem with random initial data. A turnpike result is also presented for an optimal boundary control problem governed by a transport equation with random source term, where optimal control, optimal expected state, and variance satisfy an integral turnpike inequality. The turnpike constant, the expected value, and the variance are further specified for Gaussian and uniformly distributed random variables.