Optimal control of sugarscape agent‐based model via a PDE approximation model

There is no standard framework for solving optimization problems for systems described by agent‐based models (ABMs). We present a method for constructing individual‐level controls that steer the population‐level dynamics of an ABM towards a desired state. Our method uses a system of partial differential equations (PDEs) with control functions to approximate the dynamics of the ABM with control. An optimal control problem is formulated in terms of the PDE model to mimic the optimization goal of the ABM. Mathematical theory is used to derive optimal controls for the PDE model, which are numerically approximated and transformed for use in the ABM. We use the Sugarscape ABM, a prototype ABM that includes agent and environmental heterogeneity and accumulation of agent resources over time. We present a PDE model that approximates well the spatial, temporal, and resource dynamics of the Sugarscape ABM. In both models, control represents taxation of agent wealth with the goal to maximize total taxes collected while minimizing the impact of taxation on the population over a finite time. Solutions to the optimal control problem yield taxation rates specific to an agent's location and current wealth. The use of optimal controls (generated by the PDE model) within the ABM performed better than other controls we evaluated, even though some error was introduced between the ABM and PDE models. Our results demonstrate the feasibility of using a PDE to approximate an ABM for control purposes and illustrate challenges that can arise in applying this technique to sophisticated ABMs. Copyright © 2016 John Wiley & Sons, Ltd.     

Optimal control of an HIV immunology model

A system of ordinary differential equations, which describes the interaction of HIV and T‐cells in the immune system is utilized, and optimal controls representing drug treatment strategies of this model are explored. Two types of treatments are used, and existence and uniqueness results for the optimal control pair are established. The optimality system is derived and then solved numerically using an iterative method with a Runge–Kutta fourth order scheme. Copyright © 2002 John Wiley & Sons, Ltd.     

Relaxation methods for hyperbolic PDE mixed‐integer optimal control problems

The convergence analysis for methods solving partial differential equations constrained optimal control problems containing both discrete and continuous control decisions based on relaxation and rounding strategies is extended to the class of first order semilinear hyperbolic systems in one space dimension. The results are obtained by novel a priori estimates for the size of the relaxation gap based on the characteristic flow, fixed‐point arguments, and particular regularity theory for such mixed‐integer control problems. Motivated by traffic flow problems, a relaxation model for optimal flux switching control in conservation laws is considered as an application.     

Hamiltonian two‐degrees‐of‐freedom control of chemical reactors

A novel unified approach to two‐degrees‐of‐freedom control is devised and applied to a classical chemical reactor model. The scheme is constructed from the optimal control point of view and along the lines of the Hamiltonian formalism for nonlinear processes. The proposed scheme optimizes both the feedforward and the feedback components of the control variable with respect to the same cost objective. The original Hamiltonian function governs the feedforward dynamics, and its derivatives are part of the gain for the feedback component. The optimal state trajectory is generated online, and is tracked by a combination of deterministic and stochastic optimal tools. The relevant numerical data to manipulate all stages come from a unique off‐line calculation, which provides design information for a whole family of related control problems. This is possible because a new set of PDEs (the variational equations) allow to recover the initial value of the costate variable, and the Hamilton equations can then be solved as an initial‐value problem. Perturbations from the optimal trajectory are abated through an optimal state estimator and a deterministic regulator with a generalized Riccati gain. Both gains are updated online, starting with initial values extracted from the solution to the variational equations. The control strategy is particularly useful in driving nonlinear processes from an equilibrium point to an arbitrary target in a finite‐horizon optimization context. Copyright © 2010 John Wiley & Sons, Ltd.     

Optimal control measures for a susceptible‐carrier‐infectious‐recovered‐susceptible malware propagation model

Purposing to lessen malware propagation, this paper proposes optimal control measures for a susceptible‐carrier‐infectious‐recovered‐susceptible (SCIRS) epidemiological model formed by a system of ordinary differential equations. By taking advantage of real‐world data related to the number of reported cybercrimes in Japan from 2012 to 2017, an optimal control problem is formulated to minimize the number of infected devices in a cost‐effective way. The existence and uniqueness of the results related to the optimality system are proved. Overall, numerical simulations show the usefulness of the proposed control strategies in reducing the spread of malware infections.     

Numerical solution of a class of two‐dimensional quadratic optimal control problems by using Ritz method

In this paper, we focus on a class of a two‐dimensional optimal control problem with quadratic performance index (cost function). We are going to solve the problem via the Ritz method. The method is based upon the Legendre polynomial basis. The key point of the Ritz method is that it provides greater flexibility in the initial and non‐local boundary conditions. By using this method, the given two‐dimensional continuous‐time quadratic optimal control problem is reduced to the problem of solving a system of algebraic equations. We extensively discuss the convergence of the method and finally present our numerical findings and demonstrate the efficiency and applicability of the numerical scheme by considering three examples. Copyright © 2015 John Wiley & Sons, Ltd.     

Learning-based parametrized model predictive control for trajectory tracking

This article is concerned with the tracking of nonequilibrium motions with model predictive control (MPC). It proposes to parametrize input and state trajectories of a dynamic system with basis functions to alleviate the computational burden in MPC. As a result of the parametrization, an optimization problem with fewer variables is obtained, and the memory requirements for storing the reference trajectories are reduced. The article also discusses the generation of feasible reference trajectories that account for the system's dynamics, as well as input and state constraints. In order to cope with repeatable disturbances, which may stem from unmodeled dynamics for example, an iterative learning procedure is included. The approach relies on a Kalman filter that identifies the repeatable disturbances based on previous trials. These are then included in the system's model available to the model predictive controller, which compensates them in subsequent trials. The proposed approach is evaluated on a quadcopter, whose task is to balance a pole, while flying a predefined trajectory.     

Optimal fixed‐levels control for nonlinear systems with quadratic cost‐functionals

This paper is devoted to general optimal control problems (OCPs) associated with a family of nonlinear continuous‐time switched systems in the presence of some specific control constraints. The stepwise (fixed‐level type) control restrictions we consider constitute a common class of admissible controls in many real‐world engineering systems. Moreover, these control restrictions can also be interpreted as a result of a quantization procedure appglied to the inputs of a conventional dynamic system. We study control systems with a priori given time‐driven switching mechanism in the presence of a quadratic cost functional. Our aim is to develop a practically implementable control algorithm that makes it possible to calculate approximating solutions for the class of OCPs under consideration. The paper presents a newly elaborated linear quadratic‐type optimal control scheme and also contains illustrative numerical examples. Copyright © 2015 John Wiley & Sons, Ltd.     

Optimal control analysis of a mathematical model for unemployment

Unemployment is continuously increasing worldwide because of enormous increase in population. This paper attempts to propose an optimal control policy for a deterministic unemployment model. The model considers three states, namely, unemployment, employment, and newly created vacancies. Factors like retirement and death of employed persons, termination from job, and so forth are also included in the model. The optimal control analysis for proposed unemployment model is performed using Pontryagin's maximum principle. The conditions for optimal control of the unemployment problem with effective use of implemented policies to provide employment to unemployed persons and to create new vacancies are derived and analyzed. Copyright © 2015 John Wiley & Sons, Ltd.     

Inverse optimal control for asymptotic trajectory tracking of discrete‐time stochastic nonlinear systems in block controllable form

This paper concerns an inverse optimal control–based trajectory tracking of discrete‐time stochastic nonlinear systems. It is assumed that the nonlinear system can be transformed to the so called nonlinear block controllable form. Additionally, the synthesized control law minimizes a cost functional, which is posteriori determined. In contrast to the optimal control technique, this scheme avoids to solve the stochastic Hamilton‐Jacobi‐Bellman equation, which is not an easy task. Based on a discrete‐time stochastic control Lyapunov function, the proposed optimal controller is analyzed. The proposed approach is applied successfully to the two degrees‐of‐freedom helicopter with uncertainties in real time.     

A nonlinear model predictive control strategy for trajectory tracking of a four‐wheeled omnidirectional mobile robot

This paper presents a nonlinear model‐based predictive controller (NMPC) for trajectory tracking of a four‐wheeled omnidirectional mobile robot. Methods of numerical optimization to perform real‐time nonlinear minimization of the cost function are used. The cost function penalizes the robot's position error, the robot's orientation angle error, and the control effort. Experimental results of the trajectories following and the performances of the methods of optimization are presented. Copyright © 2007 John Wiley & Sons, Ltd.     

Optimal production rates in a deterministic two‐product manufacturing system

This paper is concerned with explicit optimal control for a deterministic manufacturing system consisting of a single reliable machine and producing two part types studied by Connolly (Master Thesis, Operations Research Center, MIT, Cmbridge, 1992) and Gershwin (Manufacturing Systems Engineering, Prentice‐Hall: Englewood Cliffs, NJ, 1994). Using the verification theorem, we show the uniqueness of the optimal control under some conditions. Copyright © 2000 John Wiley & Sons, Ltd.     

Optimal trajectory generation and robust flatness–based tracking control of quadrotors

This work proposes an optimal trajectory generation and a robust flatness–based tracking controller design to create a new performance guidance module for the quadrotor in dense indoor environments. The properties of the differential flatness, the B‐spline, and the direct collocation method are exploited to convert the constrained optimization problem into a nonlinear programming one, which can be easily resolved by a classic solver. After that, the obtained optimal reference trajectory is applied to the dynamic quadrotor model and two different flatness‐based controllers, namely, one based on feedback linearization and one based on feedforward linearization, are developed and compared to ensure the trajectory tracking despite the existence of disturbances and parametric uncertainties. Numerical simulation is executed to evaluate the proposed optimal trajectory generation approach and the robust tracking strategies. It turns out that the controller based on feedforward linearization outperforms the feedback linearization one in robustness and permits obtaining a performance guidance law for an uncertain quadrotor system.     

Optimal multi‐interval control of a cantilever beam by a recursive control algorithm

The optimal‐distributed control of a transversely vibrating cantilever beam is studied with the objective of minimizing the deflection and velocity in a given period of time with the minimum possible expenditure of force. The beam undergoes transient vibrations and is subject to given displacement and velocity initial conditions. The control is exercised by means of a transversely distributed force referred to as the control force. In the present study, a multi‐interval optimal control method is developed with the application of a maximum principle. The method consists of dividing the control duration into several intervals and using the maximum principle to obtain the optimality conditions at each interval. The explicit solutions for a cantilever beam are obtained by a recursive algorithm that takes the final conditions of the last interval as the initial conditions of the next interval. The formulation and the method of solution are suitable and convenient for digital computation. Numerical results are given, which compare the deflections, velocities and the control force under the optimal multi‐interval control with those under the optimal single‐interval control. Copyright © 2008 John Wiley & Sons, Ltd.     

Optimal integral sliding mode control for a class of uncertain discrete‐time systems

This paper considers the problem of sliding mode control for a class of uncertain discrete‐time systems. Firstly, an optimal control law for the nominal system is derived to satisfy linear quadratic performance index. And then, an optimal integral sliding surface is designed to ensure the robustness for sliding dynamics. By combining with the discrete reaching law, the existence condition of the sliding mode is proved, and the bandwidth of the quasi‐sliding mode is given. It is shown that the present method utilizes a lower control gain to attain stronger robustness and eliminate the chattering. Finally, illustrative simulation results are provided. Copyright © 2013 John Wiley & Sons, Ltd.     

Optimal control problems with Atangana‐Baleanu fractional derivative

In this paper, we study fractional‐order optimal control problems (FOCPs) involving the Atangana‐Baleanu fractional derivative. A computational method based on B‐spline polynomials and their operational matrix of Atangana‐Baleanu fractional integration is proposed for the numerical solution of this class of problems. With this numerical technique, the FOCPs are reduced to a system of equations which are solved for the unknown parameters with the help of Mathematica® software. Our results show the applicability and usefulness of the numerical technique.     

Optimal and sub‐optimal control in Dengue epidemics

This work concerns the application of the optimal control theory to Dengue epidemics. The dynamics of this insect‐borne disease is modelled as a set of non‐linear ordinary differential equations including the effect of educational campaigns organized to motivate the population to break the reproduction cycle of the mosquitoes by avoiding the accumulation of still water in open‐air recipients. The cost functional is such that it reflects a compromise between actual financial spending (in insecticides and educational campaigns) and the population health (which can be objectively measured in terms of, for instance, treatment costs and loss of productivity). The optimal control problem is solved numerically using a multiple shooting method. However, the optimal control policy is difficult to implement by the health authorities because it is not practical to adjust the investment rate continuously in time. Therefore, a suboptimal control policy is computed assuming, as the admissible set, only those controls which are piecewise constant. The performance achieved by the optimal control and the sub‐optimal control policies are compared with the cases of control using only insecticides when Breteau Index is greater or equal to 5 and the case of no‐control. The results show that the sub‐optimal policy yields a substantial reduction in the cost, in terms of the proposed functional, and is only slightly inferior to the optimal control policy. Copyright © 2001 John Wiley & Sons, Ltd.     

Design of robust‐optimal controllers with low trajectory sensitivity for uncertain Takagi–Sugeno fuzzy model systems using differential evolution algorithm

By integrating the robust stabilizability condition, the orthogonal‐function approach (OFA) and the Taguchi‐sliding‐based differential evolution algorithm (TSBDEA), an integrative computational approach is presented in this paper to design the robust‐optimal fuzzy parallel‐distributed‐compensation (PDC) controller with low trajectory sensitivity such that (i) the Takagi–Sugeno (TS) fuzzy model system with parametric uncertainties can be robustly stabilized, and (ii) a quadratic finite‐horizon integral performance index for the nominal TS fuzzy model system can be minimized. In this paper, the robust stabilizability condition is proposed in terms of linear matrix inequalities (LMIs). Based on the OFA, an algebraic algorithm only involving the algebraic computation is derived for solving the nominal TS fuzzy feedback dynamic equations. By using the OFA and the LMI‐based robust stabilizability condition, the robust‐optimal fuzzy PDC control problem for the uncertain TS fuzzy dynamic systems is transformed into a static constrained‐optimization problem represented by the algebraic equations with constraint of LMI‐based robust stabilizability condition; thus, greatly simplifying the robust‐optimal PDC control design problem. Then, for the static constrained‐optimization problem, the TSBDEA has been employed to find the robust‐optimal PDC controllers with low trajectory sensitivity of the uncertain TS fuzzy model systems. A design example is given to demonstrate the applicability of the proposed new integrative approach. Copyright © 2011 John Wiley & Sons, Ltd.