Hierarchical optimization control based on crop growth model for greenhouse light environment

Hierarchical optimal force-position control of complex manufacturing processes

Optimal control methods have gained significant attention due to their promising performance in nonlinear systems. In general, an optimal control method is regarded as an optimization process for solving the optimal control laws. However, for uncertain nonlinear systems with complex optimization objectives, the solving of optimal reference trajectories is difficult and significant that might be ignored to obtain robust performance. For this problem, a double-closed-loop robust optimal control (DCL-ROC) is proposed to maintain the optimal control reliability of uncertain nonlinear systems. First, a double-closed-loop scheme is established to divide the optimal control process into a closed-loop optimization process that solves optimal reference trajectories and a closed-loop control process that solves optimal control laws. Then, the ability of the optimal control method can be improved to solve complex uncertain optimization problems. Second, a closed-loop robust optimization (CL-RO) algorithm is developed to express uncertain optimization objectives as data-driven forms and adjust optimal reference trajectories in a close loop. Then, the optimality of reference trajectories can be improved under uncertainties. Third, the optimal reference trajectories are tracked by an adaptive controller to derive the optimal control laws without certain system dynamics. Then, the adaptivity and reliability of optimal control laws can be improved. The experimental results demonstrate that the proposed method can achieve better performance than other optimal control methods.

Optimal control strategies are very useful in manipulation problems of robots. However, most optimal controls are highly sensitive to plant-parameter variations and disturbances because optimal controls try to pick up exact information from the mathematical models in order to realize the best performance. We recognize that difficulties are involved in exact modeling of plants and prevention of disturbances. Therefore, we need a optimal control method with low sensitivity. The ideas of closed-loop control system design using LQG/TR techniques (linear quadratic Gaussian control with transfer recovery) are discussed in this paper. We combine optimal controls and LQG/TR control into two-degrees-of-freedom control systems to reduce the sensitivities of optimal control strategies. In digital simulations, we obtained excellent results of a two-link manipulator in an optimal time control problem using our method. Some modeling errors and considerable disturbances are allowed in the method. Thus, we can realize good performances in practical manipulation problems using our method.

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.

Hybrid evolutionary robust optimization-based optimal control for time-delay nonlinear systems

Off-policy algorithm based Hierarchical optimal control for completely unknown dynamic systems

In this paper, the problem of the robust optimal control (ROC) for a class of nonlinear systems with uncertainties and external disturbances is studied. For this purpose, the State Dependent Riccati Equation (SDRE) is considered as a solution of the ROC. The proposed method is to transform a robust control problem into an optimal control problem, where the uncertainties and disturbances are reflected in the performance index. Hence, the performance index is selected as an energy function and using the proposed constraints in this paper the Lyapunov stability for this class of nonlinear systems is proved and the robust optimal controller is designed. In order to illustrate the high capability of the ROC method in optimal control of complex systems such as Hyper-chaotic system, the time response of state variables in the proposed method and the Global Robust Optimal Sliding Mode Control (GROSMC) method are compared together. The simulation results show that the time response of state variables in the ROC method is more sufficient than the GROSMC method.

This paper describes optimal voltage control and optimal placement of the three-phase individual step voltage regulator (3φSVR) considering voltage unbalance improvement. As a result of active efforts to promote renewable energy, there is a concern that voltage unbalance will increase due to an increase in distributed power sources. Therefore, this paper proposes the optimal control and placement method for 3φSVR for voltage unbalance improvement and loss minimization. Simulations verified that all the voltage unbalanced indices satisfied the constraint value and the objective function improved. These results confirmed that the effectiveness of the optimal control and placement method for 3φSVR.

This paper investigates optimal active control schemes applied to a point absorber wave energy converter within a receding horizon fashion. A variational formulation of the power maximization problem is adapted to solve the optimal control problem. The optimal control method is shown to be of a bang-bang type for a power take-off mechanism that incorporates both linear dampers and active control elements. We also consider a direct transcription of the optimal control problem as a general nonlinear program. A variation of the projected gradient optimization scheme is formulated and shown to be feasible and computationally inexpensive compared to a standard NLP solver. Since the system model is bilinear and the cost function is non-convex quadratic, the resulting optimization problem is not a convex quadratic program. Results will be compared with an optimal command latching method to demonstrate the improvement in absorbed power. Time domain simulations are generated under irregular sea conditions.

The reduction of drag force and suppression of vortex shedding on a circular cylinder in electrically poorly-conducting water can be realized by utilizing the Lorentz forces. In order to control the cylinder wake effectively, three kinds of control approach, i.e. open-loop, closed-loop and optimal control methods, are proposed and discussed in the present paper. Constant Lorentz forces are used in the open-loop control process, and Lorentz forces are varied to target the zero total drag or the zero pressure drag coefficients during the control process in two closed-loop control procedures. The purpose of optimal flow control is to determine controls that effectively tailor the flow, and the key element of an optimal flow control is the minimization of a cost functional that provides a quantitative measure of the objective. Numerical simulation of the theoretical models is performed in the exponential–polar coordinates system, from which it has been shown that the above three methods can suppress the vortex shedding successfully; moreover, during the control process, the optimal control needs less energy than the closed-loop control method targeting a zero total drag or pressure drag coefficients.

In this paper, we study the optimal control problem for Takagi–Sugeno (T-S) fuzzy systems with disturbances. Due to the presence of disturbances in the T-S fuzzy systems, a fuzzy optimal state feedback control approach is presented by employing a two-player zero-sum game. Since the analytical optimal control solutions and the worst-case disturbance policies of T-S fuzzy systems can be boiled down to solving the algebraic Riccati equations (AREs), which are difficult to be obtained directly, a model-free policy iteration (PI) learning algorithm is proposed to obtain their approximation solutions. It is proved that the developed fuzzy optimal state feedback controller can ensure the fuzzy systems to be asymptotically stable and satisfy the disturbance attenuation condition simultaneously. Also, the designed PI learning algorithm can converge to their optimal solutions. Finally, we apply the developed fuzzy optimal state feedback control method to the truck-trailer system and the simulation results demonstrated the effectiveness of the developed scheme.

Pseudospectral approximation techniques have been shown to provide effective and flexible methods for solving optimal control problems in a variety of applications. In this paper, we provide the conditions for the convergence of the pseudospectral method for general nonlinear optimal control problems. Further, we show that this proof is directly extendible to the multidimensional pseudospectral method for optimal ensemble control of a class of parameterized dynamical systems. Examples from quantum control and neuroscience are included to demonstrate the method.

SummaryThis paper proposes an optimal control method of an unmanned aerial helicopter (UAH) with unknown disturbances. Solving the Hamilton‐Jacobi‐Bellman (HJB) equation is considered as the common approach to design an optimal controller under a meaningful cost function when facing the nonlinear optimal control problem. However, the HJB equation is hard to solve even for a simple problem. The inverse optimal control method that avoids the difficulties of solving the HJB equation has been adopted. In this inverse optimal control approach, a stabilizing optimal control law and a particular cost function that are obtained by a control Lyapunov function are required. An integrator backstepping method is used in designing the optimal control law of the UAH. Furthermore, a disturbance‐observer–based control (DOBC) approach has been adopted in the optimal control law for dealing with the unknown disturbances of the UAH system. Simulation results have been given to certify the stability of the nonlinear UAH system and the validity of this developed control method.

This paper presents a now method of optimal digital PID feedback control for a linear multivariate system by using a, parameter optimization technique. The most difficult points for the design of optimal digital PID feedback control system by the modern control theory are : (1) the control structure of it is specified in advance, (2) unless all the state variables are fed back, the optimal control cannot be obtained by dynamic programming or the maximum principle. However, the optimal digital PID feedback gains are determined by using the parameter optimization technique in this paper. When there are some states which are not fed back in the control system, the optimal feedback gains depend on the initial states. First, the optimal solution is presented in the case where all the initial Btates are known. Next, it is extended to the case where the initial states are random variables and their statistical properties are known,

In Part I of this work, the dynamic model of drop-induced shock mitigation based on magnetorheological (MR) energy absorber (EA) was deduced. The shock mitigation control effectiveness of constant force control method and optimal Bi number control method were compared based on Bingham model and resistor-capacitor (RC) operator-based hysteresis model. In this part, to further explore the possibility of the application of MREA in the shock mitigation control system, a single-degree-of-freedom (SDOF) shock mitigation control system is built with MREA connected in parallel with a coil spring, which is then connected in series with a vehicle tire to build a one-fourth vehicle suspension shock mitigation control system. Next, the constant force control method and optimal Bi number control method are extended to the SDOF shock mitigation control system. Based on the concept of the optimal Bi number, a two-degree-of-freedom (2DOF) optimal Bi number control method applied to the one-fourth vehicle suspension system is proposed by decoupling and recoupling the damping and stiffness of the vehicle suspension system. Using the control methods conducted in Part I, the motion state of the drop-induced shock mitigation process is simulated and analyzed. The control performances of the control methods in the SDOF shock mitigation control system are compared. Besides, the average velocity change rate (AVCR) and velocity-acceleration conversion ratio (V-ACR) are used to evaluate the effectiveness and stability of the control methods. Finally, the conclusion that the 2DOF optimal Bi number control method can effectively realize the shock mitigation control of a one-fourth vehicle suspension system is verified.