Practical method for optimal control of robot manipulators. 1st Report. Control law and digital simulation.

Data mining approach for improving the optimal control of HVAC systems: An event-driven strategy

One of the major issues in systems biology is developing control theory for gene regulatory networks (GRNs). Particularly, an important objective is to develop therapeutic intervention strategies to alter regulatory dynamics of GRNs to avoid undesired or diseased states. Several optimal control strategies have been developed to find therapeutic intervention strategies for small (or medium) sized GRNs modeled as probabilistic Boolean control networks (PBCNs). However, due to the humongous nature of GRNs, we require an optimal control strategy that scales to large systems without posing any constraints on the network dynamics. In this article, we formulate the infinite horizon discounted cost optimal control problem by leveraging the Markov decision process (MDP) based PBCN structure to model GRNs. Further, we design a stationary optimal control strategy, thereby avoiding the states associated with a disease. An augmented state space is defined to facilitate the control design method. By exploiting Kullback–Leibler (KL) divergence and the log transformation of the cost function referred to as the desirability function, the optimal control is devised using the path integral (PI) approach. We propose a sampling‐based technique for the approximation of the PI and hence the optimal control of PBCNs. The sampling‐based strategy is amicable to parallel implementation, thereby addressing the optimal control problem of large‐scale PBCNs. Finally, the viability of the results in the article is shown by some illustrative examples.

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.

Adaptive optimal backstepping control for strict-feedback nonlinear systems with time-varying partial output constraints

In this paper, the engine idle speed control (ISC) problem is revisited with the l/sub 1/ optimization design methodology. A controller configuration consisting of a feedback linear quadratic Gaussian (LQG) optimal controller and a feedforward l/sub 1/ optimal controller is chosen to satisfy the design objectives. The main goal of this work is to demonstrate the application of the l/sub 1/ optimal control methodology on a standard automotive control problem and evaluate the advantages and disadvantages of the l/sub 1/ design method. Using the idle bypass valve as the only actuator, the l/sub 1/ optimal feedforward control is shown to be effective in reducing the idle speed errors at the expense of extra control energy being spent during the transient. The controller design process is described along with the simulation and experimental results on a 4.6L 2 valve V8 production engine. A method to address tradeoff issues in evaluating the performance of a feedforward control system is also discussed.

This paper presents an equivalence theorem for three different kinds of optimal control problems, which are optimal target control problems, optimal time control problems, and optimal norm control problems. Controlled systems in this study are internally controlled heat equations. With the aid of this theorem, we establish an optimal norm feedback law and build up some explicit algorithms for solutions of optimal norm and optimal time control problems.

The goethite iron-removal process is an important procedure to remove the iron ions from the zinc hydrometallurgy. However, as a coherent system with complex reaction mechanism, associated uncertainties, and interconnected adjacent reactors, it is difficult for the process to accurately control the ion concentration. Because a large amount of historical data can be obtained during the process, an optimal control algorithm based on off-policy reinforcement learning is proposed in this paper to overcome these difficulties. According to the historical data, the weights of neural network are learned offline, and the optimal control strategy is solved online. Firstly, a bounded function is introduced to define the maximum effect of the coherent system on the subsystem cost function and to extend the cost function of the nominal system, so that the decentralized guaranteed cost control problem can be expressed as the optimal control problem of the nominal system. Then, an approximate iterative control algorithm based on actor-critic structure is proposed. The actor and critic neural networks are used to approximate control strategies and cost functions respectively. To achieve complete off-line, a new neural network is added to the actor-critic structure to approximate a part of the unknown system structure, and the three neural network parameters are optimized by the state transition algorithm. Finally, the strategy update and strategy iteration operations are performed alternately to learn optimal control strategies. The effectiveness and flexibility of the proposed off-policy optimal control method is validated by data from a real industrial goethite iron-removal process.

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.

This study concentrates on a nonlinear deterministic mathematical model for the impact of pathogens on human disease transmission with optimal control strategies. Both pathogen-free and coexistence equilibria are computed. The basic reproduction number R0, which plays a vital role in mathematical epidemiology, was derived. The qualitative analysis of the model revealed the scenario for both pathogen-free and coexistence equilibria together with R0. The local stability of the equilibria is established via the Jacobian matrix and Routh-Hurwitz criteria, while the global stability of the equilibria is proven by using an appropriate Lyapunov function. Also, the normalized sensitivity analysis has been performed to observe the impact of different parameters on R0. The proposed model is extended into optimal control problem by incorporating three control variables, namely, preventive measure variable based on separation of susceptible from contacting the pathogens, integrated vector management based on chemical, biological control, ... etc. to kill pathogens and their carriers, and supporting infective medication variable based on the care of the infected individual in quarantine center. Optimal disease control analysis is examined using Pontryagin minimum principle. Numerical simulations are performed depending on analytical results and discussed quantitatively.

The pretransmission parallel hybrid electric vehicle (HEV) with a single electric motor requires relatively little changes from existing powertrain configurations. This configuration, however, has a challenging drivability issue during engine-starts because the electric motor must simultaneously provide the demanded propulsion torque and start the engine. Depending on the propulsion power level, such engine-start process may require a trade-off between drivability and quick start. The goal of this study is to find theoretical performance limits and corresponding optimal control strategies that achieve the balance between these two conflicting goals. We first develop a simplified parallel HEV powertrain model to predict the engine, clutch, and vehicle dynamics. Assuming that the clutch torque can be accurately estimated and perfectly cancelled, an optimal engine-start control problem is formulated to minimize engine-start time while supplying the driver demanded torque. This nonlinear constrained optimal control problem is solved both analytically and numerically. For some special cases, the optimization problem can be solved analytically to obtain a closed-form solution. For the numerical method, dynamic programming (DP) is used, and both analytical and numerical solutions show that selecting a proper level of constant clutch pressure is the key to achieve near-optimal drivability performance. Furthermore, the DP control policy is found to be time-invariant, and thus can be implemented in the form of a full state feedback controller.

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

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.

To find effective control of the duplex process of induction melting in conditions of uncertainties, a method was suggested that made it possible to obtain optimal controls for both stages: melting and thermal treatment. It is shown how the search for an optimal melting control strategy can be performed using the theory of statistical games. At the same time, it is selected which of the melting regimes will be the best with the existing provision of the shop with charge materials. The costs of melting are the total costs, consisting of: – costs for the selected technological mode of melting (including costs for materials), – costs from the potential rejection of castings due to the non-conformity of the chemical composition of the alloy to the specified – due to the incorrectly chosen melting mode, – costs from the downtime due to the fact that the necessary amount of metal from the furnace is not delivered to the conveyor. The choice of an optimal control strategy in accordance with the proposed procedure can remove uncertainty in the evaluation of input process variables if they are taken as indicators of the charge quality. To find the optimal control at the stage of the heat treatment, a multialternative description of the final state is proposed on the basis of solving the problem of ridge analysis. This makes it possible to remove the uncertainty in the estimation of the final state, which allows a lot of optimal solutions in the sense of achieving a given quality. It is shown that such approach makes it possible not only to synthesize the optimal controller of the temperature regime on the basis of an analysis of the system of differential equations describing the control object, but also the application of the Pontryagin maximum principle to search for optimal control of the thermal treatment. The proposed method allows to determine the optimal control in the sense of stabilization for a given process parameter of the duplex process of induction melting. The resulting solutions form the necessary logical conditions for the logic control unit for the control system of the duplex induction melting process.

Optimal Design and Control of Cyclically Operated Biological Wastewater Treatment Plants

In grid-tied photovoltaic (PV) generation systems, intelligent energy management is required to maximize its performance. In this article, a novel three-port energy router with optimized control is proposed for this application. The proposed converter can interface among three ports (PV source, battery, and dc-link) with high integration. The PV panel is connected to the battery through an interleaved boost structure on the primary side. The primary-side PV-battery system and secondary-side dc-link are connected through a dual-active-bridge (DAB) converter. The battery operates as an energy buffer to compensate for the power mismatch due to the intermittent nature of the PV source. According to the daily power profile and intelligent power management between the grid and battery, six operating modes are possible. Optimized control strategies are customized in different operating modes. The maximum-power-point-tracking (MPPT) of the PV panel can be always realized. The zero-voltage-switching (ZVS) condition for all <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mosfet</small> s, reducing circulating current over a wide range, and flexible energy flow among three ports are all taken into considerations in the design. To verify the proposed concept, a 500 W rated prototype is designed. The designed prototype exhibits high efficiency in various operating modes. The experimental results agree well with the theoretical analysis.