Multiobjective predictive cruise control for connected vehicle systems on urban conditions with InPA‐SQP

The connected vehicle (CV) system is one of the most effective core technologies in intelligent transportation systems. In order to solve the optimal velocity prediction problem for a CV system on urban roads, a multiobjective predictive cruise controller (MOPCC) for vehicles in the CV system is proposed to coordinate multiple performances including safety, tracking ability, ride comfort, and fuel economy. Firstly, with the ad hoc wireless communication technology, the signal phase and timing information is obtained to calculate the feasible velocity range for improving mobility. Then, the optimal target velocity of vehicles is computed by minimizing the fuel economic polynomial models of the vehicle system. Secondly, in order to systematically cope with those multiple performances, the Utopia point method is applied to change the multiobjective optimization problem into Utopia tracking problem. Furthermore, the MOPCC problem is formulated and solved by a fast numerical algorithm, ie, integrated perturbation analysis and sequential quadratic programming. Finally, simulations are presented to demonstrate the effectiveness of the proposed method in terms of improved the multiple performances.     

A New Integral Critic Learning for Optimal Tracking Control with Applications to Boiler-Turbine Systems

Optimal control theory and reinforcement learning are gradually being used in the field of industrial control. In this article, a new optimal tracking control scheme for 160 MW boiler-turbine systems is proposed based on an online policy iteration integral reinforcement learning (IRL) method. Firstly, a self-learning state tracking control with a cost function is developed to deal with the optimal tracking control problems for the boiler-turbine nonlinear system. Then with a modified cost function, a policy iteration-based IRL method is introduced to obtain the optimal control law. Finally, the monotonicity and the convergence of the cost function is analyzed and the detailed implementation of the policy iteration-based IRL method is provided via neural networks. The simulation results show that the control of the boiler-turbine system can converge in a short time by using this online iterative method. Through a theoretical simulation case, it can be concluded that the proposed method is more advanced compared with the MPC method.     

Switching optimal adaptive trajectory tracking control of quantum systems

In this paper, a switching optimal adaptive controller for tracking a time‐dependent trajectory in finite‐dimensional closed quantum systems is proposed. The issue of intrinsic singularities in trajectory tracking control of quantum systems leads to a sharp rise in the control amplitude. To overcome this drawback, a switching optimal adaptive quantum controller is designed based on Lyapunov stability theory and optimal quantum control approach. A state‐dependent strategy is considered to select the switching signal. The new switching controller adjusts the quantum state so that its population traces the desired dynamic trajectory and simultaneously eliminates the effects of singularities and reduces the control amplitude. The proposed controller is tested successfully for population transfer in a 4‐level closed quantum system in a simulation experiment. Both issues of reduction of the tracking error and control intensity along with a significant decrease in the number of singular points are well illustrated by simulation experiment as the advantages of the proposed method.     

An optimal-multiphase homing methodology for powered parafoil systems

In order to achieve the precise and agile homing control of the powered parafoil system, an optimal-multiphase homing methodology is explored in this article. The proposed methodology is composed by a trajectory optimization method based on quantum genetic algorithm and a trajectory tracking method based on an improved active disturbance rejection control (ADRC). First, an improved optimization method combining the optimal and multiphase theory is proposed. The optimized trajectory consists of the simple trajectories, the standard lines and circles, while also satisfying the multiple constraints such as the wind disturbance, terrain avoidance, flared landing, and so on. Then, an improved active disturbance rejection controller is presented. For improving the agility of the system during the path switching and the disturbance rejection ability in windy environment, based on ADRC, a disturbance rejection control algorithm is designed for the horizontal controller of the system. By analyzing the aerodynamic characteristic of the system, the wind disturbance will be compensated previously with a feedforward compensation unit. It will be largely reduce the observation error of the extended state observer, while improving the control effect and the reaction speed. Finally, the hardware-in-the-loop simulation is presented to prove the effectiveness of the proposed methodology. The results show that the optimization method can be achieved successfully and the optimized trajectory can be tracked by the improved ADRC controller precisely and agilely. The landing error is less than 15 m with the proposed homing methodology.     

A gradient algorithm for solution of the optimal control problem for hybrid switching systems

In this article, an algorithm is presented for solving the optimal control problem for the general form of a hybrid switching system. The cost function comprises terminal, running and switching costs. The controlled system is an autonomous hybrid switching system with jumps either at some switching times or some time varying switching manifolds. The proposed algorithm is an extension of the first-order gradient method for the conventional optimal control problem. The algorithm requires a low computational effort. The system's dynamical equations together with a set of algebraic equations are solved at each iteration in order to find the descent direction. The convergence of algorithm is proved and examples are provided to demonstrate the efficiency of the algorithm for different types of hybrid switching system optimal control problems.     

An elite hybrid strategy for solar photovoltaic system based optimized cascade controller under uniform and partial shading conditions

A hybrid technique for maximum power point tracking (MPPT) for a photovoltaic (PV) system is proposed in this paper. The proposed hybrid system is combination of Wing suit Flying Search (WFS) and modified Transient search optimization (MTSO), therefore it is called WFS-MTSO method. The proposed controller has three processes: (i) to identify the operating level of photovoltaic (uniform or in PSC), (ii) to estimate the maximal power point using WFS technique, and (iii) to ensure the photovoltaic system runs on the estimated maximum power point (MPP) by MTSO optimized cascade controller. This method begins with a sense of irradiance and temperature. The proposed photovoltaic system has two components. The first one is WFS maximal power point tracking algorithm attain maximal power point. The second one is MTSO optimized cascade controller to force the photovoltaic system to activate at maximal power point. Here, the proposed hybrid technique is utilized at MPPT to diminish tracking error and oscillation across MPP for optimizing power output. The proposed optimized cascade control improves the system efficiency by averting interruptions previously they propagate to the system. Finally, the performance of proposed hybrid system is executed on MATLAB/Simulink working platform and the performances are compared with various existing approaches. The statistical matrices, like mean, median, and standard deviation is analyzed the tracking efficiency of the proposed WFS-MTSO approach.     

Design and application of multivariable robust optimal systems

In this paper, a design algorithm for quantitative robust optimal control of uncertain multivariable feedback systems is developed. By use of the frequency-domain approach, the two-degree-of-freedom Wiener–Hopf optimal control method and the quantitative feedback theory are employed to derive the linear quadratic optimal system design with quantitative performance robustness. Two frequency-dependent weighting matrices are shaped and two-degree-of-freedom compensators are designed to achieve quantitative robust optimal control. The proposed design algorithm is then applied to an AFTI/F-16 flight control system design to illustrate the design algorithm. © 1998 John Wiley & Sons, Ltd.     

A computational method for a general class of optimal control problems involving integrodifferential equations

In this paper we consider a general class of optimal control problems involving integrodifferential equations. The integral equation component is a Volterra integral equation with convolution kernel. A method is proposed to approximate the kernel which gives rise to a system of ordinary differential equations approximating the Volterra integral equation. Thus the optimal control problem is approximated by a sequence of standard optimal control problems involving only differential equations. Each of the approximate problems is solvable by any of the existing methods for standard optimal control problems, such as the gradient restoration algorithm and the control parametrization algorithm. Convergence analysis is also carried out to justify the proposed approximation. As an application we consider an optimal control problem involving production and marketing systems with distributed time lags. This problem is then solved numerically using the proposed method.     

Computation of optimal control trajectories using Chebyshev polynomials: parameterization,and quadratic programming

An algorithm is proposed to solve the optimal control problem for linear and nonlinear systems with quadratic performance index. The method is based on parameterizing the state variables by Chebyshev series. The control variables are obtained from the system state equations as a function of the approximated state variables. In this method, there is no need to integrate the system state equations, and the performance index is evaluated by an algorithm which is also proposed in this paper. This converts the optimal control problem into a small size parameter optimization problem which is quadratic in the unknown parameters, therefore the optimal value of these parameters can be obtained by using quadratic programming results. Some numerical examples are presented to show the usefulness of the proposed algorithm. Copyright © 1999 John Wiley & Sons, Ltd.     

Frequency-domain linear quadratic optimal system design with two-degree-of-freedom configuration

Using a parametric two-degree-of-freedom controller configuration, a frequency domain linear quadratic optimal system design is developed to achieve closed-loop stability and to minimize a quadratic cost function including control input minimization, model reference signal tracking, disturbance and noise rejection. By applying frequency-dependent weighting functions, the derived method can easily achieve reference signal tracking and frequency shaping. A flight control system and a robot system are considered as design examples to illustrate the validity of the proposed design method. © 1997 John Wiley & Sons, Ltd.     

Improved guidance algorithm considering terminal attitude constraints for spacecrafts via optimal control theory

Motivated by new aerospace applications, the condition of minimum-energy achieving high accuracy of both terminal orbital injection and attitude angle is required for better navigation and observation. The stability of guidance command also needs to be improved considering the control system. In this article, an optimal guidance algorithm with an advanced numerical method of spacecrafts is proposed. In order to ensure the continuity of the attitude angle and its terminal constraints, the thrust vector is treated as the state variable in the optimal control problems, and the rate of attitude angle change is regarded as the control variable. Then the optimal ascent problem of spacecrafts based on a nondimensional dynamical model is derived in detail, including performance index considering energy consumption, optimal conditions, and terminal conditions. To improve the computational efficiency of optimal ascent problems, a numerical method and a solution strategy are proposed. Simulation results show that the terminal attitude angle error of proposed method is much less than that of the traditional guidance method, and the continuity and stability of the guidance command is also better, which demonstrates the high accuracy and strong adaptability of the guidance algorithm developed in this article.     

Optimal spatial field control for controlled release

Most distributed parameter control problems involve manipulation within the spatial domain. Such problems arise in a variety of applications including epidemiology, tissue engineering, and cancer treatment. This paper proposes an approach to solve a state‐constrained spatial field control problem that is motivated by a biomedical application. In particular, the considered manipulation over a spatial field is described by partial differential equations (PDEs) with spatial frequency constraints. The proposed optimization algorithm for tracking a reference spatial field combines three‐dimensional Fourier series, which are truncated to satisfy the spatial frequency constraints, with exploitation of structural characteristics of the PDEs. The computational efficiency and performance of the optimization algorithm are demonstrated in a numerical example. In the example, the spatial tracking error is shown to be almost entirely due to the limitation on the spatial frequency of the manipulated field. The numerical results suggest that the proposed optimal control approach has promise for controlling the release of macromolecules in tissue engineering applications.     

Infinite horizon linear quadratic tracking problem: A discounted cost function approach

The discounted cost function approach is one of the main approaches to the infinite time horizon problems in economics and market. This paper introduces a causal and optimal solution based on the discounted cost function approach for infinite horizon linear quadratic tracking problem with disturbance rejection and the problem of disturbance tracking by presenting the theoretical foundations. It is shown that the proposed method has the ability to solve the problems where the reference inputs and disturbance signals are not asymptotically stable. Two numerical examples, a grid connection of voltage source power electronic converter as a SISO system and a load frequency control of a 2‐area nonreheat thermal power system as a MIMO example, are presented to illustrate the effectiveness of the proposed method.     

Realization of causal current output‐based optimal full/reducedorder observer and tracker for the linear sampled‐data system with a direct transmission term

Realization of causal current output‐based optimal full/reduced‐order observer and tracker for the linear sampled‐data system with a direct transmission term from input to output is proposed in this paper. First, a high‐gain optimal linear quadratic analog tracker based on the reduced‐order observer is proposed for the system model with a direct transmission term from input to output, so that it can effectively induce a high quality performance on state estimation and trajectory tracking design theoretically. Then, the prediction‐based digital redesign method enables it to make the digitally controlled system to track the desired trajectory closely. However, there induces a causal problem on the realization of current output‐based optimal full/reduced‐order observer and tracker for the sampled‐data system with a direct transmission term form input to measured output. To overcome this problem, a realization of the causal observer‐based sampled‐data tracker is newly proposed in this paper. An illustrative example is presented to demonstrate the effectiveness of the proposed method. Copyright © 2014 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.     

H∞ output tracking control over networked control systems with Markovian jumping parameters

The problem of H_∞ output tracking control over networked control systems (NCSs) with communication limits and environmental disturbances is studied in this paper. A wide range of time‐varying stochastic problem arising in networked tracking control system is reduced to a standard convex optimization problem involving linear matrix inequalities (LMIs). The closed‐loop hybrid NCS is modeled as a Markov jump linear system in which random time delays and packet dropouts are described as two stochastic Markov chains. Gridding approach is introduced to guarantee the finite value of the sequences of transmission delays from sensor to actuator. Sufficient conditions for the stochastic stabilization of the hybrid NCS tracking system are derived by the LMI‐based approach through the computation of the optimal H_∞ performance. The mode‐dependent robust H_∞ output tracking controller is obtained by the optimal iteration method. Numerical examples are given to demonstrate the effectiveness of the proposed robust output tracking controller for NCS. Copyright © 2016 John Wiley & Sons, Ltd.     

Nonfragile predictive control for an omnidirectional rehabilitative training walker with constraints on the tracking errors of position and velocity

Here, we propose a novel nonfragile predictive control method for an omnidirectional rehabilitative training walker. The aim of the study was to design a tracking controller that includes simultaneous constraints on the tracking errors in the position and velocity in order to guarantee safe system states for the omnidirectional walker. The nonfragile controller uses incremental control to solve the optimal quadratic program problem. The exponential stability of the system is guaranteed by constructing constraints on the trajectory-tracking error and the velocity-tracking error. Simulation and experimental testing were conducted to confirm the effectiveness of the proposed method and verify that it can provide safe positions and velocities when applied to control the motion of an omnidirectional walker.     

An optimized fuzzy continuous sliding mode controller combined with an adaptive proportional-integral-derivative control for uncertain systems

This article discusses the design of a hybrid fuzzy variable structure control algorithm combined with genetic algorithm (GA) optimization technique to improve the adaptive proportional-integral-derivative (PID) continuous second-order sliding mode control approach (APID2SMC), recently published in our previous article in the literature. In this article, first, as an improved extension to APID2SMC published recently in the literature, an adaptive proportional-integral-derivative fuzzy sliding mode scheme (APIDFSMC) is presented in which a fuzzy logic controller is added. Second, a GA-based adaptive PID fuzzy sliding mode control approach (APIDFSMC-GA) is introduced to obtain the optimal control parameters of the fuzzy controller in APIDFSMC. The proposed control algorithms are derived based on Lyapunov stability criterion. Simulations results show that the proposed approaches provide robustness for trajectory tracking performance under the occurrence of uncertainties. These simulation results, compared with the results of conventional sliding mode controller, APID2SMC, and standalone classical PID controller, indicate that the proposed control methods yield superior and favorable tracking control performance over the other conventional controllers.     

Optimal design of trajectory parameters and position tracking with balance for riderless bicycle

Riderless bicycles are typically nonholonomic, underactuated, and nonminimum‐phase systems. The instability and complex dynamic coupling make the trajectory generation and tracking of the bicycles more challenging. In this paper, we consider both the trajectory generation and position tracking of a riderless bicycle. To achieve smooth motion performances, the desired planar trajectory of the contact point of the bicycle's rear wheel is constructed using a parameterized polynomial curve that can connect two given endpoints with associated tangent angles. The optimal parameters of the polynomial curve are obtained by minimizing the maximum of the roll angle's quasistatic trajectory of the bicycle, and this problem is solved by the particle swarm optimization algorithm. Then, position tracking of the desired planar trajectory with balance is converted into an optimization problem subject to the dynamic constraints. The cost function is designed as the combination of the position errors and the roll acceleration of the bicycle, in order to achieve an accurate tracking performance and to prevent the bicycle from falling down. This optimization problem is solved by the Gauss pseudospectral method. Simulation results are presented to demonstrate the effectiveness of the proposed method. Copyright © 2014 John Wiley & Sons, Ltd.