一种非线性系统的自适应无模型预测控制方法

为了避免基于模型的控制方法在控制非线性系统时存在建模困难和模型失配的问题,提出一种非线性系统的自适应无模型预测控制方法。该方法首先将非线性系统转化为由一组伪偏导数描述的线性系统,然后利用一种改进的投影算法在线估计这组伪偏导数,得到被控系统的泛模型。根据得到的泛模型,推导出预测模型,在此基础上根据预测控制滚动的优化策略求解二次目标函数得出最优控制律。通过对CSTR过程进行仿真验证,结果表明该方法具有良好的跟踪性能和较强的鲁棒性。     

Constrained incremental predictive controller design for a flexible joint robot

In this paper, an improved predictive control algorithm for controlling a typical nonlinear flexible-joint robot (FJR) with input constraint is proposed. The receding horizon algorithm, called generalized incremental predictive control (GIPC), utilizes both present and previous states rather than present states only. The GIPC algorithm includes the weighted difference of the current and the previous states and the summation of the control action increments. In order to illustrate the effectiveness of the proposed control strategy, it is implemented to the FJR and the results are compared with those of generalized predictive control (GPC). It is demonstrated that the proposed GIPC algorithm is more robust than the standard GPC method. Furthermore, the constrained GIPC algorithm using the quadratic programming removes instabilities caused by actuator saturation.     

Receding horizon online optimization for torque control of gasoline engines

This paper proposes a model-based nonlinear receding horizon optimal control scheme for the engine torque tracking problem. The controller design directly employs the nonlinear model exploited based on mean-value modeling principle of engine systems without any linearizing reformation, and the online optimization is achieved by applying the Continuation/GMRES (generalized minimum residual) approach. Several receding horizon control schemes are designed to investigate the effects of the integral action and integral gain selection. Simulation analyses and experimental validations are implemented to demonstrate the real-time optimization performance and control effects of the proposed torque tracking controllers.     

Game optimal receding horizon guidance laws and its equivalence to receding horizon guidance laws

In this paper, a game optimal receding horizon guidance law (GRHG) is proposed, which does not use information of the time-to-go and target maneuvers. It is shown that by adjusting design parameters appropriately, the proposed GRHG is identical to the existing receding horizon guidance law (RHG), which can intercept the target by keeping the relative vertical separation less than the given value, within which the warhead of the missile is detonated, after the appropriately selected time in the presence of arbitrary target maneuvers and initial relative vertical separation rates between the target and missile. Through a simulation study, the performance of the GRHG is illustrated and compared with that of the existing optimal guidance law (OGL).     

Fuzzy model predictive control of non-linear processes using convolution models and foraging algorithms

In this paper, a fuzzy model predictive control (FMPC) approach is introduced to design a control system for nonlinear processes. The proposed control strategy has been successfully employed for representative, benchmark chemical processes. Each nonlinear process system is described by fuzzy convolution models, which comprise a number of quasi-linear fuzzy implications (FIs). Each FI is employed to describe a fuzzy-set based relation between control input and model output. A quadratic optimization problem is then formulated, which minimizes the difference between the model predictions and the desired trajectory over a predefined predictive horizon and the requirement of control energy over a shorter control horizon. The present work proposes to solve this optimization problem by employing a contemporary population-based evolutionary optimization strategy, called the Bacterial Foraging Optimization (BFO) algorithm. The solution of this optimization problem is utilized to determine optimal controller parameters. The utility of the proposed controller is demonstrated by applying it to two non-linear chemical processes, where this controller could achieve better performances than those achieved by similar competing controller, under various operating conditions and design considerations. Further comparisons between various stochastic optimization algorithms have been reported and the efficacy of the proposed approach over similar optimization based algorithms has been concluded employing suitable performance indices.     

On the stability of receding horizon control based on horizon size for linear discrete systems

In this study, stability conditions of receding horizon control (RHC) based on a horizon size are proposed for linear discrete systems. The proposed stability conditions present a relevant horizon size which can guarantee the stability of RHC even though a final state weighting matrix does not satisfy non-increasing monotonicity of optimal cost. Therefore, the possible range of the final state weighting matrix ensuring the stability of RHC is extended to zero and also it can be applied to the stability problems of other forms of model predictive control like the conventional stability conditions.     

Auto-tuning of PID controller parameters with supervised receding horizon optimization

In this paper, a novel two-layer online auto-tuning algorithm is presented for a nonlinear time-varying system. The lower layer consists of a conventional proportional-integral-derivative (PID) controller and a plant process, while the upper layer is composed of identification and tuning modules. The purpose of the upper layer is to find a set of optimal PID parameters for the lower layer via an online receding horizon optimization approach, which result in a time-varying PID controller. Through mathematical analysis, the proposed system performance is equivalent to that of a standard generalized predictive control. Simulation and experiment demonstrate that the new method has a better control system performance compared with conventional PID controllers.     

纯时延有约束增量型模型算法控制策略

针对纯时延和有约束的情况，应用目标规划作为优化策略，提出了一种增量型模型算法控制策略。该算法由于采用线性优化策略，和以二次规划作为优化策略的控制算法相比，具有明显的优越性。     

One-step receding horizon H(∞) control for networked control systems with random delay and packet disordering

The receding horizon H(∞) control (RHHC) problem is investigated in this paper for a class of networked control systems (NCSs) with random delay and packet disordering. A new model is proposed to describe the NCS with random delay which may be larger than one sampling period. The random delay is modeled as a Markov chain while the closed-loop system is described as a Markovian jump system. Sufficient conditions for the closed-loop NCS to be stochastically stable and the performance index to be upper bounded are derived by using the receding optimization principle. Furthermore, by solving a semi-definite programming (SDP) with linear matrix inequalities (LMIs) constraint, a piecewise-constant receding horizon H(∞) controller is obtained, and the designed piecewise-constant controller ensures that the closed-loop NCS achieves a prescribed H(∞) disturbance attenuation level. Finally, an illustrative example is given to demonstrate the effectiveness of the proposed method.     

Receding horizon tracking control for wheeled mobile robots with time-delay

In this paper, a receding horizon (RH) controller is developed for tracking control of wheeled mobile robots (WMRs) subject to nonholonomic constraint in the environments without obstacles. The problem is simplified by neglecting the vehicle dynamics and considering only the steering system. First, the tracking-error kinematic model is linearized at the equilibrium point. And then, it is transferred to an exact discrete form considering the time-delay. The control policy is derived from the optimization of a quadratic cost function, which penalizes the tracking error and control variables in each sampling time. The minimizing problem is solved by using the QP (quadratic programming) method taking the current error state as the initial value and including the velocity constraints. The performance of the control algorithm is verified via the computer simulations with several different predefined trajectories showing that the strategy is feasible. This paper was recommended for publication in revised form by Associate Editor Doo Yong Lee Kil To Chong (M’96) received the Ph.D. degree in mechanical engineering from Texas A&M University, College Station, in 1995. Currently, he is a Professor at the School of Electronics and Information Engineering, Chonbuk National University, Jeonju, Korea, and Head of the Mechatronics Research Center granted from the Korea Science Foundation. His research interests are in the areas of motor fault detection, network system control, time-delay systems, and neural networks. Chang Goo Lee was born in Chonju, South Korea on Dec., 1958. He received the B.S. and M.S., and Dr.Eng. degrees in Electrical Engineering from Chonbuk National University, South Korea, 1981, 1983 and 1990 respectively. He had been with ETRI as a senior researcher from 1983 to 1991. Since 1992, He has been with the School of Electronic and Information Engineering, Chonbuk National University where he is presently a Professor. His research interests include intelligent control, nonlinear control, and home network control. Yu Gao received the master’s degree in Electronics and Information from Chonbuk National University, Korea, in 2008. He got his bachelor’s degree in Physics from Soochow University, China, in 2005. Currently, he is a Ph.D. candidate in the School of Electronics and Information, Chonbuk National University, Korea. His research interests are in the area of the receding horizon control.     

A symplectic pseudospectral method for nonlinear optimal control problems with inequality constraints

A symplectic pseudospectral method based on the dual variational principle and the quasilinearization method is proposed and is successfully applied to solve nonlinear optimal control problems with inequality constraints in this paper. Nonlinear optimal control problem is firstly converted into a series of constraint linear-quadratic optimal control problems with the help of quasilinearization techniques. Then a symplectic pseudospectral method based on dual variational principle for solving the converted constrained linear-quadratic optimal control problems is developed. In the proposed method, inequality constraints which can be functions of pure state, pure control and mixed state-control are transformed into equality constraints with the help of parameteric variables. After that, state variables, costate variables and parametric variables are interpolated locally at Legendre-Gauss-Lobatto points. Finally, based on the parametric variational principle and complementary conditions, the converted problem is transformed into a standard linear complementary problem which can be solved easily. Numerical examples show that the proposed method is of high accuracy and efficiency.     

Predictive IP controller for robust position control of linear servo system

Position control is a typical application of linear servo system. In this paper, to reduce the system overshoot, an integral plus proportional (IP) controller is used in the position control implementation. To further improve the control performance, a gain-tuning IP controller based on a generalized predictive control (GPC) law is proposed. Firstly, to represent the dynamics of the position loop, a second-order linear model is used and its model parameters are estimated on-line by using a recursive least squares method. Secondly, based on the GPC law, an optimal control sequence is obtained by using receding horizon, then directly supplies the IP controller with the corresponding control parameters in the real operations. Finally, simulation and experimental results are presented to show the efficiency of proposed scheme.     

Intelligent semi-active vibration control of eleven degrees of freedom suspension system using magnetorheological dampers

A novel intelligent semi-active control system for an eleven degrees of freedom passenger car’s suspension system using magnetorheological (MR) damper with neuro-fuzzy (NF) control strategy to enhance desired suspension performance is proposed. In comparison with earlier studies, an improvement in problem modeling is made. The proposed method consists of two parts: a fuzzy control strategy to establish an efficient controller to improve ride comfort and road handling (RCH) and an inverse mapping model to estimate the force needed for a semi-active damper. The fuzzy logic rules are extracted based on Sugeno inference engine. The inverse mapping model is based on an artificial neural network and incorporated into the fuzzy controller to enhance RCH. To verify the performance of the NF controller (NFC), comparisons with existing semi-active techniques are made. The typical control strategy are linear quadratic regulator (LQR) and linear quadratic Gaussian (LQG) controllers with clipped optimal control algorithm, while inherent time-delay and non-linear properties of MR damper lie in these strategies. Simulation results demonstrated that the NFC has better control performance and less control effort than the optimal in improving the service life of the suspension system and the ride comfort of a car.     

Optimal Predictive Control for Path Following of a Full Drive-by-Wire Vehicle at Varying Speeds

The current research of the global chassis control problem for the full drive-by-wire vehicle focuses on the control allocation(CA) of the four-wheel-distributed traction/braking/steering systems. However, the path following performance and the handling stability of the vehicle can be enhanced a step further by automatically adjusting the vehicle speed to the optimal value. The optimal solution for the combined longitudinal and lateral motion control(MC)problem is given. First, a new variable step-size spatial transformation method is proposed and utilized in the prediction model to derive the dynamics of the vehicle with respect to the road, such that the tracking errors can be explicitly obtained over the prediction horizon at varying speeds. Second, a nonlinear model predictive control(NMPC) algorithm is introduced to handle the nonlinear coupling between any two directions of the vehicular planar motion and computes the sequence of the optimal motion states for following the desired path. Third, a hierarchical control structure is proposed to separate the motion controller into a NMPC based path planner and a terminal sliding mode control(TSMC) based path follower. As revealed through off-line simulations, the hierarchical methodology brings nearly 1700% improvement in computational efficiency without loss of control performance. Finally, the control algorithm is verified through a hardware in-the-loop simulation system. Double-lanechange(DLC) test results show that by using the optimal predictive controller, the root-mean-square(RMS) values of the lateral deviations and the orientation errors can be reduced by 41% and 30%, respectively, comparing to those by the optimal preview acceleration(OPA) driver model with the non-preview speed-tracking method. Additionally,the average vehicle speed is increased by 0.26 km/h with the peak sideslip angle suppressed to 1.9°. This research proposes a novel motion controller, which provides the full drive-by-wire vehicle with better lane-keeping and collision-avoidance capabilities during autonomous driving.     

Impact angle constrained three-dimensional integrated guidance and control for STT missile in the presence of input saturation

Considering a class of skid-to-turn (STT) missile with fixed target and constrained terminal impact angles, a novel three-dimensional (3D) integrated guidance and control (IGC) scheme is proposed in this paper. Based on coriolis theorem, the fully nonlinear IGC model without the assumption that the missile flies heading to the target at initial time is established in the three-dimensional space. For this strict-feedback form of multi-variable system, dynamic surface control algorithm is implemented combining with extended observer (ESO) to complete the preliminary design. Then, in order to deal with the problems of the input constraints, a hyperbolic tangent function is introduced to approximate the saturation function and auxiliary system including a Nussbaum function established to compensate for the approximation error. The stability of the closed-loop system is proven based on Lyapunov theory. Numerical simulations results show that the proposed integrated guidance and control algorithm can ensure the accuracy of target interception with initial alignment angle deviation and the input saturation is suppressed with smooth deflection curves.     

Extended state observer based output control for spacecraft rendezvous and docking with actuator saturation

This paper investigates the relative position tracking and attitude synchronization problem of a chaser spacecraft rendezvous and docking with an uncontrolled tumbling target in the presence of external disturbances and actuator saturation. By combining the extended state observer technique with backstepping control methodology, a robust output-feedback control strategy with no precise motion information of the tumbling target is proposed. Moreover, a particular Nussbaum-type function is introduced to compensate for the nonlinear terms arising for actuator saturation. Within the Lyapunov framework, it is then shown that the proposed control strategy can guarantee the relative position and attitude errors converge into small regions containing the origin. Finally, numerical simulations are carried out to verify the effectiveness of the designed control strategy.     

Optimal second order sliding mode control for nonlinear uncertain systems

In this paper, a chattering free optimal second order sliding mode control (OSOSMC) method is proposed to stabilize nonlinear systems affected by uncertainties. The nonlinear optimal control strategy is based on the control Lyapunov function (CLF). For ensuring robustness of the optimal controller in the presence of parametric uncertainty and external disturbances, a sliding mode control scheme is realized by combining an integral and a terminal sliding surface. The resulting second order sliding mode can effectively reduce chattering in the control input. Simulation results confirm the supremacy of the proposed optimal second order sliding mode control over some existing sliding mode controllers in controlling nonlinear systems affected by uncertainty.     

Non-singular terminal dynamic surface control based integrated guidance and control design and simulation

In this paper, a novel cascade type design model is transformed from the simulation model, which has a broader scope of application, for integrated guidance and control (IGC). A novel non-singular terminal dynamic surface control based IGC method is proposed. It can guarantee the missile with multiple disturbances fast hits the target with high accuracy, while considering the terminal impact angular constraint commendably. And the stability of the closed-loop system is strictly proved. The essence of integrated guidance and control design philosophy is reached that establishing a direct relation between guidance and attitude equations by “intermediate states” and then designing an IGC law for the obtained integrated cascade design model. Finally, a series of simulations and comparisons with a 6-DOF nonlinear missile that includes all aerodynamic effects are demonstrated to illustrate the effectiveness and advantage of the proposed IGC method.     

Experimental evaluation of HJB optimal controllers for the attitude dynamics of a multirotor aerial vehicle

The present paper is concerned with the design and experimental evaluation of optimal control laws for the nonlinear attitude dynamics of a multirotor aerial vehicle. Three design methods based on Hamilton-Jacobi-Bellman equation are taken into account. The first one is a linear control with guarantee of stability for nonlinear systems. The second and third are a nonlinear suboptimal control techniques. These techniques are based on an optimal control design approach that takes into account the nonlinearities present in the vehicle dynamics. The stability Proof of the closed-loop system is presented. The performance of the control system designed is evaluated via simulations and also via an experimental scheme using the Quanser 3-DOF Hover. The experiments show the effectiveness of the linear control method over the nonlinear strategy.     

Optimal second order sliding mode control for linear uncertain systems

In this paper an optimal second order sliding mode controller (OSOSMC) is proposed to track a linear uncertain system. The optimal controller based on the linear quadratic regulator method is designed for the nominal system. An integral sliding mode controller is combined with the optimal controller to ensure robustness of the linear system which is affected by parametric uncertainties and external disturbances. To achieve finite time convergence of the sliding mode, a nonsingular terminal sliding surface is added with the integral sliding surface giving rise to a second order sliding mode controller. The main advantage of the proposed OSOSMC is that the control input is substantially reduced and it becomes chattering free. Simulation results confirm superiority of the proposed OSOSMC over some existing.