溴氟菊酯防治大豆食心虫,大豆蚜,甘蓝夜蛾试验

１９９２～１９９５年用１０％溴氟菊酯乳油对大豆食心虫、大豆蚜、甘蓝夜蛾进行了小区试验。结果表明，该药对上述三种害虫具有良好的防效。ｌ０００倍液对大豆食心虫防治效果与２０％甲氰菊酯乳油２０００倍液相当，达到３９２．９％以上；用１０００～２０００倍液防治大豆蚜，药后７天防效达８８．５％以上；用５００～１５００倍液防治甘蓝夜蛾，药后７天均达到了８４．６％以上的防治效果。而且对作物安全。     

用DCS顺控实现电除尘自动控制

介绍一种对电除尘工序进行远程自动控制的方法。使用DCS顺序控制图，通过模块组态和内部编程，实现多台设备的自动控制，优化工艺过程，提高该工序的自动化水平。     

Nonlinear multiobjective model-predictive control scheme for wastewater treatment process

A nonlinear multiobjective model-predictive control (NMMPC) scheme, consisting of self-organizing radial basis function (SORBF) neural network prediction and multiobjective gradient optimization, is proposed for wastewater treatment process (WWTP) in this paper. The proposed NMMPC comprises a SORBF neural network identifier and a multiple objectives controller via the multi-gradient method (MGM). The SORBF neural network with concurrent structure and parameter learning is developed as a model identifier for approximating on-line the states of WWTP. Then, this NMMPC optimizes the multiple objectives under different operating functions, where all the objectives are minimized simultaneously. The solution of optimal control is based on the MGM which can shorten the solution time. Moreover, the stability and control performance of the closed-loop control system are well studied. Numerical simulations reveal that the proposed control strategy gives satisfactory tracking and disturbance rejection performance for WWTP. Experimental results show the efficacy of the proposed method.     

A virtual actuator and sensor approach for fault tolerant control of LPV systems

In this paper, a fault tolerant control (FTC) strategy using virtual actuators and sensors for linear parameter varying (LPV) systems is proposed. The main idea of this FTC method, initially developed for LTI systems, is to reconfigure the control loop such that the nominal controller could still be used without need of retuning it. The plant with the faulty actuator/sensor is modified adding the virtual actuator/sensor block that masks the actuator/sensor fault. The suggested technique is an active FTC strategy that reconfigures the virtual actuator/sensor on-line taking into account faults and operating point changes. The stability of the reconfigured control loop is guaranteed if the faulty plant is stabilizable/detectable. The LPV virtual actuator/sensor is designed using polytopic LPV techniques and linear matrix inequalities (LMIs). A two-tank system simulator is used to assess the performance of the proposed method. In particular, it is shown that the application of the proposed technique results in an improvement, in terms of performance, with respect to the LTI counterpart.     

Customizable hardware design of fuzzy controllers applied to autonomous car driving

Nowadays, final products often encompass a certain intelligence therein to deal with variation or lack of precision in the sensing input data. This intelligence is usually acquired via the utilization of existing soft techniques, such as artificial neural networks, genetic algorithms and fuzzy control, among others. Thus, it is profitable to have on-the-shelf shell scalable and adaptive hardware designs that implement these soft techniques. This availability allows for an immediate embedding of any of those designs onto final products. This usually entails a reduced time-to-market of the product. Process control is one of the many applications that took advantage of the fuzzy paradigm. In general, controllers are embedded into the controlled device. This paper presents a novel design of a reconfigurable efficient parallel architecture to implement fuzzy controllers on hardware with almost no design effort for final users. The proposed architecture is herein proven suitable for embedding. It is customizable, so it allows the setup and configuration of the controller parameters, and hence its use for any problem application. Two fuzzy controllers that model autonomous car driving are implemented and their cost and performance evaluated.     

Steady-state target optimization designs for integrating real-time optimization and model predictive control

In industrial practice, the optimal steady-state operation of continuous-time processes is typically addressed by a control hierarchy involving various layers. Therein, the real-time optimization (RTO) layer computes the optimal operating point based on a nonlinear steady-state model of the plant. The optimal point is implemented by means of the model predictive control (MPC) layer, which typically uses a linear dynamical model of the plant. The MPC layer usually includes two stages: a steady-state target optimization (SSTO) followed by the MPC dynamic regulator. In this work, we consider the integration of RTO with MPC in the presence of plant-model mismatch and constraints, by focusing on the design of the SSTO problem. Three different quadratic program (QP) designs are considered: (i) the standard design that finds steady-state targets that are as close as possible to the RTO setpoints; (ii) a novel optimizing control design that tracks the active constraints and the optimal inputs for the remaining degrees of freedom; and (iii) an improved QP approximation design were the SSTO problem approximates the RTO problem. The main advantage of the strategies (ii) and (iii) is in the improved optimality of the stationary operating points reached by the SSTO-MPC control system. The performance of the different SSTO designs is illustrated in simulation for several case studies.     

Consistent hierarchical economic NMPC for a class of hybrid systems using neighboring-extremal updates

A hierarchical two-layer control algorithm is developed for a class of hybrid (discrete-continuous dynamic) systems to support economically optimal operation of batch or continuous processes with a predefined production schedule. For this class of hybrid systems, the optimal control moves as well as the controlled switching times between two adjacent modes are determined online. In contrast to closely related schemes for integrated scheduling and control, the sequence of modes is not optimized. On the upper layer, the economic optimal control problem is solved rigorously by a slow hybrid economic model predictive controller at a low sampling rate. On the lower layer, a fast hybrid neighboring-extremal controller is based on the same economic optimal control problem as the slow controller to ensure consistency between both layers. The fast neighboring-extremal controller updates rather than tracks the optimal trajectories from the upper layer to account for disturbances. Consequently, the fast controller steers the process to its operational bounds under disturbances and the economic potential of the process is exploited anytime. The suggested two-layer control algorithm provides fully consistent control action on the fast and slow time-scale and thus avoids performance degradation and even infeasibilities which are commonly encountered if inconsistent optimal control problems are formulated and solved.     

JITL-based concentration control for semi-batch pH-shift reactive crystallization of l-glutamic acid

Although concentration control (C-control) strategy has been shown to give effective and robust control performance for batch cooling and semi-batch antisolvent crystallizations in recent years, no research work was reported concerning the potential application of conventional C-control for the more challenging semi-batch pH-shift reactive crystallization that is common in the process industries. To this end, this paper presents detailed analysis to find out that it is not feasible to apply the C-control to semi-batch pH-shift reactive crystallization. To circumvent this problem, a variant of C-control strategy by incorporating the Just-in-Time Learning (JITL) method to cope with strong process nonlinearity inherent in the pH-shift reactive crystallization is developed in this paper. Simulation results are presented to illustrate the proposed design and a comparison with conventional optimal control is made.     

L2 disturbance attenuation for highly dissipative nonlinear spatially distributed processes via HJI approach

For many practical industrial spatially distributed processes (SDPs), their dynamics are usually described by highly dissipative nonlinear partial differential equations (PDEs). In this paper, we address the L₂ disturbance attenuation problem of nonlinear SDPs using the Hamilton–Jacobi–Isaacs (HJI) approach. Firstly, by collecting an ensemble of PDE states, Karhunen–Loève decomposition (KLD) is employed to compute empirical eigenfunctions (EEFs) of the SDP based on the method of snapshots. Subsequently, these EEFs together with singular perturbation (SP) technique are used to obtain a finite-dimensional slow subsystem of ordinary differential equation (ODE) that accurately describes the dominant dynamics of the PDE system. Secondly, based on the slow subsystem, the L₂ disturbance attenuation problem is reformulated and a finite-dimensional H_∞ controller is synthesized in terms of the HJI equation. Moreover, the stability and L₂-gain performance of the closed-loop PDE system are analyzed. Thirdly, since the HJI equation is a nonlinear PDE that has proven to be impossible to solve analytically, we combine the method of weighted residuals (MWR) and simultaneous policy update algorithm (SPUA) to obtain its approximate solution. Finally, the simulation studies are conducted on a nonlinear diffusion-reaction process and a temperature cooling fin of high-speed aerospace vehicle, and the achieved results demonstrate the effectiveness of the developed control method.