Nonlinear-State Feedback Controller Design for Nonlinear Processes.

Notice of Violation of IEEE Publication Principles<br><br>"Internal model control based on locally linear model tree (LOLIMOT) model with application to a PH neutral process"<br>by M. Jalili-Kharaajoo, A. Rahmati and F. Rashidi,<br>in proceedings of the 2003 IEEE Internationa Conference on Systems, Man, and Cybernetics, vol 4, pp. 3051-3-55.<br><br>After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles.<br><br>This paper contains portions of original text from the paper cited below. The original text was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission.<br><br>"Nonlinear Internal Model Control for MISO Systems Based on Local Linear Neuro-Fuzzy Models"<br> by A. Fink, O. Nelles and R. Isermann,<br>in 15th IFAC World Congress, Barcelona, Spain, 2002.<br><br> <br/> The internal model control (IMC) scheme has been widely applied in the field of process control. So far, IMC has been mainly applied to linear processes. This paper discusses the extension of the IMC scheme to nonlinear processes based on local linear models where the properties of linear design procedures can be exploited. The IMC scheme results in controllers that are comparable to conventional multi layer perceptron (MLP) networks. In practice, the tuning of conventional MLP based controllers can be very time-consuming whereas the IMC design procedure is very simple and reliable. The design effort of the IMC based on locally linear model tree (LOLIMOT) algorithm are discussed and the control results are compared by application to nonlinear control of an industrial-scale PH neutralization process. Simulation studies of a PH neutralization process confirm the excellent nonlinear modeling properties of the proposed locally linear network and illustrate the potential for set point tracking and disturbance rejection within an IMC framework.

The internal model control (IMC) scheme has been widely applied in the field of process control. This is due to its simple and straightforward controller design procedure as well as its good disturbance rejection capabilities and robustness properties. So far, IMC has been mainly applied to linear processes. This paper discusses the extension of the IMC scheme to nonlinear processes based on local linear models where the properties of the linear design procedures can be exploited directly. The resulting controllers are comparable to gain-scheduled PI or PID controllers which are the standard controllers in process industry. In practice, the tuning of conventional PI or PID controllers can be very time-consuming. In this paper, the design effort of the nonlinear IMC and conventional controller design methods are discussed and the control results are compared by applying it to a Hammerstein process and nonlinear temperature control of a heat exchanger.

We consider methods for synthesizing robust controllers that can be directly implemented in the Internal Model Control (IMC) scheme. We show how to put H design methods into the IMC scheme to improve its robustness. These methods include one degree-of-freedom (one d.o.f.) and two degrees-of-freedom (two d.o.f.) H designs. Explicit state-space formulae for the H one d.o.f. and two d.o.f. IMC-based controllers are derived in discrete time. We show that the IMC controller solves a special H control problem, namely a disturbance feedforward control problem, and it proves robust stability against coprime factor uncertainty and with a degree of robust performance for the two d.o.f. case, in the sense of making the closed-loop system match a pre-specified reference model. A design for a glass tube production process is presented as an illustrative example.

Batch crystallizers are predominantly used in chemical industries like pharmaceuticals, food industries and specialty chemicals. The nonlinear nature of the batch process leads to difficulties when the objective is to obtain a uniform Crystal Size Distribution (CSD). In this study, a linear PI controller is designed using classical controller tuning methods for controlling the crystallizer outlet temperature by manipulating the inlet jacket temperature; however, the response is not satisfactory. A simple PID controller cannot guarantee a satisfactory response that is why an optimal controller is designed to keep the concentration and temperature in a range that suits our needs. Any typical process operation has constraints on states, inputs and outputs. So, a nonlinear process needs to be operated satisfying the constraints. Hence, a nonlinear controller like Generic Model Controller (GMC) which is similar in structure to the PI controller is implemented. It minimizes the derivative of the squared error, thus improving the output response of the process. Minimization of crystal size variation is considered as an objective function in this study. Model predictive control is also designed that uses advanced optimization algorithm to minimize the error while linearizing the process. Constraints are fed into the MPC toolbox in MATLAB and Prediction, Control horizons and Performance weights are tuned using Sridhar and Cooper Method. Performances of all the three controllers (PID, GMC and MPC) are compared and it is found that MPC is the most superior one in terms of settling time and percentage overshoot.

This work presents an analysis, design, and comparison of the PI (Proportional and Integral) controller and control schemes PI linear and nonlinear controllers reset-based. The parameters of these control schemes have been set by trial and error to finally be tested in a nonlinear process with variable dead time. Controller performance is measured using integral square error (ISE), integral time square error (ITSE), total variations of control efforts (TVU), maximum overshoot (\(M_{p}\)) and settling time \(\left( {t_{s} } \right)\). The results show that reset control systems overcome fundamental limitations coming from linear control systems.

As an important part of a power system, a load frequency control has to be prepared with a better controller to ensure internal frequency stability. In this paper, an Internal Model Control (IMC) scheme for a Load Frequency Control (LFC) with an adaptive internal model is proposed. The effectiveness of the IMC control has been tested in a three area power system. Results of the simulation show that the proposed IMC with Extreme Learning Machine (ELM) based adaptive model can accurately cover the power system dynamics. Furthermore, the proposed controller can effectively reduce the frequency and mechanical power deviation under disturbances of the power system.

In this paper, a novel decoupling control scheme combining least squares support vector machines (LSSVM) inverse models and 2-degree-of-freedom (DOF) internal model controllers (IMC) is employed in the decoupling control system of the bearingless permanent magnet synchronous motor (BPMSM). This scheme can be used to enhance the control properties of high-precision, fast-response, and strong-robustness for the BPMSM system, and effectively eliminate the nonlinear and coupling influence. It introduces LSSVM inverse models into the original BPMSM system to constitute a decoupled pseudo-linear system. In addition, the particle swarm optimization algorithm (PSO) is used to optimize parameters of the LSSVM, which improves its fitting ability and prediction accuracy. What is more, the internal model control scheme is used to design additional closed-loop controllers, thereby improving the robustness of the entire control system. Therefore, this scheme successfully combines the advantages of the LSSVM inverse models and the internal model controller. It can enhance the stability and the static as well as dynamic properties of the whole BPMSM system while independently adjusting the tracking and interference rejection performances. The effectiveness of the proposed scheme has been verified by simulation results at various operations.

Non-linear adaptive control of fermentation processes utilizing a priori modelling knowledge

An adaptive fuzzy internal model control (IMC) scheme is proposed to control nonlinear systems with inherent time delay. A Takagi-Sugeno (TS) fuzzy modeling with adaptive tuner is used to obtain the plant model. TS model is obtained using experimental data of step responses at different input levels and the model accuracy is improved using the normalized least mean squares (nLMS) based online adaptive tuner. Adaptive inverse of the plant is established using adaptive inverse control scheme on TS model with adaptive tuner. The scheme is implemented on a laboratory scale heating process. The experimental results indicate that the heating process output tracks the reference signal.

• Proposed a dynamic energy management system for hybrid sources in an electric vehicle. • Buck-boost H-bridge converters are introduced for bidirectional power flow control. • System non-linearities are addressed using a nonlinear state feedback controller. • The proposed system accurately delivers instantaneous vehicle speed and torque. This paper describes a methodology to control a multi-source battery-capacitor hybrid EV incorporating a dynamic power splitting strategy. Considering the requirement for precise speed control characteristics of the EV and the non-linear behavior of traction motor with system disturbances, a non-linear state feedback controller (NLSFC) with a disturbance observer is suggested as the speed control technique for the EV traction motor. To interface a parallel configured multi-source system, a non-inverted buck-boost H-bridge is proposed as the source side converter for effective recovery of the braking energy. Detailed control design for traction motor and converter is also presented for efficient control of EV. Besides, an adaptive energy management strategy considering power profile and dynamic source characteristics is incorporated into the overall control system. The simulation results from various case studies indicate that the proposed system can follow the required vehicle speed and torque despite the sudden change in EV load torque. Furthermore, the comparative analysis between NLSFC and PI controller shows improvement with steady-state error reduction from 0.43 to 0.0014 and peak overshoot drop from 12.07% to 3.51% during acceleration of the system when proposed NLSFC is adopted. The proposed solutions will be implemented in a prototype EV system in the future.

In this paper, a neural network approach for the identification and control of a separately excited direct (DC) motor (SEDCM) driving a centrifugal pump load is applied. In this application, two radial basis function neural networks (RBFNN) are used: The first is a RBFNN identifier trained offline to emulate the dynamic performance of the DC motor-load system. The second is a RBFNN controller, which is trained to make the motor speed follow a selected reference signal. Two RBFNN control schemes are proposed using direct inverse and internal model control schemes. The performance of the RBFNN identifier and controller is investigated in terms of step response, sharp changes in speed trajectory, and sudden load change, as well as changes in motor parameters. The performance of RBFNN in system identification and control has been compared with the performance of the well-known back-propagation neural network (BPNN). The simulation results show that both of the BPNN and RBFNN controllers exhibit excellent dynamic response, adapt well to changes in speed trajectory and load connected to the motor, and adapt to the variations of motor parameters. Furthermore, the simulation results show that the step response of RBFNN internal model and direct inverse controllers are identical.

Data-driven MIMO model-free reference tracking control with nonlinear state-feedback and fractional order controllers

&lt;div class="htmlview paragraph"&gt;As a result of the increased complexity of today's power trains, the traditional ways of designing engine control systems essentially through ad hoc methods and experimental tuning will no longer provide the desired level of performance. Further, it is too time-consuming due to the calibration process. In this paper, a novel model-based controller is described which accommodates many of today's demands on controller development of the automotive industry.&lt;/div&gt; &lt;div class="htmlview paragraph"&gt;The control problem treated here is a boost pressure control of a turbocharged diesel engine with a variable nozzle turbine (VNT). Depending on the injected fuel and the current speed of the diesel engine, the boost pressure has to follow a desired trajectory. Since the system is essentially nonlinear, a robust nonlinear controller is used.&lt;/div&gt; &lt;div class="htmlview paragraph"&gt;The tracking problem is treated by a control method which combines the Internal Model Control (IMC) structure with the flatness-based approach to design feedforward controllers. The main idea of IMC is to include the model of the plant into the feedback controller. If the model perfectly represents the plant and no disturbances occur, the IMC structure degenerates to a pure feedforward control. Flat systems are characterized by the fact that their input can be expressed explicitly in terms of internal system dynamics, which results in a simple method for designing a feedforward controller. In order to extend this concept to nonlinear systems, a flatness-based feedforward controller is proposed as IMC controller. It is shown that this new concept provides an efficient controller design for certain nonlinear systems and ensures robustness and offset-free tracking. Simulations and testbed results on a turbocharged diesel engine demonstrate the feasibility of this control scheme which results in impressive control performance.&lt;/div&gt;

Nonlinear control of a simulated industrial evaporation process

The ramifications of incorporating neural networks into two model-based control architectures, namely internal model control (IMC) and model predictive control (MPC), are considered. The development of a neural network analog to the conventional IMC design is described, the controller behavior is discussed, and control architectures necessary to improve controller performance are presented. The performance of the neural network controller under less restrictive assumptions is examined, and a neural network analog to the conventional MPC design is developed and tested. An IMC-type neural network controller in which the process model replaced by a neural network gives very good performance, even when only partial state data are available, also gives excellent performance. These results indicate that neural networks can learn accurate models and give good nonlinear control when model equations are not known. Suggestions for improving performance are presented. >