Conventional Cascade Control Research Articles

Application of a multivariable feedback linearization scheme for STATCOM control

This paper investigates application of a multivariable control technique to the multi-input multi-output (MIMO) nonlinear model of a static synchronous compensator (STATCOM). The proposed controller design is based on a feedback linearization scheme. Its prime goal is the coordinated control of ac and dc voltage for a STATCOM installed in a power distribution system. First, the nonlinear mathematical model of STATCOM along with the distribution system is derived. Then, by using input–output feedback linearization, a state feedback control law is obtained by pole placement. The efficacy of the control strategy is evaluated by digital computer simulations on the complete system for various types of loads and/or disturbances. The comparative study of these results with those obtained in a conventional cascade control architecture establishes the elegance of this new control scheme.

Improving performance using cascade control and a Smith predictor

Many investigations have been done on tuning proportional-integral-derivative (PID) controllers in single-input single-output (SISO) systems. However, only a few investigations have been carried out on tuning PID controllers in cascade control systems. In this paper, a new approach, namely the use of a Smith predictor in the outer loop of a cascade control system, is investigated. The method can be used in temperature control problems where the secondary part of the process (the inner loop) may have a negligible delay while the primary loop (the outer loop) has a time-delay. Two different approaches, including an autotuning method, to find the controller parameters are proposed. It is shown by some examples that the proposed structure as expected can provide better performance than conventional cascade control, a Smith predictor scheme or single feedback control system.

Implementation of an Inverse-Model-Based Control Strategy Using Neural Networks on a Partially Simulated Exothermic Reactor

Recently, the use of control strategies based upon inverse process models for non-linear systems has been found promising. The requirement of a true analytical inverse can be avoided when neural network models are used; they have the ability to approximate both the forward and the inverse system dynamics. Although many simulation studies have illustrated the use of neural network inverse models for control, very few on-line applications have been reported. This paper describes a novel implementation of a neural network inverse-model based control method on a experimental system—a partially simulated reactor, designed to test the use of such non-linear algorithms. The implementation involved the control of the reactor temperature in the face of set point changes and load disturbances despite the existence of significant plant/model mismatch. Comparison was also made with conventional PID cascade control in several cases. The results obtained show the capability of these neural-network-based controllers and, incidentally, point out the differences between simulation studies and on-line experimental tests. Since the system in this study was only mildly non-linear, in some cases, the performance was comparable to that achieved by classical controllers while in other cases an improved control was achieved.

DC-bus voltage control of three-phase AC/DC PWM converters using feedback linearization

In this paper, a fast voltage control strategy of three-phase AC/DC pulsewidth modulation (PWM) converters applying a feedback linearization technique is proposed. First, incorporating the power balance of the input and output sides in system modeling, a nonlinear model of the PWM converter is derived with state variables such as AC input currents and DC output voltage. Then, by input-output feedback linearization, the system is linearized and a state feedback control law is obtained by pole placement. With this control scheme, output voltage responses become faster than those in a conventional cascade control structure. For robust control to parameter variations, integrators are added to the exact feedback control law. Since the fast voltage control is feasible for load changes, it is shown that the DC electrolytic capacitor size can be reduced. In addition, the capacitor current is analyzed for size reduction of the capacitor. As is usual with PWM converters, the input current is regulated to be sinusoidal and the source power factor can be controlled at unity. The experimental results are provided to verify the validity of the proposed control algorithm for a 1.5 kVA insulated gate bipolar transistor PWM converter system.

A parallel control strategy abstracted from the baroreceptor reflex

A parallel control strategy is developed for process applications by “reverse engineering” the functions of the baroreceptor reflex—the biological control system that regulates arterial blood pressure. The specific control architecture and algorithm employed by the reflex are analyzed from a process control perspective. A parallel control structure for process applications is then developed by reparameterizing the controllers in the biologically derived architecture. The resulting structure allows independent design of the parallel controllers via H 2-optimal control theory. The parallel control technique is applicable to single-input processes for which two types of output measurements are available: (i) a primary measurement of the controlled output whose dynamic response to input changes is unfavorable (e.g. delayed); and (ii) a second-ary measurement of a different output whose dynamic response is more favorable (e.g. undelayed). The parallel control system uses the primary and secondary outputs in a coordinated fashion in order to provide high performance disturbance rejection. Compared to conventional cascade control, the parallel control strategy provides improved stability and robustness characteristics. Two simulation examples demonstrate the superior performance and failure tolerance that can be achieved with the parallel control strategy compared to cascade control and single-input, single-output control techniques.

Limitations to Input-Output Analysis of Cascade Control of Unstable Chemical Reactors

In this paper we show that the traditional methods of analyzing cascade control (based on series or parallel input/output relationships) should not be used when the primary and secondary processes are coupled, and the process is open-loop unstable. The connection between cascade control and traditional state feedback control is studied. In addition, we develop a cascade control system methodology which incorporates a specified inner-loop controller in the outer-loop controller design. This cascade control design procedure is compared to a conventional cascade control design procedure and to a noncascade control strategy.

Steam Superheater Control Via Self-Tuning Regulator

Self-tuning regulator (STR) based on recursive identification and multistep control performance is described. It was applied on 200 MW fossil–fuel–fired power unit with once–through steam boiler, for steam temperature control. The control loop of super heated steam temperature is one of the most important loops in the steam boiler. Dynamics of this plant varies with the power output and working conditions. Conventional cascade control, especially under changing conditions, has not been fully successful, and therefore, the adaptive control was verified. The STR was tried in two different control concepts. In the first concept, main PI controller in the conventional cascade control loop was replaced with the STR. In the second concept the STR calculated the set point of the main PI controller of conventional cascade loop, from outlet steam temperature deviation. Described are the control loop and the results of experiments, together with a comparison between conventional and adaptive control.