Modeling and supervisory control design for a combined cycle power plant

The traditional control strategy based on PID controllers may be unsatisfactory when dealing with processes with large time delay and constraints. This paper presents a supervisory model based constrained predictive controller (MPC) for a combined cycle power plant (CCPP). First, a non-linear dynamic model of CCPP using the laws of physics was proposed. Then, the supervisory control using the linear constrained MPC method was designed to tune the performance of the PID controllers by including output constraints and manipulating the set points. This scheme showed excellent tracking and disturbance rejection results and improved performance compared with a stand-alone PID controller’s scheme

Automating control system design via a multiobjective evolutionary algorithm

This chapter presents a performance-prioritized computer aided control system design (CACSD) methodology using a multi-objective evolutionary algorithm. The evolutionary CACSD approach unifies different control laws in both the time and frequency domains based upon performance satisfactions, without the need of aggregating different design criteria into a compromise function. It is shown that control engineers' expertise as well as settings on goal or priority for different preference on each performance requirement can be easily included and modified on-line according to the evolving trade-offs, which makes the controller design interactive, transparent and simple for real-time implementation. Advantages of the evolutionary CACSD methodology are illustrated upon a non-minimal phase plant control system, which offer a set of low-order Pareto optimal controllers satisfying all the conflicting performance requirements in the face of system constraints

Controllers, observers, and applications thereof

Controller scaling and parameterization are described. Techniques that can be improved by employing the scaling and parameterization include, but are not limited to, controller design, tuning and optimization. The scaling and parameterization methods described here apply to transfer function based controllers, including PID controllers. The parameterization methods also apply to state feedback and state observer based controllers, as well as linear active disturbance rejection (ADRC) controllers. Parameterization simplifies the use of ADRC. A discrete extended state observer (DESO) and a generalized extended state observer (GESO) are described. They improve the performance of the ESO and therefore ADRC. A tracking control algorithm is also described that improves the performance of the ADRC controller. A general algorithm is described for applying ADRC to multi-input multi-output systems. Several specific applications of the control systems and processes are disclosed

Active vibration control techniques for flexible space structures

Two proposed control system design techniques for active vibration control in flexible space structures are detailed. Control issues relevant only to flexible-body dynamics are addressed, whereas no attempt was made to integrate the flexible and rigid-body spacecraft dynamics. Both of the proposed approaches revealed encouraging results; however, further investigation of the interaction of the flexible and rigid-body dynamics is warranted

Controling interactions in motion control systems

Design of motion control systems should take into account (a) unconstrained motion performed without interaction with environment or other systems, (b) constrained motion performed by certain functional interaction with environment or other system. Control in both cases can be formulated in terms of maintaining desired system configuration what makes essentially the same structure for common tasks: trajectory tracking, interaction force control, compliance control etc. It will be shown that the same design approach can be used for systems that maintain some functional relations like parallel robots