scope:

INTRODUCTION

About one hundred years ago, applying the thermostat and the control valve to home heating controls, the automatic control system has been initially realized. Since then, the heating, ventilating, and air-conditioning (HVAC) systems have been considered from viewpoint of control engineering. The complexity of multi-variable system, interacting system, and distributed system are the common characteristics for any process industries. The HVAC systems, however, have not been advanced compared to chemical and steel processes that exploited the advantages of digital control (Hartman 2003).

The HVAC systems have huge different characteristics in control engineering from chemical plants. One of the characteristics is that the equilibrium point (or the operating point) usually changes with disturbance such as outdoor temperature, control input, and thermal loads etc. The change of the equilibrium point means the change of plant parameters. Thus, the HVAC control system is extremely difficult to obtain an exact mathematical model.

Today, a variable air volume (VAV) system is universally accepted as means of achieving energy efficient and comfortable building environment. While the VAV control strategies provide a high quality environment for building occupants, the VAV system analysis rarely receives the attention it deserves. As a result, basic control strategies for the VAV system have seen little significant change up to now (Hartman 2003).

Recently, applying the model prediction control to the HVAC systems, the control performance has been highly improved by pursuing the perturbation of equilibrium point of plant (Taira 2004). In this paper, recognizing the operating point of control input and calculating the optimal control input about the perturbation for equilibrium point on next sampling time, the control system gives better responses than the traditional feedback control systems.

One of the primary objectives of the HVAC systems is to maintain the indoor temperature and humidity at the setpoint values to provide a high quality environment for building occupants. Proportional-plus-integral (PI) controllers are by far the most common control algorithm and the situation has not yet changed greatly. With a simple Proportional (P) controller, there would be an offset (or a steady-state error), which the operator could eliminate by the manual reset to compensate for thermal loads change. The supply air flowrate (the control input) to the room (the controlled plant) should offset thermal loads change imposed on the room due to the function of an Integral (I) action. PI controller, however, often leads to a poorly damped response. Proportional-plus-integral-plus-derivative (PID) controllers have been more desirable than PI controllers due to the stabilizing effect of a Derivative (D) action. In practice, however, the D action has been frequently switched off for the simple reason that it is difficult to tune properly (Shilling 1963, Shinsky 1967, Takahashi 1969).

In some applications, thermal loads (or disturbances) can be estimated in advance before they entered the plant. A typical example is a certain system for HVAC systems in which the outdoor thermometer detects sudden weather changes and the occupant roughly anticipates thermal loads changes. Disturbances should be offset by the compensation of the manual reset. This control strategy can be called a type of feed-forward control. The control scheme with lower (or no) I action may be interpreted as a Proportional-plus-derivative (PD) controller. In this paper, of special interest to us is how to make the D action more effective for HVAC systems. At first, this paper proposes a compensation method of the manual reset to offset thermal loads before they affect the control output and confirms the effectiveness of compensation.