# Development of Lift Control System Algorithm and P-M-E Analysis in the Workplace
## 1 Introduction  
Lifts are essential motor-powered vertical media of transportation in residential, commercial and industrial buildings that play a huge role in the movement of people around these environments. Nowadays, as a result of tremendous development in the structural and architectural engineering in multi-storage buildings, lifts have become inevitable and a key requirement for human transportation [1]. Lifts are used in almost all the multi-storage buildings of metropolitan areas and hence, it is important to replace the traditional relay logic controlled lifts with more programmable technology based lifts for better efficiency, such as PLC [2; 3]. These relay controlled systems have several limitations, such as a high fault ratio, difficulty in replacing flawed parts of the automated system and highly complex circuitry. Another drawback is the difficulty in providing fault tolerance using relay logic. PLC serves as a more enhanced replacement for designing modern lift control systems to circumvent these shortcomings and improve the troubleshooting of the system by allowing for easy monitoring of the inputs and outputs via human-machine interaction (HMI) devices, such as the HMI-LED indicator, better operational speed, reliability and relatively lower costs compared to other programmable control systems [4; 5; 6; 7; 8]. PLC has been successfully demonstrated in several control studies, including lift control systems [9; 10; 11; 12; 13; 14]. When designing lifts with PLC, the dispatching algorithm is one of the most important aspects in the control system and therefore, an efficient algorithm can reduce the average waiting time of passengers to a remarkable average of 25 s or less and also reduce the power consumption of the lift system. Six main types of dispatching algorithms are generally implemented, which are namely: (i) Collective up--CU; (ii) Collective down--CD; (iii) Selective up--SU; (iv) Selective down--SD;(v) Selective-collective up--SCU; and (vi) Collective-selective down--CSD algorithms [5]. The choice of the preferred algorithm is selected on a specific instance based on the traffic amount and percentage. To achieve an efficient control system, the algorithm employs a fuzzy scheme to improve control based on logical reasoning and implementing systems through programmed expert knowledge. The concept of a fuzzy control system was first introduced by [PERSON] as he introduced the concept of linguistic variables that serve as fuzzy sets (i.e., input variables in the fuzzy control) [15; 16]. The fuzzy system is normally divided into four main parts, which are namely fuzzy knowledge rules, fuzzifier, fuzzy inference engine and the defuzzifier. This is done to transfer the input signal into linguistic terms and the inference makes the calculations and decision to prioritize certain lift assignment. Furthermore, the output information is converted by the defuzzifier into a single signal that serves as the control instructions [17]. The concept of fuzzy control and fuzzy logic has been extended to lift control and group control systems. The early to mid 1990s led to a boom in the implementation of fuzzy logic in lift systems [18; 19; 20; 21; 22]. [PERSON] et al. [23; 24] demonstrated a design based on a fuzzy control model that identifies traffic patterns and implements the traffic patterns and traffic mode based on information, such as traffic percentage, time, area-weight and other linguistic terms. [PERSON] et al. further demonstrated a fuzzy logic in group supervisory control, which demonstrates an optimum lift vehicle assignment with control devices [25]. Recent studies have introduced novel approaches for applying fuzzy logic in order to improve the expert prediction of lift control as compared to the old methods to increase efficiency in lift systems and energy optimization [26; 27; 28]. [PERSON] et al. further extended conventional fuzzy lift systems by introducing a self-tuning mechanism to adapt the control system to the continually changing traffic with better precision [29; 30].