Fuzzy Control Strategy of Tunnel Lighting Based on Input Parameter Optimization

The mismatch between illumination and traffic safety brightness in tunnels leads to much-wasted lighting energy and safety problems. Effective control of tunnel lighting can be achieved by lighting on demand and reducing lighting energy consumption. This paper uses the curve fitting and linear interpolation methods to optimize the luminance reduction factor K proposed in the Highway Tunnel Lighting Rules. It constructs a multi-zone division of traffic speed, traffic flow, and brightness outside the tunnel to obtain the K value under actual road conditions and to establish a fuzzy controller for dimming control. The simulation demonstrates that the proposed strategy can save about 37.38% of tunnel lighting compared to the graded lighting control strategy.


Introduction
As a particular part of highways, by the end of 2020, there were 21,316 road tunnels nationwide, including 1, 394 extra-long tunnels and 5, 541 long tunnels [1].Electricity expenditure for tunnel lighting accounts for the vast majority of the total revenue from highway operations, and the high energy consumption situation seriously affects tunnel development [2].Therefore, using intelligent controls to reduce energy consumption pathways is essential to achieving energy efficiency in tunnels.Many scholars have focused on single models and intelligent control strategies combining different models.
Single model: Li [3] proposed a multi-stage brightness intelligent control strategy for tunnel lighting based on grey theory but could not achieve intelligent control according to the external tunnel environment.Qin et al. [4] used PID closed-loop feedback adjustment to keep the required brightness and the interior tunnel road surface brightness consistent to ensure the tunnel lighting demand.Zhou et al. [5] and Zhao et al. [6] fitted the relationship between the detected luminance outside the cave, traffic volume, and speed as a function of the demanded brightness in each tunnel section through neural networks, respectively.
Different models combined: Qin et al. [7] and Li et al. [8] reduced energy consumption through a tunnel lighting control strategy with a fuzzy neural network algorithm.A control system based on vehicle perception and a fuzzy PID algorithm by Du and Qin [9] accelerates the rate of brightness adjustment inside the tunnel.It reduces the impact of the light fading phenomenon on the lighting system.Ding et al. [10] used IoT technology as the basis to achieve the goal of tunnel lighting dimming control through genetic neural network algorithms.
The above study relies on the luminance reduction factor K for tunnel lighting calculations without considering actual traffic speed, traffic flow, and brightness outside the tunnel, resulting in overly broad values.There is no cross-comparison to verify the effectiveness of the control strategy in terms of energy saving.This paper uses curve fitting and linear interpolation to optimize the entrance section K.The parameters of vehicle speed, traffic flow, and brightness outside the cave are subdivided into multiple intervals.Using a tunnel as a simulation example, the energy-saving effect of the control strategy proposed in this paper is verified by applying five different control strategies.

Tunnel lighting control systems
The lighting control system consists of a data acquisition unit, a data processing unit, and an LED control unit.The data acquisition unit obtains data on vehicle speed, traffic flow, and brightness outside the tunnel through microwave vehicle detectors and illuminance sensors and transmits the data using an RS485 bus.The data analysis unit uses a PLC controller as the core and calculates the optimized K-value using the parameter optimization method.It establishes a fuzzy controller to obtain the tunnel lighting brightness value by combining the refined vehicle speed, traffic flow, and tunnel brightness values.The LED control unit consists of a centralized controller, a luminaire controller, and LED luminaires in the tunnel.PWM pulses to adjust the brightness of the tunnel lighting to the luminaires.The centralized controller accepts the PWM pulse control signal converted from the cavern lighting brightness value and adjusts the brightness of the LED luminaires via the luminaire controller, the tunnel lighting control structure is shown in Fig. 1.

Calculation specifications for road tunnel lighting
The Highway Tunnel Lighting Design Rules segment the lighting design and calculate the formulae for the internal sections of a highway tunnel, as shown in Table 1.According to the principle of decreasing tunnel lighting, the tunnel lighting is divided into entrance section (TH1, TH2), transition section lighting (TR1, TR2, TR3), intermediate section lighting (TN), and exit section lighting (EX1, EX2), the lighting brightness values are L th , L tr , L in , and L ex .L 20 is the tunnel exterior brightness, and K is the brightness reduction factor (referred to as the K value).Table 1.Lighting brightness of tunnel sections Entrance Transitional section Outlet section

Tunnel lighting fuzzy controller
For K, the design traffic flow and design speed are considered, but the actual traffic flow and speed are not.This paper optimizes the K value by curve fitting and linear interpolation.The corresponding K value is calculated according to the measured traffic flow and speed.It is defined by the weather conditions outside the tunnel within the permissible range of speed, traffic flow, and brightness outside the tunnel to achieve lighting energy saving by the fuzzy controller.The optimized vehicle speed, traffic flow, and outside brightness are inputs to the fuzzy controller.The required lighting brightness values are obtained according to the internal operational steps of the fuzzy controller: fuzzification processing, fuzzy rule inference, and clarification processing.

Input parameter optimization
Based on the existing table of K values, the Matlab software was used to perform a linear fit of K values for different real-time vehicle speeds at high and low traffic volumes to obtain the fitted curve equations for the brightness discount factors K H and K L .
where N is the actual traffic flow, veh/h; N L is the small traffic flow in the actual traffic flow zone, veh/h; N H is the large traffic flow in the actual traffic flow zone, Considering that the traffic flow N and design speed v of the K-value-taking table in the design details are too broad, it is not possible to make real-time lighting adjustments for the actual road conditions.Therefore, the design speed and traffic flow are finely divided into multiple zones.The common driving speed of 45-80 km/h is divided into an interval of 5 km/h for a total of 8 refined zones.The traffic flow of 350-1200 veh/h is divided into [350, 690], [690, 1030], and [1030, 1200] refined zones to facilitate the observation.The weather conditions outside the tunnel are also divided into multiple zones for the exterior tunnel brightness value L 20 : sunny, cloudy, cloudy, heavy cloudy, evening, and night zones.

Fuzzy processing
Fuzzification is a control method in which the controlled quantities of the control system scale the precise input quantities to match their respective theoretical domain ranges after expert knowledge.The physical domain of vehicle speed is set to [45, 80] and the discrete domain to {45, 50, 55, 60, 65, 70, 75, 80}, the physical domain of traffic flow to [350, 1200] and the discrete domain to {350, 520, 690, 860, 1030, 1200}, the physical domain of tunnel exterior brightness to [200,4000] and the discrete domain to {200, 960, 1720, 2480, 3240, 4000}.Combined with the gentle tunnel lighting adaptation curve, the generalized bell-type affiliation function curve shape is smoother.It has better stability and control, using the generalized bell-type affiliation function.

Fuzzy control rules and clarity of treatment
Fuzzy control rules are the core of fuzzy control, the control system's correction device and compensation device.A fuzzy control rule table is established by dividing the vehicle speed, traffic flow, and brightness outside the cave parameters into 8, 6, and 6 fuzzy subsets and converting the data into fuzzy rules using if...and... then language-based fuzzy statements.The fuzzy variables output by the fuzzy inference rule table is transformed into specific values using clarification processing.To include the overall information on the affiliation degree function and to guarantee the objectivity of the decision according to the different biases of the affiliation degree, this paper uses the area center of gravity method for clarification processing.

Simulation experiments
Using Matlab simulation software and fuzzy inference rule table, the simulation output of tunnel lighting brightness under constant speed, constant traffic flow and constant brightness outside the tunnel is shown in Figure 2,3,4.As there are differences in external brightness, speed, and traffic flow between the left and right side of the tunnel at the same moment, this paper collects three types of data on the brightness outside the tunnel, the speed and traffic flow of vehicles entering the entrance section of the tunnel from 6 am to 6 pm for a single side tunnel, the results of which are shown in Figures 6 and 7. Five control strategies, including four-level lighting control, BP neural network [5], optimized compensation control [11], fuzzy control [12], and the method in this paper, are used to collect environmental data according to the "Highway Tunnel Lighting Rules" and the technique of optimizing K value.Because there is a linear relationship between the lighting of each section of the tunnel, the lighting brightness value of the tunnel entrance section 1 with the largest power consumption is calculated.The energy-saving effect is analyzed according to the difference in the lighting brightness value, the brightness comparison results of multiple control strategies are shown in Figure 8.As can be seen from Table 2, the fuzzy control strategy based on input parameter optimization proposed in this paper has the best energy-saving effect.Compared to the traditional graded lighting control strategy based on the rules, the lighting brightness value of TH1 in the entrance section of the tunnel is reduced by about 37.38%, which can achieve the energy-saving purpose.(1) The fitting and interpolation calculation methods are used to optimize the K value of each section of the tunnel.The fitting curve and calculation formula of the K value is obtained, and the tunnel illumination brightness is calculated in detail.Combined with factors such as vehicle speed, traffic flow, brightness outside the tunnel, and weather conditions, the complex conditions inside the tunnel are subdivided to facilitate the lighting control system to change the lighting brightness in real-time and achieve energy saving.
(2) The comprehensive comparison of various algorithms shows that the three control strategies after optimizing the K value are significantly lower than the four-level control illumination demand after optimizing the K value.Using the parameter optimization fuzzy control strategy, the TH1 illumination brightness of the tunnel entrance section can be reduced by 37.38%.

Figure 1 .
Figure 1.Tunnel lighting control structure

Figure 2 .
Figure 2. Lighting brightness output diagram of constant speed

Figure 4 . 5 Figure 5 .
Figure 4. Illumination brightness output diagram with constant brightness outside the hole5.Energy efficiency analysisThis paper uses a tunnel as a validation example, and the tunnel is a double-hole four-lane tunnel.The design speed is 80 km/h, the length of the left hole is 3939 m, and the size of the right hole is 3824.5 m.The two cave lamps use a symmetrical arrangement.The tunnel entrance section lamps use two rows of performance.The rest of the sections use a single-row arrangement.Internal tunnel strengthening and basic and emergency lighting used LED lamps.The length of each section of the tunnel is shown in Figure5.

Figure 6 .
Figure 6.Brightness curve outside tunnel Figure 7. Vehicle speed vs volume flow graphs

Figure 8 .
Figure 8.Multiple control strategy illumination brightness comparison chart

Table 2 .
Energy saving effect comparison table This paper proposes a lighting control energy saving scheme combining optimized input parameters and fuzzy control algorithm to achieve the lighting energy saving target.It verifies and analyzes through simulation experiments to obtain the following conclusions: 2023 5th International Conference on Energy Systems and Electrical Power Journal of Physics: Conference Series 2584 (2023) 012103 IOP Publishing doi:10.1088/1742-6596/2584/1/012103