## 5 Conclusions  
A hybrid controller in a broadcast and communication environment with the BET system produced very promising results in terms of finding a consensus as to the rendezvous point. The agent was able to reach consensus regarding the rendezvous for the desired target in a minimum time and number of iterations, while reducing the utilization of channels. A lower number of channels will reduce communication resources, which will simultaneously reduce the number of control updates and save energy. In fact, BET  
Figure 11: NOC per iteration (BET). \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline Time (s) & 0.2 & 0.61 & 1.02 & 1.43 & 1.84 & 2.24 & 2.65 & 3.06 & 3.47 & 3.88 \\ \hline Iterations & 1 & 3 & 5 & 7 & 9 & 11 & 13 & 15 & 17 & 19 \\ \hline NOC & 2 & 4 & 4 & 5 & 5 & 5 & 5 & 5 & 6 & 6 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Number of channels per 10 iterations (BET). Figure 12: NOC per iteration (BTT). \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline Time (s) & 0.12 & 0.36 & 0.6 & 0.84 & 1.08 & 1.32 & 1.56 & 1.8 & 2.04 & 2.28 \\ \hline Iterations & 1 & 3 & 5 & 7 & 9 & 11 & 13 & 15 & 17 & 19 \\ \hline NOC & 10 & 10 & 10 & 10 & 10 & 10 & 10 & 10 & 10 & 10 & 10 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Number of channels per 10 iterations (BTT). was much better than BTT in terms of time, iterations, number of channels and trajectory movement. Furthermore, the trajectory of agents showed less communication effects towards the gradient value of SPSA, which would have apparent effects on the agent's next state position. Thus, BET has been proven to be efficient and practical in order to find a consensus as to a rendezvous point in a broadcast and communication environment. In future, this research can be validated using a real multi-agent robot which uses broadcast and distributed control supported by a network wireless system. The effectiveness and robustness of the proposed controller to achieve convergence with probability one, w.p.1 is expected from the results obtained in our modeling. Additional physical tests can be conducted to measure agent efficiencies by recording the time, channel utilization and energy usage. In addition, instead of reducing the communication channels to limit the usage of communication resources, an extension of this research towards energy-aware and energy efficient systems can be proposed. To this end, the total energy obtained from communication and motion can be calculated, which will solve the issue of limitations in agent resources in agent microcontrollers. Finally, the idea of embedding event-triggered systems in other complex systems and in relation to various dynamics of agents is recommended in order to guarantee the practicality and viability of multi-agent controllers. Conceptualization, N.S. and Z.H.I. ; methodology, N.S. and Z.H.I. ; software, NS; validation, N.S. ; formal analysis, N.S. and Z.H.I. ; investigation, N.S. and Z.H.I. ; resources, N.S. and Z.H.I. ; data curation, N.S. ; writing--original draft preparation, N.S. ; writing--review and editing, N.S. and Z.H.I. ; visualization, N.S. and Z.H.I.I. ; supervision, Z.H.I. ; project administration, N.S. and Z.H.I. ; funding acquisition, Z.H.I. All authors have read and agreed to the published version of the manuscript. This work was supported in part by the Ministry of Higher Education Malaysia and Universiti Teknologi Malaysia under Grant no. R.K130000.7843.5F348 and R.K130000.7643.4C355. Not applicable. No new data were created or analyzed in this study. Data sharing is not applicable to this article. Thanks to UTM for providing their available software and robotic platform. The authors declare no competing interests. With the ET model in the system, in which the state measurement error is expressed as Equation (15), the system dynamic (Equation (1)) is represented as Equation (A1),  
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