PID Tuning Based on Improved Quantum Genetic Algorithm

In the process of trajectory tracking using the linear quadratic regulator (LQR) for driverless wheeled tractors, a weighting matrix optimization method based on an improved quantum genetic algorithm (IQGA) is proposed to solve the problem of weight selection. Firstly, the kinematic model of the wheeled tractor is established according to the Ackermann steering model, and the established model is linearized and discretized. Then, the quantum gate rotation angle adaptive strategy is optimized to adjust the rotation angle required for individual evolution to ensure a timely jumping out of the local optimum. Secondly, the populations were perturbed by the chaotic perturbation strategy and Hadamard gate variation according to their dispersion degree in order to increase their diversity and search accuracy, respectively. Thirdly, the state weighting matrix Q and the control weighting matrix R in LQR were optimized using IQGA to obtain control increments for the trajectory tracking control of the driverless wheeled tractor with circular and double-shifted orbits. Finally, the tracking simulation of circular and double-shifted orbits based on the combination of Carsim and Matlab was carried out to compare the performance of LQR optimized by five algorithms, including traditional LQR, genetic algorithm (GA), particle swarm algorithm (PSO), quantum genetic algorithm (QGA), and IQGA. The simulation results show that the proposed IQGA speeds up the algorithm’s convergence, increases the population’s diversity, improves the global search ability, preserves the excellent information of the population, and has substantial advantages over other algorithms in terms of performance. When the tractor tracked the circular trajectory at 5 m/s, the root mean square error (RMSE) of four parameters, including speed, lateral displacement, longitudinal displacement, and heading angle, was reduced by about 30%, 1%, 55%, and 3%, respectively. When the tractor tracked the double-shifted trajectory at 5 m/s, the RMSE of the four parameters, such as speed, lateral displacement error, longitudinal displacement error, and heading angle, was reduced by about 32%, 25%, 37%, and 1%, respectively.

ABSTRACTSelf‐interference (SI) suppression has always been critical for simultaneous transmit and receive (STAR) systems. To address the strong coupling between nearby transmit and receive antennas, this study employs an improved quantum genetic algorithm (IQGA) to eliminate SI by combining digital self‐interference cancellation (SIC) and adaptive beamforming (ABF). Through digital SIC, both transmit noise and transmit signals are suppressed. The objective of the transceiver beamformer is to further reduce SI through SIC while simultaneously achieving high transceiver gain in the desired direction. IQGA represents the weights of feasible transceiver beams using chromosomes. By updating the population through quantum rotation, quantum crossover and quantum mutation strategies, IQGA demonstrates improvements in convergence accuracy, convergence speed, and reliability. In comparison to traditional ABF approaches, IQGA eliminates the need for complex matrix calculations and numerical derivations, thereby simplifying the solution process. The simulation results indicate that by cancelling the SI component through SIC and ABF, an isolation of 159.78 dB can be achieved at a transmit power of 1000 W. This represents an improvement of 40.32 dB compared to SIC alone and 113.87 dB compared to scenarios without SIC and ABF.

Study the optimization of aero-engine PID (Proportional-Integral-Derivative) controller parameters. In order to improve the tuning accuracy on aero-engine PID control parameters for the optimal solution, this paper presents a genetic algorithm-based PID parameter tuning method. Because the choice of crossover and mutation probabilities in genetic algorithm have a significant impact on the convergence speed and stability of the control system, genetic algorithm is adopted of which the crossover and mutation probability can automatically change with the fitness. Simulation results show that after variable crossover and mutation probability adaptive genetic algorithm to optimize PID control parameters, the average convergence algebra is significantly reduced and the overall control performance of the system is better. Aero engine work process is complex systems that big lag, nonlinear and multi-coupling throughout the flight envelope, Aero engine aerodynamic thermodynamic processes will occur a very big change, with the change of environmental conditions and flight status (airspeed, Mach number, etc.), so be quick, effective control is particularly important. PID control is one of the earliest developed control strategy, due to its simple structure, robustness, high reliability, easy to implement and other characteristics, widely used in process control and motion control, especially in the determination system that precise mathematical model can be established. Three parameters of proportional coefficient ( Kp ), integral time ( Ki ) and derivative time ( Kd ) tuning are direct influenced the control effect of the PID controller, the optimizing of PID controller parameters directly affect the control effect of control system. At present, there are many ways to optimize the PID parameters, such as Ziegler-Nichols method, indirect optimization method, the gradient method, climbing method, etc. The designer of these methods, mainly through the Mathematical model of controlled object, combined with practical experience, through repeated on-site equipment debugging obtained. Because these tuning method are relatively complicated, and affected by human factors, the final parameters of being got are not usually the optimal solution. The traditional tuning method based on a specific model is not applicable, for such aero engine as complex nonlinear systems. Therefore you can take advantage of intelligent control technology for optimizing PID parameters in order to achieve optimal control. Genetic Algorithm (GA) is an optimization method for parallel simulation of natural genetic mechanisms and biological evolution and the formation of a random search, survival of the fittest principle of biological evolution exist in nature, was introduced to optimize the parameters of a coded series groups.

In the paper recent design and tuning methods of PID controllers are summarized briefly. Several decades have seen the widely application of PID controllers and the design and tuning methods have been of considerable development. Process model identification, PID controller structure design, PID parameters tuning methods and the use and expansions of PID are described in turn. PID parameter tuning methods are highlighted.

Facility allocation plays a crucial role in modern urban planning and design. However, the allocation of various facilities in a reasonable manner is a complex problem involving high-dimensional multi-peak combination optimization. Existing heuristic methods for facility configuration, including traditional genetic algorithms and their improved variants, often suffer from premature convergence and limited search diversity when dealing with large-scale spatial optimization problems under geographical correlation and heterogeneity constraints in urban coupled environments. These limitations result in suboptimal solutions and reduced effectiveness of planning schemes. This paper proposes an improved quantum genetic algorithm with adaptive dynamic rotation angle (IQGA-ADA) for spatial optimization of facility configuration. The proposed IQGA-ADA method addresses the limitations of existing approaches through two key innovations: (1) an ADA mechanism integrated into the population renewal strategy to prevent early-stage local convergence and enhance global search capabilities; (2) the application of S-transform for processing multi-modal spatial data (including population distribution, traffic network, and facility location information) to extract characteristic quantities (root mean square and mean value) that enhance the identification degree of facility configuration patterns. By conducting a case analysis on the spatial optimization of first aid facilities, the proposed algorithm demonstrates its capability to enhance the fairness of repositioning optimization for such facilities. Quantitative results show that IQGA-ADA achieves a 68% improvement in fitness value over the real-coded genetic algorithm (RCGA), with the average fitness converging at approximately 5 iterations compared to 125 iterations for RCGA. Experimental results indicate that the proposed algorithm exhibits stronger global search capabilities and performs better in handling high-dimensional multi-peak spatial optimization problems. Moreover, the research highlights the potential application of quantum evolution mechanisms in addressing geospatial optimization problems.

This chapter investigates a kind of whole new approach to spectrum sharing in cognitive radio (CR) system, quantum-inspired approach. First, we improved Quantum Genetic Algorithm (QGA) by adding quantum crossover operator to overcome the shortcomings of the original QGA, easily falling into a local extremum when used to optimize the continuous functions with many extrema. We then propose a novel spectrum sharing scheme in noncooperative game for CR system based on our improved QGA. We use QGA as competitive strategies and provide simulation results that confirm the proposed schemes have better convergence rate and higher sum capacity than genetic algorithm (GA)-based scheme, namely at most 1 bit/s/Hz increase in capacity. The results demonstrate the effectiveness and the applicability of QGA in spectrum sharing in CR system.

E-commerce has become the mainstream consumption mode for people, and with the continuous increase of online shopping business volume, controlling logistics costs has become an urgent problem to be solved. In order to solve the challenges in vehicle scheduling and achieve a scientific and reasonable vehicle scheduling scheme, this research is based on Hadoop Big data platform, introduces the concept of time axis, and builds a vehicle scheduling model based on Hadoop intelligent platform. Based on quantum genetic algorithm and combined with MapReduce model, a quantum improved genetic algorithm is constructed to solve vehicle scheduling optimization problems. The results show that the traditional quantum genetic algorithm converges after 20 iterations, achieving an optimal value of 575 and taking 360 seconds. The improved quantum genetic algorithm converged after 10 iterations and achieved an optimal value of 675, taking 200 seconds. Compared with quantum genetic algorithm, the improved quantum genetic algorithm reduces the time spent by 44.4%. Selecting customer data from a certain logistics company for testing, the improved algorithm shortens the delivery time and achieves the design of the optimal path scheduling plan. This study optimized transportation routes and resource scheduling, reduced transportation costs, and played an important role in optimizing vehicle scheduling in the logistics industry.

To solve structure optimization problems of the information transmission networks, based on quantum genetic algorithm (QGA), the paper proposes an improved quantum genetic algorithm (IQGA). The core of the algorithm is to take full advantage of interference and entanglement of quantum states, through adopting the quantum gates rotation corner dynamic adjusting mechanism, quantum crossover and mutation operations, genetic operation parameters dynamic adaptive adjustment mechanism and solution elites control strategy. The algorithm has better performance in solving combinatorial optimization problems of the weak correlation between genes.

In order to optimize large-scale online data stream clustering quality and algorithm performance, a fast clustering optimization method is proposed based on line data flow evolution incentive. Firstly, in the online stage, the high density partition algorithm is used to generate micro cluster set, and the set takes the snapshot window as the time scale. The micro cluster set is updated in real-time. In the offline stage, the micro cluster set is read, and the improved quantum genetic algorithm and the evolution incentive function are used for the iterative optimization in clustering center. Finally, the adaptive mutation operator is taken as the optimal variation operation for the populations. The global search ability of the algorithm is improved. Simulation results show that algorithm has better clustering accuracy, and the convergence speed is high with less memory cost. It has good application value in practice.

Data-driven optimization of pollutant emission and operational efficiency for circulating fluidized bed unit

Capacity allocation and optimization strategy of an energy storage system based on an improved quantum genetic algorithm

The Bouc–Wen nonlinear hysteretic model is widely used in nonlinear systems, and it is difficult to identify the parameters of Bouc–Wen model because of its high nonlinearity. In the paper, a hybrid genetic algorithm combined with sequential quadratic programming is proposed to solve the problem about the parameter identification of Bouc–Wen model. Sequential quadratic programming method has good local convergence and superlinear convergence rate. Therefore, compared with the genetic algorithm, the proposed identification method can accelerate the convergence speed in parameter recognition of Bouc–Wen model and achieve excellent recognition accuracy. Through the function test and the numerical simulation of the shear structure, it is proved that the proposed method has high convergence speed and accurate identification. In addition, the theory and method proposed in this paper are combined with Bouc–Wen model to identify parameters of overall restoring force model of the energy-dissipation structures, the identification results are in good agreement with the experimental results, verifying reliability and accuracy of the model and robustness of the proposed identification method.

With the wide application of Buck converters, it is of great significance to select the optimal PID controller parameters to ensure the dynamic and steady-state performance of the Buck converters. On this basis, a PID parameter tuning method based on an improved differential evolution algorithm is proposed. Also, a crossover approach with population generation across orders of magnitude, elastic boundary absorption, and retention of some combined features is put forward. In addition, an adaptive mutation factor, and a cost function that suppresses overshoot and is sensitive to the tuning time are designed. The results of 10 tests show that the differential evolution algorithm fails in the case of parameters in a range cross orders of magnitude, while the algorithm proposed in this paper enables a better combination of PID parameters selected in terms of speed and stability, with a wide search space and stable convergence results.

In this study, the use of drones in transportation and some optimization studies carried out in the literature so far were examined. Based on these investigations, a genetic algorithm-based method has been proposed to solve the hybrid truck multi-drone problem. With the proposed algorithm, scenarios have been created using single or multiple drones and one truck for a certain number of customers to be delivered, and results have been obtained. In these scenarios, the main aim is to minimize delivery time. The results obtained have also been compared with other optimization algorithms and other results in the literature, and in addition, it has been proven that there is an inverse relationship between the number of drones and the delivery time. As a result of the comparisons, it has been clearly seen that the proposed algorithm is quite successful in finding the most suitable route in the shortest time compared to other studies. A solution has been created for a real-world problem with the proposed genetic algorithm-based algorithm, and it has been concluded that it is quite successful here as well.

In this paper, an innovative design method of PID controller based on genetic algorithm tuning parameters, VHDL description and FPGA implementation is proposed. The parameters of the PID controller are optimized by using genetic algorithm. The intelligent PID controller based on genetic algorithm is realized by using FPGA. During the realization process, the functional modules of the system are divided based on top-down design method. Each module of the system is described with VHDL language. Finally, the closed loop tests of the intelligent PID controller based on FPGA and genetic algorithm are implemented by means of DSP Builder and Matlab/Simulink. Simulation results have shown that the design method of the intelligent PID controller based on FPGA and genetic algorithm is verified, and it has some advantages such as design flexible, self-tuning on line, high reliability, short cycle of technical development, high running speed and so on. This intelligent PID controller based on FPGA and genetic algorithm can be applied in high control precision digital control system with high speed responsibility and high stability.