LQR Trajectory Tracking Control of Unmanned Wheeled Tractor Based on Improved Quantum Genetic Algorithm

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.

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.

This paper introduces an application of Multi-Objective Evolution Algorithm (MOEA) to design Q and R weighting matrices in Linear Quadratic regulators (LQR). Considering the difficulty of designing weighting matrices for a linear quadratic regulator, a multi-objective evolutionary algorithm based approach is proposed. The LQR weighting matrices, state feedback control rate and consequently the optimal controller are obtained by means of establishing the multi-objective optimization model of LQR weighting matrices and applying MOEA to it, which makes control system meet multiple performance indexes simultaneously. Controller of double inverted pendulum system is designed using the proposed approach. Simulation results show that it has shorter adjusting time and smaller amplitude value deviating from steady-state than a Non-dominated Sorting Genetic Algorithm LQR ( NSGA- LQR )weighting matrices design approach.

To improve the directional performance of articulated heavy vehicles (AHV), three previewed points driver model and LQR active trailer steering (ATS) strategy are proposed and examined using closed-loop driver/vehicle dynamics simulation. Firstly, a simplified linear three-degree-of-freedom (3-DOF) yaw plane model for AHV is established and validated. Secondly, based on the multi-point preview-predictor following strategy, the weighted-average error of three previewed points of the tractor is defined as the final lateral previewed displacement error. According to the assumption of constant yaw rate and the trajectory prediction of AHV, and three previewed points driver model has been configured, it’s path-following performance is verified. Thirdly, instead of using the idea of minimization, a linear quadratic regulator (LQR) controller is proposed based on the linear 3-DOF yaw plane model of AHV to make the control variables to keep following the desired responses. Through joint simulations using MATLAB/Simulink and TruckSim, the effectiveness of the built-in driver model, three-previewed point driver model and three-previewed point driver model + LQR ATS control methods on improving vehicle orientation performance were compared and analyzed through closed-loop driver/vehicle dynamics simulations. The significant effectiveness of the proposed method in improving maneuverability and stability is verified through low-speed 360° trajectory tracking tests and high-speed double lane change maneuver simulations. Finally, a hardware-in-the-loop (HIL) platform is used to further verify the effectiveness of the designed strategy.

This paper presents a method of pole placement in the linear quadratic regulator and its application to the flight control system design. There are three features in the proposed method. First, a weighting matrix that gives desired closed-loop pole locations is obtained by solving a set of differential equations. They are derived from the characteristic equation of the Hamilton matrix. Second, poles can be placed exactly at the desired positions and arbitrarily except the symmetry of complex conjugate poles with respect to the real axis. Third, it is a diagonal weighting matrix that is obtained by the proposed method. The third feature makes output regulation with desired pole locations possible. After demonstrating the effectiveness of the method through a simple literature example, it is then applied to the F-4 aircraft's lateral dynamic model. HE optimal regulator, or linear quadratic regulator (LQR), is a typical state-space design method. It has several features. For example, by imposing penalty on state variables and control inputs in a quadratic performance index, one can investigate tradeoff between system performance and control efforts. Besides, it is well known that the LQR has infinite gain margin and 60-deg phase margin for single-input systems. However, by this technique one cannot assign closed-loop pole locations. Since pole locations have a large effect on time-response characteristics, such as overshoot, rise time, etc., it is desirable to assign pole positions in the LQR design. Pole placement in the LQR is a problem associated with the selection of appropriate weighting matrices, when a set of desired closed-loop eigenvalues (CLEs) are known. This problem has been studied by many researchers since early 1970s, and many papers113 have been published in this research area. Among them, the study of Hayase 1 provides a relation between the weighting matrix and the feedback gains for a single-input system in controllable canonical form. Recently, Ohta et al. 2 showed a simple derivation of the relation. Meanwhile, for multi-input systems, Saif3 established a simple method using aggregation to compute a weighting matrix by which arbitrary and exact pole placement can be attained in a general form. He integrated the previously proposed three approaches.46 All of the aforementioned methods for multi-input systems follow Solheim's approach for placing imaginary parts as well as for placing real parts using the mirror image property.7 Another approach, which is completely different from the preceding approaches, is that of Graupe. In his method, first, differential equations of a weighting matrix with respect to the CLEs are derived, and then a weighting matrix is found as a solution of an optimization problem, wherein the difference between CLEs and assigned eigenvalues is made as small as possible.8 However, it is complicated and does not seem practical. Besides the preceding approaches, there are many approaches such as assigning eigenvalues with prescribed degree of stability, 9 placing eigenvalues into a specified region,10'11 and achieving pole placement asymptotically.12'13 In this paper, we propose a method of pole placement that is very different from the previously proposed methods. Whereas Graupe's differential equations are derived from the Riccati equation, we

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.

Parameters tuning is the key of the design of PID controller, for the PID parameters tuning, a PID parameters tuning method based on improved quantum genetic algorithm is proposed in this study. This study proposes an improved genetic algorithm (IQGA) which introduces a dynamic adjustment strategy of population and search intervals to improve the convergence speed, search ability and stability of quantum genetic algorithm-based PID parameters tuning method. Experimental results show that the proposed algorithm can obtain a good performance in PID parameters tuning problem.

An adaptive method with weight matrix as a function of the state to design the rotatory flexible system control law

The capacity of free-space optical (FSO) communication links could potentially be increased by the simultaneous transmission of multiple orbital angular momentum (OAM) beams. For such an OAM multiplexing approach, one requires the collection of adequate power as well as proportion of the phase front for a system with minimal crosstalk. Here we study the design considerations for an OAM-multiplexed free-space data link, analyzing the power loss, channel crosstalk, and power penalty of the link in the case of limited-size receiver apertures and misalignment between the transmitter and the receiver. We describe the trade-offs for different transmitted beam sizes, receiver aperture sizes, and mode spacing of the transmitted OAM beams under given lateral displacements or receiver angular errors. Through simulations and some experiments, we show that (1) a system with a larger transmitted beam size and a larger receiver aperture is more tolerant to lateral displacement but less tolerant to the receiver angular error, and (2) a system with a larger mode spacing, which uses larger OAM charges, suffers more system power loss but less channel crosstalk; thus, a system with a small mode spacing shows a lower system power penalty when system power loss dominates (e.g., a small lateral displacement or receiver angular error), whereas that with a larger mode spacing shows a lower power penalty when channel crosstalk dominates (e.g., a larger lateral displacement or receiver angular error). This work could be beneficial to the practical implementation of OAM-multiplexed FSO links.

The authors present a genetic approach to the optimal linear quadratic regulator (LQR) problem. Design of the optimal LQR depends on choosing weighting matrices. One must carry out a trial-and-error process to choose the proper weighting matrices in general. It is not a trivial problem. They show a new possibility using a genetic algorithm (GA) to find the proper weighting matrices and hence to obtain the optimal control gain. They apply the approach to the attitude control of an inverted pendulum system, which is known as the LQR problem. They perform computer simulations on the system with a 50 cm long inverted pendulum and obtain good results.

Load frequency control (LFC) in power system serves an important role in minimizing the gap between the electrical power generation and demand. This task becomes much more complex in the presences of electrical sources with a large time constant such as hydro power. The conventional controllers are not able to give the satisfactory operation for the LFC against the rapidly changing electrical load demand. The system response varies unboundedly due to fixed gains of the conventional controllers. Hence, to overcome the incapability of conventional controllers, this paper proposes a new structure of linear quadratic regulator (LQR) for controlling the frequency response of hydro power dominating interconnected power system. To show the efficacy of LQR, the LQR is merged with a new and strong Jaya optimization technique. Further to improve the performance of LQR, the weight matrix Q of LQR is optimize using Jaya optimization algorithm for various cases. The system outcomes are acquired for load alteration. The outcomes confirm the usefulness of new Jaya-LQR blend for hydro dominating system.

Model predictive control (MPC) is a multi-objective control technique that can handle system constraints. However, the performance of an MPC controller highly relies on a proper prioritization weight for each objective, which highlights the need for a precise weight tuning technique. In this paper, we propose an analytical tuning technique by matching the MPC controller performance with the performance of a linear quadratic regulator (LQR) controller. The proposed methodology derives the transformation of a LQR weighting matrix with a fixed weighting factor using a discrete algebraic Riccati equation (DARE) and designs an MPC controller using the idea of a discrete time linear quadratic tracking problem (LQT) in the presence of constraints. The proposed methodology ensures optimal performance between unconstrained MPC and LQR controllers and provides a sub-optimal solution while the constraints are active during transient operations. The resulting MPC behaves as the discrete time LQR by selecting an appropriate weighting matrix in the MPC control problem and ensures the asymptotic stability of the system. In this paper, the effectiveness of the proposed technique is investigated in the application of a novel vehicle collision avoidance system that is designed in the form of linear inequality constraints within MPC. The simulation results confirm the potency of the proposed MPC control technique in performing a safe, feasible and collision-free path while respecting the inputs, states and collision avoidance constraints.

Selecting appropriate weighting matrices for desired linear quadratic regulator (LQR) controller design using evolutionary algorithms is presented in this paper. Obviously, it is not easy to determine the appropriate weighting matrices for an optimal control system and a suitable systematic method is not presented for this goal. In other words, there is no direct relationship between weighting matrices and control system characteristics, and selecting these matrices is done by using trial and error based on designer’s experience. In this paper, we use the particle swarm optimization (PSO) method which is inspired by the social behavior of fish and birds in finding food sources to determine these matrices. Stable convergence characteristics and high calculation speed are the advantages of the proposed method. Simulation results demonstrate that in comparison with genetic algorithms (GAs), the PSO method is very efficient and robust in designing of optimal LQR controller. Key words: Linear quadratic regulator (LQR), weighting matrices, particle swarm optimization, genetic algorithm.

To overcome the shortcomings of precocity and being easily trapped into local optimum of the standard quantum genetic algorithm (QGA) , Information Technology in An Improved Quantum Genetic Algorithm based on dynamic adjustment of the quantum rotation angle of quantum gate (DAAQGA) was proposed. Mutation operation using the quantum not-gate is also introduced to enhance the diversity of population. Chaos search are also introduced into the modified algorithm to improve the search accuracy. Simulation experiments have been carried and the results show that the improved algorithm has excellent performance both in the preventing premature ability and in the search accuracy.